Control devices, methods, and computer program products

Abstract

A problem to be addressed by the present invention relates to a control device, a method, and a computer program product for controlling a driving assistance system for a vehicle. The control device comprises a first measuring device that determines a plurality of first obstacle parameters of a detected obstacle, and is connected to a second measuring device that determines a plurality of second obstacle parameters of the detected obstacle. The control device further comprises an obstacle parameter calculation unit (104) that is configured to receive the plurality of first and second obstacle parameters and calculate a plurality of third obstacle parameters of the detected obstacle on the basis of the plurality of first and second obstacle parameters. The control device further comprises a unit (105) that is configured to calculate a first determination parameter on the basis of the plurality of third obstacle parameters and enable driving assistance when the first determination parameter is smaller than a predetermined activation threshold value.

Description

Technical Field

[0001] The present subject matter relates to a control device, a method, and a computer program product for controlling a driving assistance system for a vehicle.

Background Art

[0002] Current advanced driving assistance systems (ADAS) use on-board sensors mounted on a vehicle to detect obstacles in the area surrounding the vehicle and intervene when there is a risk of collision. To avoid false triggers, an obstacle is observed / detected continuously several times before intervention is enabled. This can, for example, increase the certainty of the calculation results of the position and speed of the detected obstacle. However, repeating the detection requires a specific observation time and, for example, may cause the ADAS to apply strong braking, resulting in discomfort for the driver of the vehicle. Such a situation can occur particularly when an obstacle suddenly appears from a blind spot in front of or behind the vehicle. One possibility to mitigate such a situation is to use V2X (vehicle to everything, vehicle-to-vehicle / vehicle-to-infrastructure) communication to obtain information beyond the detection range of the vehicle's on-board sensors.

[0003] Patent Document 1 describes a system and apparatus for detecting a moving object that enters the field of view of a camera device of a vehicle. This system uses position information obtained from a positioning device of the vehicle itself mounted on the vehicle and position information periodically received from a mobile terminal possessed by the moving object. Based on the position information of the mobile terminal of the moving object and the traveling direction of the vehicle itself, when the moving object enters the field of view of the camera, an area for detecting the moving object in the image captured by the camera is set, and the moving object is detected.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

[0005] However, direct control of ADAS by external devices is difficult because it is essential to avoid erroneous activation of direct control due to delays, transmission instability, and incorrect transmission of V2X information. Therefore, it is important to evaluate V2X information (for example, considering the specifications and scope of the external device) and use only the appropriate parameters of the V2X information to initialize ADAS functions.

[0006] The subject matter described herein addresses the technical objective of improving the driving comfort of vehicles equipped with driver assistance systems while simultaneously enhancing the reliability of driver assistance functions. This objective is achieved by the subject matter of the independent claims. Further preferred developments are described in the dependent claims. [Means for solving the problem]

[0007] According to the subject matter described in the attached claims, control devices, methods, and computer program products for controlling a driver assistance system for a vehicle are proposed. In particular, the subject matter disclosed herein improves the features of a driver assistance system when an obstacle suddenly appears in the area surrounding the vehicle.

[0008] The driver assistance systems controlled by the disclosed subject matter may include, for example, automatic emergency braking (AEB), adaptive cruise control (ACC), or lane change assist (LCA). Other types of driver assistance systems may also be combined with the proposed subject matter.

[0009] The control device comprises a first obstacle parameter acquisition unit configured to receive a plurality of first obstacle parameters of obstacles in an area surrounding a vehicle detected by a first measuring device (which may include a range from centimeters to several meters and up to several kilometers), wherein the plurality of first obstacle parameters include one or more parameters of a first category and one or more parameters of a second category.

[0010] The first measuring device may be a radar (radio-guided ranging) sensor, a camera sensor, a lidar (light-detecting ranging) sensor, a sonar (sonic navigation ranging) sensor, a GNSS (Global Navigation Satellite System) sensor, or any other sensor suitable for detecting obstacles in the area surrounding the vehicle. The first measuring device may be connected to the vehicle, integrated with it, or part of it. The obstacle may be, for example, a pedestrian, a bicycle, another vehicle, or any other object that appears around the vehicle as the vehicle is traveling along the road. Multiple first obstacle parameters may include, for example, the obstacle type (bicycle, pedestrian, etc.), position, direction of travel, speed, yaw rate, and acceleration of the obstacle detected by the first measuring device, or any other type of parameter describing the characteristics of the obstacle.

[0011] The first measuring device (also referred to as a sensor or onboard sensor or onboard device) may be part of a control device or external to it. For example, the control device may receive signals / data from the first measuring device, which may be located at a different location / position on the vehicle, if it is a separate control unit or a control unit integrated with another control unit on the vehicle. Alternatively, the first measuring device may be integrated with a control device as described herein, and in further modifications, the first obstacle parameter acquisition unit and the first measuring device may be integrated with each other as a single unit, thereby enabling the functions of the two units described herein to be performed by the above-described integrated single unit.

[0012] In this context, the first obstacle parameter is understood as a parameter of an object detected by the first measuring device. Multiple first obstacle parameters may be divided into a first group containing one or more parameters of a first category and a second group containing one or more parameters of a second category. The parameter categories may be defined, for example, by features / attributes / characteristics that one parameter may have in common with another parameter.

[0013] Furthermore, the control device includes a second obstacle parameter acquisition unit configured to receive a plurality of second obstacle parameters of obstacles in the area surrounding the vehicle, as detected by the second measuring device. The second measuring device (or sensor or external sensor) is located outside the vehicle where the first measuring device is located. The plurality of second obstacle parameters include one or more parameters of a first category and one or more parameters of a second category. The second obstacle parameter acquisition unit is part of the control device described herein and preferably receives data / signals from the second measuring device and forwards them to the next unit, preferably an obstacle parameter calculation unit, in order to receive them in the control device. However, if the second obstacle parameter acquisition unit is omitted in an alternative configuration of the control device described herein, the second measuring device located outside the vehicle where the first measuring device and / or control device are located, as described herein, may directly transmit the second obstacle parameters to the obstacle parameter calculation unit.

[0014] The second obstacle parameters are understood as parameters of detected obstacles determined / detected / measured by a second measuring device or a second obstacle parameter acquisition unit. Multiple second obstacle parameters may further include, for example, obstacle type, location, direction (direction of travel), velocity, yaw rate and acceleration detected by the first measuring device, or any other type of parameters describing the characteristics of the obstacle, and may be further divided into a first group containing one or more parameters of the first category and a second group containing one or more parameters of the second category. In particular, the second measuring device may determine the same first and second category parameters as the first measuring device.

[0015] The second measuring device may further be a radar sensor, camera sensor, lidar sensor, sonar sensor, GNSS sensor, or any other sensor suitable for detecting obstacles in the area surrounding the vehicle. The second measuring device may be the same type of sensor as the first measuring device or a different type of sensor. In particular, the second measuring device may be located in a different location from the first measuring device, thereby enabling it to detect obstacles at a different time than the first measuring device. Preferably, the second measuring device may be positioned to detect obstacles earlier than the first measuring device. As described above, the second measuring device is provided remotely from the vehicle, i.e., not integrated with the vehicle or located on the vehicle, but outside the vehicle.

[0016] Preferably, one or more parameters in the first category are parameters that can be determined / measured / detected by the first measuring device (or first obstacle parameter acquisition unit) with greater certainty than the second measuring device (or second obstacle parameter acquisition unit). Conversely, preferably, one or more parameters in the second category are parameters that can be determined / measured / detected with greater certainty by the second measuring device.

[0017] For example, if the second measuring device detects an obstacle earlier than the first measuring device, the second measuring device (or the second obstacle parameter acquisition unit) can already receive a large number of obstacle parameters before the first measuring device (or the first obstacle parameter acquisition unit) receives the first obstacle parameters. Due to the longer observation time of the obstacle resulting from the continuous reception of obstacle parameters, parameters that remain constant over the observation time can be determined with greater accuracy by the second measuring device.

[0018] Similarly, if the two measuring devices have different sensor types, the characteristics of the two measuring devices may differ. This, in turn, allows the first measuring device to determine the parameters of the first category with greater accuracy, and the second measuring device to determine the parameters of the second category with greater accuracy.

[0019] Furthermore, the control device includes an obstacle parameter calculation unit that receives a plurality of first and second obstacle parameters from first and second obstacle parameter acquisition units and calculates a plurality of third obstacle parameters, including one or more parameters of a first category and one or more parameters of a second category, based on the plurality of first and second obstacle parameters.

[0020] In other words, the obstacle parameter calculation unit uses a plurality of first and second obstacle parameters determined by the first and second measuring devices (or obstacle parameter acquisition units) to calculate a new set of third obstacle parameters. This new set of third obstacle parameters further includes one or more parameters from a first category and one or more parameters from a second category. When calculating the plurality of third parameters, the obstacle parameter calculation unit calculates one or more parameters from a first category based on the plurality of first obstacle parameters and one or more parameters from a second category based on the plurality of second obstacle parameters.

[0021] In particular, the obstacle parameter calculation unit obtains, from a plurality of first obstacle parameters, parameters of a first category that can be determined with greater certainty by a first measuring device (or first obstacle parameter acquisition unit), and parameters of a second category that can be determined with greater certainty by a second measuring device (or second obstacle parameter acquisition unit), from a plurality of second obstacle parameters, in order to calculate a new set of third obstacle parameters.

[0022] By using the most reliable parameters, the obstacle parameter calculation unit can calculate multiple third obstacle parameters with high accuracy. This also means that the obstacle parameter calculation unit can provide multiple reliable obstacle parameters more quickly compared to calculations based on only one measuring device, which takes a longer time to obtain reliable values ​​for each obstacle parameter.

[0023] As described above, the control device may include first and second obstacle parameter acquisition units that receive first and second obstacle parameters from the first and second measurement devices. These obstacle parameter acquisition units may, for example, perform processing (e.g., smoothing, filtering, averaging) of the first and second obstacle parameters determined by the first and second measurement devices before transmitting them to the obstacle parameter calculation unit. Further, these obstacle parameter acquisition units may also be configured to detect / determine and / or select target parameters of obstacles detected by the first or second measurement device. In other words, the first / second measurement device may further be configured to detect obstacles, and the first / second obstacle parameter acquisition unit may be configured to process detection data from the first / second measurement device to extract / acquire parameters used for further processing within the control device described herein. Additionally or alternatively, the first and second obstacle parameter acquisition units may further (simply) serve as reception and parameter transfer units within the control device.

[0024] Furthermore, the control device includes an enable unit (which may also be referred to as a driving assistance activation determination unit or a driving assistance activation unit, etc.) that calculates a first determination parameter based on a plurality of third obstacle parameters received from the obstacle parameter calculation unit and enables driving assistance when the determination parameter is smaller than a predetermined activation threshold. In other words, the obstacle parameter calculation unit transmits the plurality of third obstacle parameters to the enable unit, and the enable unit derives comparison data (determination parameter) from the third obstacle parameters to determine whether to activate driving assistance. Driving assistance may be, for example, automatic braking, acceleration, or steering. Alternatively or additionally, driving assistance may also be an auditory or visual signal that prompts the driver to perform a specific action, such as braking or decelerating.

[0025] The enable unit then compares the determination parameter with a predetermined activation threshold value and enables driving assistance when the determination parameter is below the threshold value. For example, the enable unit may calculate, as the determination parameter based on the third obstacle parameter, the time until the vehicle reaches the obstacle (time-to-collision) or the difference between the vehicle and the obstacle. The predetermined activation threshold value in this case may be a predetermined time or a predetermined distance.

[0026] When the enable unit derives the first determination parameter from a plurality of third obstacle parameters based on the most reliable obstacle parameters received from the first and second measurement units, the enable unit can determine the first determination parameter early with high accuracy. As a result, the control device can activate driving assistance before the obstacle appears next to the vehicle, thus avoiding sudden driving operations and enhancing driving comfort.

[0027] In one example, the control device may further include an activation unit that can activate driving assistance based on an enable signal received from the enable unit. In other words, when the determination parameter is below the predetermined threshold value, the enable unit can transmit the enable signal to the activation unit, and the activation unit then activates actuators / control elements for driving assistance in the vehicle, such as hydraulic valves for braking or steering operations and / or signal outputs for providing auditory or visual information. However, it is also possible for the actuator to be directly activated by the enable unit.

[0028] According to one example, the obstacle parameter calculation unit may determine whether the first and second measurement devices have detected the same (identical) obstacle based on at least one comparison result among a plurality of first and second obstacle parameters, and may calculate a plurality of third obstacle parameters only when the determination result is positive, that is, only when the obstacles detected by the first and second measurement devices are the same.

[0029] For example, the obstacle parameter calculation unit may first determine whether multiple first and second obstacle parameters include the same obstacle type, for example, whether both measuring devices detected a bicycle. If they include the same obstacle type, the obstacle parameter calculation unit may calculate the distance between the obstacle location included in the first obstacle parameter and the obstacle location included in the second obstacle parameter. If the calculated distance is less than a predetermined distance threshold, the obstacle parameter calculation unit may recognize that the obstacles detected by both measuring devices are the same object. In the case of a positive result, i.e., they are the same, the obstacle parameter calculation unit may use the multiple first and second obstacle parameters to calculate multiple third obstacle parameters as described above. In the case of a negative result, i.e., they are not the same, the obstacle parameter calculation unit may receive additional multiple first and / or second obstacle parameters from the first and / or second measuring devices and repeat this process until a positive result is obtained.

[0030] For example, the first measuring device may be able to communicate with the obstacle parameter calculation unit (or the first obstacle parameter acquisition unit) at a faster speed than the second measuring device, but be able to detect obstacles more slowly than the second measuring device. Conversely, the second measuring device may be able to detect obstacles earlier than the first measuring device, but be able to communicate with the obstacle parameter calculation unit (or the second obstacle parameter acquisition unit) at a slower speed / over a longer communication path than the first measuring device.

[0031] Preferably, as described above, the first measuring device may be an onboard measuring device located inside the vehicle, while the second measuring device is an external measuring device located outside the vehicle. Communication between the onboard measuring device and the control device (or first obstacle parameter acquisition unit) described herein may be performed in real time or with low latency, while communication between the external measuring device and the control device described herein may be performed, for example, via a cellular network with a longer latency period.

[0032] The onboard measuring device (preferably the first measuring device) may be a radar sensor, camera sensor, LiDAR sensor, sonar sensor, GNSS sensor, or any other sensor as an onboard sensor for the vehicle. In particular, a combination of radar sensors, camera sensors, LiDAR sensors, sonar sensors, and GNSS sensors may be mounted on the vehicle. However, each of these sensors can only detect an obstacle if it appears in its field of view, i.e., if the obstacle is not obstructed by another obstacle in the vicinity of the vehicle. Therefore, a second external device that can detect obstacles early can provide a second obstacle parameter that is effective for driver assistance that can be operated / activated at an early stage rather than suddenly.

[0033] The external measuring device (preferably a second measuring device) may be, for example, a roadside unit capable of detecting obstacles via radar sensors and / or camera sensors. In addition, the roadside unit may be configured to exchange information with obstacles equipped with its own onboard measuring devices, such as other vehicles and pedestrians / cyclists carrying mobile devices. The latter, i.e., other vehicles and mobile devices (smartphones, tablets, laptops) with onboard measuring devices, can also be suitable external measuring devices as the second measuring device. In particular, another vehicle in the vicinity of the vehicle may be an obstacle that provides information about its own state, such as its current position, speed, and direction of travel, and / or only a measuring device that provides information about another obstacle in the vicinity of the vehicle detected by its own onboard measuring device.

[0034] In one example, the second measuring device may be an onboard measuring device of another vehicle that is approaching as a potential obstacle around the vehicle. In this case, the obstacle parameter calculation unit may also receive the width and height of the other vehicle as further second obstacle parameters, and may take these further obstacle parameters into consideration when calculating a number of third obstacle parameters. In particular, the obstacle parameter calculation unit can use the width and height of the vehicle to determine its spatial position coordinates. Knowing the spatial position coordinates of the obstacle allows the enable unit to then determine the collision distance and / or collision margin to the obstacle with greater accuracy.

[0035] Furthermore, as mentioned above, it is also possible that both the first and second measuring devices are external devices located outside the vehicle. In this case, the measuring device located closer to the vehicle can function as the first measuring device, and the measuring device located further away from the vehicle can function as the second measuring device. Therefore, the measuring device closer to the vehicle has a shorter latency period than the measuring device located further away. On the other hand, the measuring device located further away from the vehicle can detect obstacles earlier than the measuring device located closer.

[0036] For example, a roadside unit immediately to the right of a vehicle may act as a first measuring device, and if a pedestrian with a smartphone appears as an obstacle around the vehicle, the pedestrian's smartphone may act as a second measuring device. The control device receives signals from each external measuring device and may determine, for example, which measuring device should act as the first and second measuring device depending on the signal strength. The obstacle parameter calculation unit then receives the first and second obstacle parameters from both external devices (preferably via the first / second obstacle parameter acquisition unit) and may calculate a third obstacle parameter based on the parameter with the highest certainty.

[0037] In this example, because the latency between the roadside unit and the control device is small, which is important for accurately detecting the pedestrian's current position, the obstacle parameter calculation unit can receive, for example, the pedestrian's position from the roadside unit as a first obstacle parameter. On the other hand, since it can be assumed that the pedestrian's speed is approximately constant over the observed time slot, the obstacle parameter calculation unit can receive, for example, the pedestrian's speed from the pedestrian's smartphone as a second obstacle parameter. The accuracy and reliability of the determination are increased when the smartphone determines the pedestrian's speed over a much longer period than the roadside unit.

[0038] For example, the parameters of the first category may be position parameters of the obstacle, including static information about the obstacle, and the parameters of the second category may be movement parameters of the obstacle, including dynamic information about the obstacle. Static information about the obstacle may include, for example, the type of obstacle (pedestrian, bicycle, vehicle, etc.), the current time (timestamp) when the obstacle was detected, and its current position and direction of movement. In particular, static information is characterized by facts that are not time-dependent on the time of acquisition. Dynamic information about the obstacle may include, for example, its velocity, yaw rate, and acceleration. In particular, dynamic information is characterized by facts that are time-dependent on the time of acquisition.

[0039] Since positional parameters such as the location and direction of movement of an obstacle change each time they are determined by the first / second measuring device, it is important that they be transmitted immediately to the obstacle parameter calculation unit so that the obstacle parameter calculation unit can obtain the current values ​​of the positional parameters. The first measuring device can provide multiple first obstacle parameters within a short latency period, so that the obstacle parameter calculation unit can calculate multiple third positional parameters using the positional parameters received from the first measuring device.

[0040] However, it can be assumed that the motion parameters of the obstacle, such as velocity, acceleration, and yaw rate, remain constant within the observed time slot. Therefore, the timing of transmitting motion parameters to the obstacle parameter calculation unit may be less important than the transmission of position parameters. On the other hand, the certainty of the obstacle parameters increases with each determination; that is, the earlier certain obstacle parameters can be determined, the higher their accuracy and certainty. Since the second measuring device can detect obstacles earlier than the first measuring device, the obstacle parameter calculation unit can use the motion parameters received from the second measuring device to calculate several third position parameters.

[0041] In this way, the obstacle parameter calculation unit can be ensured to calculate multiple third obstacle parameters, and based on this, driving assistance can be enabled using the first and second obstacle parameters that have the highest accuracy and reliability.

[0042] For example, an obstacle parameter calculation unit may include a predictive model for calculating a plurality of third obstacle parameters, and the predictive model may calculate a plurality of third obstacle parameters when an obstacle is first detected by using one or more parameters of a second category from a plurality of second obstacle parameters as one or more initial parameters. In other words, the predictive model may use the movement parameters of the plurality of second obstacle parameters to initialize the predictive model. Thus, the predictive model can start calculations with already reliable values ​​of, for example, velocity, acceleration, and yaw rate, improving the accuracy of the prediction. In particular, the predictive model may include a Kalman filter for calculating a plurality of third obstacle parameters based on a plurality of first and second obstacle parameters determined by first and second measuring devices.

[0043] For example, an obstacle parameter calculation unit may calculate a confidence index representing the reliability of several third obstacle parameters and transmit the calculated confidence index, along with the third obstacle parameters, to an enable unit. The confidence index may be, for example, a counter that can be incremented each time an event occurs that increases the certainty of the third obstacle parameter, and decremented each time an event occurs that decreases the certainty of the third obstacle parameter. In this case, the enable unit may enable the activation of the driver assistance if the first determination value is lower than a predetermined activation threshold, and the value of the confidence index is higher than the first predetermined confidence threshold. This ensures that the driver assistance is performed only when the several third obstacle parameters that can serve as the basis for calculating the determination value for enabling the driver assistance achieve sufficient certainty.

[0044] For example, the enable unit may further receive a plurality of first obstacle parameters and calculate a second determination parameter based on the plurality of first obstacle parameters. In this case, the enable unit may activate driving assistance if the first and / or second determination parameters are lower than a predetermined activation threshold.

[0045] In other words, the enable unit can calculate two decision parameters, the first of which is derived from a plurality of third obstacle parameters calculated by the obstacle parameter calculation unit based on a plurality of first and second obstacle parameters as described above, and the second of which is derived only from a plurality of first obstacle parameters. By enabling driver assistance when at least one of the two decision parameters is below a predetermined threshold, the control unit ensures that driver assistance can be enabled with high certainty, even when it has access only to first measuring devices such as onboard sensors of the vehicle.

[0046] For example, an obstacle parameter calculation unit may increase the value of a confidence index based on the number of times an obstacle is detected by a first measuring device. Due to the high-speed communication path of the first measuring device, which assumes stable signal transmission, the certainty of several first obstacle parameters can be considered to depend primarily on the observation time, i.e., the number of times the obstacle is detected by the first measuring device, which can be significantly shorter than the observation time of a second measuring device that detects the obstacle early.

[0047] For example, an obstacle parameter calculation unit may calculate a confidence index that takes into account the specifications of several second obstacle parameters determined by a second measuring device. Since the second measuring device may be significantly more remote from the vehicle than the first measuring device, the influence of the obstacle parameter detection and transmission method may be significantly more important than that of the first measuring device. For example, if the second measuring device transmits GNSS-based messages, their accuracy may depend on the environment of the second measuring device, since GNSS cannot provide signals, for example, in tunnels.

[0048] The specifications for multiple second obstacle parameters may include information regarding the characteristics / characteristics / quality of the second obstacle parameters, including the characteristics / characteristics / quality of the second measuring device. This specification may include, for example, information regarding the sensor type of the second measuring device, message type, signal resolution, and timestamp of the second measuring parameter, as well as any other information that delivers information regarding the characteristics / characteristics / quality of the second obstacle parameters.

[0049] In one example, the control device may include a specification acquisition unit for acquiring the specifications of a plurality of second obstacle parameters before they are transmitted to the obstacle parameter calculation unit. In this case, the specification acquisition unit may process the signals received from the second measuring device / second obstacle parameter acquisition unit to prepare them for calculations performed by the obstacle parameter calculation unit. However, it is also possible for the obstacle parameter calculation unit to receive the specifications of the second obstacle parameters directly.

[0050] For example, the specifications of multiple second obstacle parameters may include multiple specification parameters, and the obstacle parameter calculation unit may adjust the value of the confidence index based on the value of each specification parameter. In particular, the multiple specification parameters may include multiple pieces of information about the boundary conditions under which the multiple second obstacle parameters are determined. Based on this information, the obstacle parameter calculation unit may increment or decrement the value of the confidence index.

[0051] In one example, an obstacle parameter calculation unit and / or a specification acquisition unit may receive a timestamp as a specification parameter from a second measuring device (or a second obstacle parameter acquisition unit), providing the most recent time the second measuring device determined the second obstacle parameter. The obstacle parameter calculation unit may then calculate the delay time of the received second obstacle parameter and decrement the confidence index value according to the length of the delay time. In particular, a long delay time may result in a larger decrease in the confidence index value than a short delay time. If the delay time is shorter than a predetermined threshold, the confidence level may remain constant. The predetermined threshold for delay time may, for example, correspond to the delay time of the first measuring device.

[0052] In another example, the obstacle parameter calculation unit and / or specification acquisition unit may acquire the number of times a second measuring device (second obstacle parameter acquisition unit) has determined a second obstacle parameter (observation length) as a specification parameter, and the obstacle parameter calculation unit may decrease / decrement the confidence index value depending on the observation length. In particular, shorter observation lengths may result in a greater decrease in the confidence index value than longer observation times. If the observation length exceeds a predetermined threshold, the confidence level may remain constant.

[0053] In yet another example, an obstacle parameter calculation unit and / or specification acquisition unit may receive the variance of a determined second obstacle parameter, such as the variance of the determined velocity signal of the obstacle, as a specification parameter. In this case, the obstacle parameter calculation unit may decrement the confidence index value according to the variance of the determined parameter, and if the variance is small, the result may be a smaller decrease in the confidence index value than if the variance is large. However, if the variance of the parameter is below a predetermined threshold, the confidence level may remain constant.

[0054] In yet another example, an obstacle parameter calculation unit and / or specification acquisition unit may receive a message type for a second obstacle parameter as a specification parameter. Possible message categories may include, for example, collaborative recognition messages providing information about themselves from another vehicle, messages from roadside units, collective recognition messages providing information about other objects from another vehicle, messages provided by mobile devices, and other messages that do not fall into any of the above categories. In this case, the obstacle parameter calculation unit may increase the value of the confidence index according to the order of the above message categories, with collaborative recognition messages providing the highest confidence increase, while messages that do not fall into the above categories provide the lowest confidence increase.

[0055] In yet another example, the obstacle parameter calculation unit and / or specification acquisition unit may acquire the stability of communication with a second measuring device as a specification parameter and adjust the value of a reliability index based on that communication stability. In this case, the obstacle parameter calculation unit may, for example, determine the signal strength of wireless communication around the vehicle and derive the communication stability with the second measuring unit from that signal strength. In particular, high signal strength may indicate stable communication, while low signal strength may indicate unstable communication.

[0056] In yet another example where another vehicle acting as an obstacle serves as a second measuring device, the obstacle parameter calculation unit and / or specification acquisition unit may receive the activation status of the other vehicle's driver assistance system as a specification parameter and increase / increment the confidence index value if the driver assistance system is enabled.

[0057] Each of the specification parameters described above can contribute to the adjustment of the confidence index; that is, the value of the confidence index may be the result of a combination of adjustments from multiple specification parameters. In this context, each or at least some of the specification parameters are weighted in light of their importance for the reliable calculation of the third obstacle parameters. In particular, specification parameters that have high importance for the accurate calculation of multiple third obstacle parameters may be weighted with high coefficients, while specification parameters that have low importance for the accurate calculation of multiple third obstacle parameters may be weighted with low coefficients.

[0058] In another example, an obstacle parameter calculation unit or specification acquisition unit may receive multiple map information of the area surrounding the vehicle and adjust the value of the confidence index based on the multiple map information. This map information may be stored in a memory unit of the control device and may include, for example, information on the location of buildings and traffic congestion, and based on this, the obstacle parameter calculation unit may draw conclusions regarding the quality of a second obstacle parameter determined by a second measuring device. In particular, the message type assessment may be modified based on the map information. For example, the obstacle parameter calculation unit may reduce the increase in the value of the confidence index based on a message from a roadside unit when traffic congestion occurs immediately next to the roadside unit, where the obstacle may be obstructed by other vehicles.

[0059] When the obstacle parameter calculation unit and / or specification acquisition unit receives map information as described above, it may, additionally or alternatively, determine the stability of communication with the second measurement device from the information provided in the map information. For example, if a vehicle is traveling through an area with tall buildings, the buildings may interfere with communication with the second measurement device, and therefore the stability of communication may be low. The same applies if the vehicle is traveling through a busy area where data traffic may be very high. These environmental conditions, which can be derived from the map information, can be used to determine the stability of communication between the obstacle parameter calculation unit and / or specification acquisition unit (or generally the control device) and the second measurement device, and the obstacle parameter calculation unit may increase or decrease the value of the confidence index based on each condition.

[0060] For example, a second obstacle parameter acquisition unit may receive multiple second obstacle parameters from more than one second measurement device. In this case, based on at least one of multiple specification parameters and at least one of multiple map information, the obstacle parameter calculation unit may select multiple second obstacle parameters received from more than one second measurement device. For example, the obstacle parameter calculation unit may determine the order of the second measurement devices depending on the message type of those second obstacle parameters. If the message received from the second measurement device is a cooperative recognition message that provides information that another vehicle is an obstacle, the other vehicle may be selected as a suitable second measurement device because cooperative recognition messages are messages of high certainty. However, the environment of the other vehicle may also be considered when selecting multiple second obstacle parameters received from more than one second measurement device. If the other vehicle selected as a suitable second measurement device is traveling through a congested area, the transmission of the communication path to the obstacle parameter calculation unit may be disrupted. Therefore, the obstacle parameter calculation unit may also consider map information when determining the order of the second measurement devices.

[0061] After selecting a plurality of second obstacle parameters received from more than one second measuring device, the obstacle parameter calculation unit may determine whether an obstacle detected by one second measuring device is identical to an obstacle detected by the other second measuring device, based on at least one of the plurality of second obstacle parameters from one second measuring device and the other second measuring devices.

[0062] For example, the obstacle parameter calculation unit may first determine whether multiple second obstacle parameters from one second measuring device and the other second measuring device include the same obstacle type, for example, whether both second measuring devices have detected a bicycle. If they include the same obstacle type, the obstacle parameter calculation unit may calculate the distance between the obstacle location included in the second obstacle parameters of one second measuring device and the obstacle location included in the second obstacle parameters of the other second obstacle device. If the calculated distance is less than a predetermined distance threshold, the obstacle parameter calculation unit may recognize that the obstacle detected by both second measuring devices is the same object. In this case, the second obstacle parameter acquisition unit may receive multiple second obstacle parameters from at least one of the second measuring devices.

[0063] However, if the determination is negative, the second obstacle parameter acquisition unit may receive multiple second obstacle parameters from one of the second measuring devices that has detected an obstacle identical to the obstacle detected by the first measuring device.

[0064] For example, the obstacle parameter calculation unit may increase the confidence index value if the obstacles detected by one and the other second measuring device are the same, and may decrease the confidence index value if the obstacles detected by one and the other second measuring device are different. If both second measuring devices detect the same obstacle, the certainty of the second obstacle parameter is high because they have been judged twice. However, if one and the other second measuring devices detect different obstacles, the certainty of the second obstacle parameter is low because it is not clear which of the two second measuring devices detected the obstacle.

[0065] For example, an obstacle parameter calculation unit may receive the fields of view of one and the other second measuring device, and may reduce the confidence index value if the field of view of one second measuring device overlaps with the field of view of the other second measuring device. In this case, the overlapping fields of view may lead to inconsistent results regarding obstacles detected by one and the other second measuring device. Therefore, the confidence index value is reduced when the fields of view of both second measuring devices overlap.

[0066] For example, the enable unit may include a warning enable unit that can calculate a warning determination parameter based on a plurality of third obstacle parameters and enable a warning as driver assistance if the calculated warning determination parameter is smaller than a predetermined warning activation threshold. Furthermore, this enable unit may include an intervention enable unit that can calculate an intervention determination parameter based on a plurality of third obstacle parameters and enable an intervention as driver assistance if the intervention determination parameter is smaller than a predetermined intervention activation threshold. In particular, the predetermined warning activation threshold may be larger than the predetermined intervention activation threshold. For example, if the warning and / or intervention determination parameter is collision margin time, the warning activation threshold may include a collision margin time value larger than the intervention activation threshold. Thereafter, the warning may be activated earlier than the intervention.

[0067] If the control device may further include an activation unit, the activation unit may further include a warning activation unit that can activate a warning based on a determination of a warning enable unit, and an intervention activation unit configured to activate an intervention based on a determination of an intervention enable unit.

[0068] For example, if the confidence index is lower than a second predetermined confidence threshold, the obstacle parameter calculation unit may calculate a first plurality of third obstacle parameters and a second plurality of third obstacle parameters. The second predetermined confidence threshold may be greater than or equal to the first predetermined confidence threshold.

[0069] In this case, the first plurality of third obstacle parameters may be calculated based on a plurality of first obstacle parameters and a plurality of second obstacle parameters, and the second plurality of third obstacle parameters may be calculated based solely on the plurality of first obstacle parameters. Furthermore, the warning enable unit may calculate a warning determination parameter based on the first plurality of third obstacle parameters, and the intervention enable unit may calculate an intervention determination parameter based on the second plurality of third obstacle parameters. In other words, even if the confidence index is below a second predetermined threshold, a warning may be enabled based on a combination of position parameters obtained from a plurality of first obstacle parameters and movement parameters obtained from a plurality of second obstacle parameters. However, in this case, intervention in the driver's driving behavior may be performed based solely on first obstacle parameters which may be determined by the vehicle's onboard measuring device. This ensures that the entire control of the driver assistance system remains in use of the vehicle when external measuring devices may not be 100% reliable.

[0070] However, if the confidence index is higher than a second predetermined confidence threshold, meaning that the second obstacle parameter determined by the external measuring device also has high certainty, the obstacle parameter calculation unit may calculate only the first plurality of third obstacle parameters, and the warning enable unit and the intervention enable unit may calculate the warning decision parameter and the intervention decision parameter, respectively, based on the first plurality of third obstacle parameters.

[0071] The disclosed subject matter may further include a control system comprising the control devices and first and / or second measuring devices as described above. The disclosed subject matter also further includes a vehicle comprising the control devices and at least a first measuring device as described above.

[0072] The disclosed subject matter further includes a method for controlling a driver assistance system for a vehicle, wherein a plurality of first obstacle parameters of a detected obstacle include one or more parameters of a first category and one or more parameters of a second category, and a plurality of second obstacle parameters of a detected obstacle include one or more parameters of a first category and one or more parameters of a second category.

[0073] Next, a plurality of first and second obstacle parameters are received by the obstacle parameter calculation unit, and a plurality of third obstacle parameters for the detected obstacles are calculated by the obstacle parameter calculation unit, the plurality of third obstacle parameters including one or more parameters of a first category and one or more parameters of a second category based on the plurality of first and second obstacle parameters, one or more parameters of the first category calculated based on the plurality of first obstacle parameters, and one or more parameters of the second category calculated based on the plurality of second obstacle parameters.

[0074] Next, the enable unit calculates a determination parameter based on multiple third obstacle parameters received from the obstacle parameter calculation unit. If the determination parameter is lower than a predetermined threshold, the enable unit enables the driver assistance.

[0075] Furthermore, each component of the disclosed control device or the control system described above is also included in a manner that can be described in the claims and / or by the claims of the computer program product.

[0076] In the following, the disclosed subject matter will be further described based on several examples with reference to the attached drawings. The same elements are given the same reference numerals, and redundant descriptions of the same elements will be avoided. Here again, the figures show embodiments that may be modified according to the embodiments described above and their further modifications, and / or modifications described in connection with the detailed description of the drawings. In particular, this also applies to providing the first / second measuring device and / or the first / second obstacle parameter acquisition device, as well as their respective adaptations for data transmission / reception inputs and outputs, either separately or as an integrated unit. In other words, when the measuring device and the obstacle parameter acquisition unit are provided as separate units, it is a preferred optional choice to transmit information / data about detected obstacles to the respective obstacle parameter acquisition units. The above data may already include parameters necessary for further processing, or it may be the parameters themselves, in which case the obstacle parameter acquisition unit mainly functions as an input unit for a control device, passing the above data (or modified data) to the next unit, such as an obstacle parameter calculation unit. The above data may also include raw detection data / information about the detected object, in which case the obstacle parameter acquisition unit is configured to extract, select and / or determine the target parameters and their respective data, and transmit them to the next unit, such as the obstacle parameter calculation unit. Naturally, it is also possible to combine any optional components. Otherwise, if the measuring devices and each obstacle parameter acquisition device are integrated into a combined unit or a single unit, they can also perform the functions described above. Preferably, in the case of a combination, only the first measuring device and the first obstacle parameter acquisition unit are combined, while the second measuring device (located outside the vehicle in a preferred embodiment) is provided separately from the second obstacle parameter acquisition unit. As can be seen from the following description of the drawings and the drawings themselves, mainly four units provided separately are shown below, but this does not limit the present disclosure and further optional components and modifications, such as those described above. [Brief explanation of the drawing]

[0077] [Figure 1] This figure schematically illustrates a control device as an example of the subject matter to be disclosed. [Figure 2] Figure 1 is a flowchart illustrating an example of the initialization procedure for the control device shown. [Figure 3a] Figure 1 schematically shows several first, second, and third obstacle parameters that can be determined by the control device shown. [Figure 3b] Figure 1 schematically shows several first, second, and third obstacle parameters that can be determined by the control device shown. [Figure 4a] This figure schematically illustrates an example of tracking obstacles using control devices other than those shown in Figure 1. [Figure 4b] This figure schematically illustrates an example of tracking obstacles using the control device shown in Figure 1. [Figure 5] This flowchart illustrates an example of how the control device shown in Figure 1 recognizes that the first measurement unit has detected the same obstacle as the second measurement unit. [Figure 6] Figure 1 is a flowchart illustrating an example of enabling driver assistance using the control device shown. [Figure 7a] This figure schematically illustrates an example of enabling driver assistance using control devices other than those shown in Figure 1. [Figure 7b] Figure 1 is a schematic diagram illustrating an example of enabling driver assistance using the control device shown. [Figure 8] This figure schematically illustrates a control device as another example of the disclosed subject matter. [Figure 9a] This flowchart illustrates an example in which each device receives specifications for multiple second obstacle parameters, and the control device shown in Figure 8 adjusts the reliability index of the second obstacle parameters based on the received specifications. [Figure 9b]This flowchart illustrates an example in which each device receives specifications for multiple second obstacle parameters, and the control device shown in Figure 8 adjusts the reliability index of the second obstacle parameters based on the received specifications. [Figure 10a] This flowchart illustrates an example of how each control device, as shown in Figure 8, adjusts the reliability index based on the specification parameters. [Figure 10b] This flowchart illustrates an example of how each control device, as shown in Figure 8, adjusts the reliability index based on the specification parameters. [Figure 11a] This flowchart illustrates an example of how each control device, as shown in Figure 8, adjusts the reliability index based on different specification parameters. [Figure 11b] This flowchart illustrates an example of how each control device, as shown in Figure 8, adjusts the reliability index based on different specification parameters. [Figure 12] Figure 8 is a flowchart illustrating an example of adjusting the reliability index based on further specification parameters using the control device shown. [Figure 13] Figure 8 is a flowchart illustrating an example of adjusting the reliability index based on map information using the control device shown. [Figure 14] Figure 8 is a flowchart illustrating an example of adjusting the reliability index based on further specification parameters using the control device shown. [Figure 15a] Each of these flowcharts illustrates an example of how a control device, as shown in Figure 8, processes multiple second obstacle parameters received from one or more second measuring devices. [Figure 15b] Each of these flowcharts illustrates an example of how a control device, as shown in Figure 8, processes multiple second obstacle parameters received from one or more second measuring devices. [Figure 16]Figure 8 is a flowchart illustrating an example of how a control device prioritizes multiple second obstacle parameters received from more than one second measuring device. [Figure 17a] This flowchart illustrates an example of how each control device, as shown in Figure 8, recognizes multiple second obstacle parameters received from one or more second measuring devices. [Figure 17b] This flowchart illustrates an example of how each control device, as shown in Figure 8, recognizes multiple second obstacle parameters received from one or more second measuring devices. [Figure 17c] This flowchart illustrates an example of how each control device, as shown in Figure 8, recognizes multiple second obstacle parameters received from one or more second measuring devices. [Figure 18a] This diagram schematically illustrates an example of driver assistance when an obstacle is detected by a second measuring device, using the control device shown in Figure 8. [Figure 18b] This diagram schematically illustrates an example of driver assistance when an obstacle is detected by a second measuring device, using the control device shown in Figure 8. [Figure 19a] Figure 8 schematically illustrates an example of driver assistance when more than one obstacle is detected by a second measuring device, using the control device shown in Figure 8. [Figure 19b] Figure 8 schematically illustrates an example of driver assistance when more than one obstacle is detected by a second measuring device, using the control device shown in Figure 8. [Figure 20a] Each of these flowcharts illustrates an example of adjusting the reliability index based on different fields of view of one or more second measuring devices using the control devices shown in Figure 8. [Figure 20b] Each of these flowcharts illustrates an example of adjusting the reliability index based on different fields of view of one or more second measuring devices using the control devices shown in Figure 8. [Figure 20c]Each of these flowcharts illustrates an example of adjusting the reliability index based on different fields of view of one or more second measuring devices using the control devices shown in Figure 8. [Figure 21] This is a schematic diagram illustrating a control device as yet another example of the subject matter disclosed. [Figure 22] This flowchart illustrates an example of how a control device, shown in Figure 21, receives specifications for multiple second obstacle parameters and adjusts the reliability index of the second obstacle parameters based on the received specifications. [Figure 23a] Figure 21 is a flowchart illustrating an example of the initialization process for the control device shown. [Figure 23b] Figure 21 is a flowchart illustrating an example of the initialization process for the control device shown. [Figure 24] Figure 21 is a flowchart illustrating an example of enabling driver assistance using the control device shown. [Figure 25a] This figure schematically shows an example of driver assistance performed using control devices other than those shown in Figure 21, in comparison with driver assistance performed using the control devices shown in Figure 21. [Figure 25b] This figure schematically shows an example of driver assistance performed using control devices other than those shown in Figure 21, in comparison with driver assistance performed using the control devices shown in Figure 21. [Figure 26] Figures 25a and 25b show a schematic representation of the results of the driver assistance examples illustrated in the diagrams. [Figure 27] This is a schematic diagram illustrating a control device as yet another example of the subject matter disclosed. [Figure 28] Figure 27 is a schematic diagram illustrating an example of driver assistance when an obstacle is detected using the control device shown. [Figure 29a] Figure 27 schematically illustrates another example of driver assistance when an obstacle is detected using the control device shown. [Figure 29b]Figure 27 schematically illustrates another example of driver assistance when an obstacle is detected using the control device shown. [Figure 30] This is a schematic diagram illustrating a control device as yet another example of the subject matter disclosed. [Figure 31] Figure 30 is a flowchart illustrating an example of a control process performed by the control device shown. [Figure 32] This figure schematically shows an example of driver assistance performed using control devices other than those shown in Figure 30, in comparison with an example of driver assistance performed using the control devices shown in Figure 30. [Modes for carrying out the invention]

[0078] Figure 1 is a schematic diagram showing a control device 1 according to an example of the disclosed subject matter. The control device 1 is mounted on a vehicle V, which is equipped with an onboard sensor (first measuring device) 100 for detecting obstacles around the vehicle V, and the first measuring device / onboard sensor 100 may include, for example, a radar sensor, a camera sensor, a lidar sensor, a sonar sensor, a GNSS sensor and / or any other sensor suitable for detecting obstacles around the vehicle V. In addition, the control device 1 is communicably connected to an external sensor (second measuring device) 102 which can be connected to the control device 1 via vehicle-to-location (V2X) communication. The external sensor 102 may also be a radar sensor, a camera sensor, a lidar sensor, a sonar sensor, a GNSS sensor and / or any other sensor suitable for detecting obstacles around the vehicle V, and the external sensor 102 may be included, for example, in another vehicle, a roadside unit and / or a mobile device. Communication between the onboard sensor 100 and the control device 1 can be performed in real time, while communication between the external sensor 102 and the control device 1 can be performed, for example, over a cellular network with a longer latency period.

[0079] Both the onboard sensor 100 and the external sensor 102 can detect obstacles in the area surrounding the vehicle V and determine a number of first and second obstacle parameters, including, for example, the type of obstacle, location, direction of travel, speed, yaw rate, and acceleration of the detected obstacle.

[0080] Multiple first and second obstacle parameters can be divided into a first group containing one or more parameters of a first category and a second group containing one or more parameters of a second category. Parameter categories can be defined, for example, by features / attributes / characteristics that one parameter may have in common with another. In particular, parameters of the first category may be obstacle position parameters containing static information about the obstacle, and parameters of the second category may be obstacle movement parameters containing dynamic information about the obstacle.

[0081] Static information about an obstacle may include, for example, the type of obstacle (pedestrian, bicycle, vehicle, etc.), the current time (timestamp) when the obstacle was detected, and its current position and direction of movement. In particular, static information is characterized by facts that are independent of the time of acquisition. Conversely, dynamic information about an obstacle may include, for example, its velocity, yaw rate, and acceleration. In particular, dynamic information is characterized by facts that are independent of the time of acquisition.

[0082] Furthermore, the control device 1 in the illustrated example includes first and second obstacle (parameter) acquisition units 101 and 103 that can receive a plurality of first and second obstacle parameters from the onboard sensor 100 and the external sensor 102. The obstacle parameter acquisition units 101 and 103 may process the first and second obstacle parameters (e.g., smoothing, filtering, averaging) before transmitting them to the obstacle parameter calculation unit 104 of the control device 1, or they may determine or select parameters, especially if the first / second measuring devices are primarily configured to detect objects. Furthermore, the obstacle parameter calculation unit 104 may also directly receive the first and second obstacle parameters from the onboard sensor 100 and the external sensor 102.

[0083] Figure 1 shows a configuration in which the control device 1 is part of the vehicle V and the external sensor (second measuring device) 102 is located remotely from the vehicle V. However, in alternative modifications, both measuring devices 100 and 102 may be located externally / remotely from the vehicle V. Additionally or alternatively, the example in Figure 1 (or further control devices 1a-1d) may also be modified so that at least one of the sensors (first / second measuring devices) can be combined with the respective obstacle parameter acquisition units 101, 103. For example, in another modification, the first measuring device 100 and the first obstacle parameter acquisition unit 101 may be the same or an integrated unit (rather than separate units), and more preferably, therefore both may be part of the control device 1. In this same modification, the second measuring device 102 may be located remotely from the vehicle V, thereby allowing the second obstacle parameter acquisition unit 103 to be located as shown in Figure 1.

[0084] The obstacle parameter calculation unit 104 then calculates a plurality of third obstacle parameters based on a plurality of first and second obstacle parameters, which may include one or more position parameters and one or more movement parameters. In other words, the obstacle parameter calculation unit 104 calculates a new set of third obstacle parameters using the plurality of first and second obstacle parameters determined by the onboard sensor 100 and the external sensor 102 (or obstacle parameter acquisition unit).

[0085] Positional parameters such as the position and direction of movement of an obstacle change their values ​​each time they are determined by the first / second measuring devices (sensors) 100, 102, and therefore are preferably transmitted immediately to the obstacle parameter calculation unit 104 so that the obstacle parameter calculation unit 104 can obtain the current values ​​of the positional parameters. However, it can be assumed that motion parameters such as the velocity, acceleration, and yaw rate of the obstacle remain constant within the observed time slot. Therefore, the timing of transmitting motion parameters to the obstacle parameter calculation unit 104 may be less important than the transmission of positional parameters. On the other hand, the accuracy and certainty of determining obstacle parameters increase with each determination (step), that is, the earlier constant obstacle parameters can be determined, the higher the accuracy and certainty.

[0086] As described above, the parameters of obstacles can also be determined by the obstacle parameter acquisition unit from the detected object data received from sensors 100 and 102. For simplicity, the following describes an example in which sensors 100 and 102 determine the parameters of detected obstacles and transmit them, preferably via first / second obstacle parameter units 101 and 103, to subsequent units such as the obstacle parameter calculation unit 104 of the control device 1, even if not explicitly stated. This also has further variations of the control device as shown in Figures 8, 21, and so on.

[0087] Therefore, it is preferable for the obstacle parameter calculation unit 104 to read position parameters that can be determined with greater certainty by the onboard sensor 100 from a plurality of first obstacle parameters, and movement parameters that can be determined with greater certainty by the external device 102 from a plurality of second obstacle parameters, in order to calculate a new set of third obstacle parameters.

[0088] The illustrated control device 1 further includes an enable unit 105 that calculates a first decision parameter based on a plurality of third obstacle parameters received from an obstacle parameter calculation unit 104, and enables driver assistance if the decision parameter is smaller than a predetermined activation threshold. In other words, the obstacle parameter calculation unit 104 transmits a plurality of third obstacle parameters to the enable unit 105, and the enable unit 105 derives comparison data (decision parameter) from the third obstacle parameters to determine whether to enable driver assistance. Driver assistance may be, for example, automatic braking, acceleration, or steering. Alternatively or additionally, driver assistance may also be auditory or visual signals that prompt the driver to take a specific action.

[0089] The enable unit 105 then compares the determination parameter with a predetermined activation threshold and enables the driving assistance if the determination parameter is below the threshold. For example, the enable unit 105 may calculate the determination parameter as the time until the vehicle reaches the obstacle (collision margin time) or the difference between the vehicle and the obstacle, based on a third obstacle parameter. In this case, the predetermined activation threshold may be a predetermined time or a predetermined distance.

[0090] In the illustrated example, the control device 1 may further include an activation unit 106 that can activate driver assistance based on an enable signal received from the enable unit. In this case, the enable unit 105 can transmit an enable signal to the activation unit 106, which then activates actuators / control elements for driver assistance in the vehicle, such as hydraulic valves for braking or steering and / or signal outputs for providing auditory or visual information. However, actuators may also be activated directly by the enable unit 105 (or the signal transmitted by it).

[0091] When the enable unit 105 derives a first decision parameter from a plurality of third obstacle parameters based on the most reliable obstacle parameters derived from the onboard sensor 100 and the external sensor 102, the enable unit 105 can determine the first decision parameter early with high accuracy. This allows the control device to activate driving assistance before an obstacle appears next to the vehicle, thus avoiding sudden driving maneuvers and improving driving comfort.

[0092] Figure 2 is a flowchart illustrating an example of the initialization procedure for the control device 1 shown in Figure 1. In particular, Figure 2 shows the initialization process of the prediction model included in the obstacle parameter calculation unit 104 of the control device 1 shown in Figure 1.

[0093] To verify whether initialization of the prediction model is required, in step S200, multiple / sets of previously calculated third obstacle parameters OP3[t-1][Q] are loaded by the obstacle parameter calculation unit 104, where the variable Q represents the matrix of the third obstacle parameters and the variable t represents time.

[0094] In the subsequent step S201, the prediction model of the obstacle parameter calculation unit 104 calculates the current set of third obstacle parameters OP3p[t][Q] based on the third obstacle parameter OP3[t-1][Q] determined in the previous step. Then, in step S202, the obstacle parameter calculation unit 104 receives the current set of first obstacle parameters OP1[t][M], where the variable M represents the matrix of first obstacle parameters.

[0095] Next, in step S203, the obstacle parameter calculation unit 104 compares the obstacle positions from the current set of third obstacle parameters OP3p[t][Q] with the obstacle positions from the set of first obstacle parameters OP1[t][M].

[0096] If both positions are the same, in step S208, the prediction model of the obstacle parameter calculation unit 104 is updated using the calculated current set OP3p[t][Q] of the third obstacle parameter and the position parameter OP1[t][m] of the multiple first obstacle parameters.

[0097] In addition, in step S208, when the obstacle parameter calculation unit 104 receives a new set of first obstacle parameters from the onboard sensor 100, the confidence index OP3[t][q].CONF of the third obstacle parameters is incremented. Each received set of first obstacle parameters from the onboard sensor 100 increases the certainty of obstacle detection, and therefore the confidence index OP3[t][q].CONF is incremented each time the obstacle parameter calculation unit 104 receives new first obstacle parameters from the onboard sensor 100.

[0098] Next, it is verified whether the confidence index OP3[t][q].CONF is greater than the first predetermined confidence threshold TH_CONF. If it is greater, in step S209, the confidence flag OP3[t][q].TGFLG of the third obstacle parameter is set to 1, indicating that multiple third obstacle parameters are available to the enable unit 105 as decision parameters (see Figure 6) to determine the collision margin time TTC[Q].

[0099] However, if the position parameters of the first and third obstacle parameters are not the same, in step S204, the obstacle parameter calculation unit 104 receives a plurality of second obstacle parameters OP2[t][N] determined by the external sensor 102, where the variable N represents a matrix of second obstacle parameters. In the next step S205, because there is a delay in communication between the external sensor 102 and the obstacle parameter calculation unit 104, the prediction model of the obstacle parameter calculation unit 104 calculates the current set of second obstacle parameters OP2p[t][N] based on the determined second obstacle parameters OP2[t][N].

[0100] Next, in step S206, the obstacle parameter calculation unit 104 compares the position of the obstacle from the current set of second obstacle parameters OP2p[t][N] with the position of the obstacle from the set of first obstacle parameters OP1[t][M].

[0101] If both positions are the same, in step S207, the prediction model of the obstacle parameter calculation unit 104 is initialized using the movement parameter OP2p[t][n] of the current second obstacle parameter OP2p[t][N] and the position parameter OP1[t][m] of the first obstacle parameter.

[0102] The process then continues, as described above, by verifying whether the confidence index OP3[t][q].CONF is greater than a predetermined confidence threshold TH_CONF. If so, in step S209, the confidence flag OP3[t][q].TGFLG of the third obstacle parameter is set to 1, indicating that multiple third obstacle parameters are available to the enable unit 105 to determine the collision margin time TTC[Q] as decision parameters (see Figure 6).

[0103] If the locations of obstacles in the first and second obstacle parameters are not the same, the obstacle parameter calculation unit 104 considers the obstacle detected by the onboard sensor 100 to be a new or other obstacle in the first obstacle parameter. In this case, the process described is repeated until the external sensor 102 detects a new obstacle in the second obstacle parameter.

[0104] Figures 3a and 3b schematically illustrate several examples of first, second, and third obstacle parameters that can be determined by the control device shown in Figure 1.

[0105] In particular, Figure 3a shows a plurality of first obstacle parameters OP1[t][M] determined by a first measuring device such as the onboard sensor 100 shown in Figure 1. The plurality of first obstacle parameters OP1[t][M] include the position of the obstacle in x and y coordinates PX1, PY1 detected by the first measuring device 100, as well as the detected direction of travel TH1, velocity VX1, VY1 in the x and y directions, yaw rate YAW1, and acceleration AX1, AY1 in the x and y directions. In addition, the plurality of first obstacle parameters OP1[t][M] shown include the certainty of the determined first obstacle parameters OP1[t][M] and a confidence index CONF1 representing the type / class of the detected obstacle CLS1, where the class may indicate the type of vehicle or obstacle or another traffic participant such as automobile, bicycle, fixed obstacle, or pedestrian.

[0106] Figure 3a also shows a plurality of second obstacle parameters OP2[t][N] containing the same types of parameters as the plurality of first obstacle parameters OP1[t][M]. The second obstacle parameters are determined by a second measuring device, such as the external sensor 102 shown in Figure 1, and are indicated accordingly by "2".

[0107] The first obstacle parameters are shown in a regular font, while the second obstacle parameters are marked with bold symbols (letters, numbers, etc.). In this way, it is highlighted which of the third obstacle parameters were obtained from the first obstacle parameters and which were obtained from the second obstacle parameters in order to determine the initial values ​​for the predictive model of the obstacle parameter calculation unit 104.

[0108] In other words, in Figure 3a, the value of the third obstacle parameter is shown in bold when it is obtained from the value of the second obstacle parameter, or when it is the value of the second obstacle parameter. The non-bold values ​​of the third obstacle parameter are obtained from the value of the first obstacle parameter.

[0109] The third obstacle parameter OP3[t][Q] is shown on the right side of Figure 3a, where the timestamp TM1, position PX1, PY1, and direction of travel TH1 of the position parameters are not printed in bold, i.e., they were obtained from the first obstacle parameter, while the velocity VX2, VY2, yaw rate YAW2, and acceleration AX2, AY2 of the motion parameters are printed in bold, i.e., they were obtained from the second obstacle parameter.

[0110] The illustrated third obstacle parameter OP3[t][Q] further includes a preferred reliability index consisting of the reliability indices CONF1 and CONF2 of the first and second obstacle parameters (which are added together, for example, as indicated by "+"), and the same type / class of detected vehicle as the first and second obstacle parameters CLS1 and CLS2. Furthermore, the third obstacle parameter includes a reliability flag TGFLG, which is preferably set to 0 at a first time when the obstacle is detected by the first measuring device 100 and the second measuring device 102.

[0111] Figure 3b shows the case where the obstacle is another vehicle that also acts as a second measuring device 102. In this case, the second obstacle parameter OP2[t][N] also includes the vehicle's width WD2 and height HT2, which are then passed on to the third obstacle parameter to initialize the predictive model of the obstacle parameter calculation unit 104.

[0112] Figure 4a is a schematic diagram illustrating an example of tracking an obstacle using a control device other than that shown in Figure 1, and Figure 4b is a schematic diagram illustrating an example of tracking an obstacle using the control device shown in Figure 1.

[0113] In the example shown in Figure 4a, only the onboard sensor 100a is used to detect obstacles. In this case, when an obstacle is detected, the obstacle tracking unit 400a is initialized using only the first obstacle parameter determined by the onboard sensor 100a. Furthermore, obstacle tracking is also performed based only on the first obstacle parameter determined by the onboard sensor 100a. The confidence level of the determined parameter increases according to the number of times the obstacle parameter has been determined by the onboard sensor 100a. If this confidence level exceeds a predetermined threshold, the obstacle tracking unit 400a changes the determined parameter from a low confidence level (value) to a high confidence level (value). Subsequently, when the collision margin time, etc., can be calculated by the enable unit 105a, if the collision margin time is less than a predetermined threshold, the activation unit 106a can activate the driver assistance.

[0114] Accordingly, an example of the teaching disclosed herein is illustrated in the example shown in Figure 4b. In this example, using the control device of Figure 1, an additional external sensor 102 is applied in addition to the onboard sensor 100b, which can detect obstacles earlier than the onboard sensor 100b but may have a longer communication path / lower communication speed to the obstacle tracking unit 400b. In this case, the obstacle tracking unit 400b, which may include at least the obstacle parameter calculation unit 104 of the control device in Figure 1, is initialized using position parameters obtained from first obstacle parameters determined by the onboard sensor 100b and movement parameters obtained from second obstacle parameters determined by the external sensor 102. Since the obstacle parameter with the highest initial confidence can be obtained / selected from the two sensors 100b and 102, the determined parameter confidence (value) converges / increases faster than in the example shown in Figure 4a. As a result, the collision margin can be calculated earlier by the enable unit 105b, which in turn leads to earlier activation of driver assistance by the activation unit 106b.

[0115] Figure 5 shows a flowchart of the control device subroutine in Figure 1, illustrating an example of how the first measurement unit recognizes whether it has detected the same obstacle as the second measurement unit. In particular, the process shown in the flowchart of Figure 5 illustrates an example in which the detection of the same obstacle is evaluated by comparing the obstacle positions of the first and second obstacle parameters in step S206 of Figure 2.

[0116] After the process in Figure 5 begins, the obstacle parameter calculation unit 104 first checks whether the first and second measuring devices have detected obstacles of the same type / class (first determination step in Figure 5), where the term CLS refers to the type / class of the obstacle, and the variables n and m represent the second and first obstacle parameters, respectively. Next, in step S500, the obstacle parameter calculation unit 104 calculates the distance dis between the positions of the obstacles detected by the first and second measuring devices using the least squares method (sqrt), where the terms PX and PY refer to the x and y coordinates of the obstacle positions. If the distance dis is smaller than a predetermined distance threshold TH_DISTANCE, in step S501, the detected obstacles are recognized as the same obstacle, and the process shown in the flowchart of Figure 2 continues to step S206. However, if the distance dis is greater than a predetermined distance threshold TH_DISTANCE, two different obstacles are recognized in step S502, and the process shown in the flowchart of Figure 2 returns to step S204. This also applies if the obstacle parameter calculation unit 104 determines different types of obstacles at the start of the process (the "no" path from the first determination step in Figure 5).

[0117] Figure 6 is a flowchart illustrating an example of enabling / activating driver assistance using the control device shown in Figure 1.

[0118] In the first step S600, in this example, the AEB_FLG flag for enabling driver assistance with automatic emergency braking (AEB) is set to 0, meaning that automatic emergency braking is disabled.

[0119] In the subsequent calculation loop, the enable unit 105 of the control device shown in Figure 1 checks whether the confidence flag TGFLG=1 is set for each of the multiple first obstacle parameters m=1,...,N. If the result is positive, it calculates the collision margin time TTC[m] based on each of the multiple first obstacle parameters m=1,...,N (S601). If the determined collision margin time TTC[m] is smaller than a predetermined activation threshold TH_TTC, in step S602, the enable unit activates the automatic emergency brake by setting the activation flag AEB_FLG to 1. If any of the above checks are negative, the process terminates the current calculation loop and proceeds to a second calculation loop in which the collision margin time TTC[q] is calculated based on multiple third obstacle parameters q=1,...,Q.

[0120] In a second calculation loop including steps S603 and S604, the process is carried out for a plurality of third obstacle parameters q=1,...,Q. In step S603, the collision margin time TTC[q] is calculated by the enable unit 105 based on the plurality of third obstacle parameters q=1,...,Q, and in step S604, if the determined collision margin time TTC[q] is smaller than a predetermined activation threshold TH_TTC, the automatic emergency brake is activated by the enable unit (AEB_FLG=1). If any of the checks performed in the second calculation loop are negative, the process returns to step S600 and proceeds further until the automatic emergency brake activation flag AEB_FLG is set to 1.

[0121] This means that automatic emergency braking can be activated either by a collision time margin (TTC) [m] calculated based on a first obstacle parameter, and / or by a collision time margin (TTC) [q] calculated based on a third obstacle parameter. The use of both sets of parameters, i.e., multiple first and third obstacle parameters, ensures, on the one hand, that automatic emergency braking is initialized even when a second measuring device is unavailable. On the other hand, using a third obstacle parameter to calculate the collision time margin allows for early activation of automatic emergency braking when a second measuring device is available, since the third obstacle parameter allows for the early setting of the confidence flag TGFLG=1. Thus, the reliability of the AEB function is further enhanced while simultaneously improving driving comfort.

[0122] Figure 7a is a schematic diagram illustrating an example of enabling driver assistance using a control device other than those shown in Figure 1, and Figure 7b is a schematic diagram illustrating an example of enabling driver assistance using the control device 1 shown in Figure 1.

[0123] In particular, Figure 7a shows an example in which driver assistance features such as emergency braking are enabled only based on multiple first obstacle parameters, and Figure 7b shows an example in which driver assistance features such as emergency braking are enabled based on multiple first and second obstacle parameters.

[0124] In both figures, a pedestrian 70, a boundary 72 (e.g., a building wall), and a vehicle 75 or V having an onboard sensor as the first measuring device are illustrated. At time T, the pedestrian 70 approaches the front of the vehicle 75 or V from the area behind the boundary 72.

[0125] According to Figure 7a, the vehicle's onboard sensor determines the first obstacle parameter OP1[T][m] at time T when the pedestrian 70 is first detected.

[0126] The pedestrian's position, initially determined by the onboard sensor, is marked by a frame surrounding the pedestrian. The first obstacle parameter OP1[T][m] includes the x and y coordinates of this position PX1,PY1, but does not include the pedestrian's speed 70, as the pedestrian's previous position is unknown at this point, and the pedestrian's speed can be determined by the vehicle 75's onboard sensor based on this. Therefore, the confidence index CONF1 of the first obstacle parameter OP1[T][n] is low at time T.

[0127] At time T+t1, the onboard sensors of vehicle 75 determine the first obstacle parameter OP1[T+t1][n] at least one more time (indicated by the length of the dotted arrows placed around the frame surrounding pedestrian 70), including the velocity of pedestrian 70 in the x and y directions VX,VY, which is adversely affected by a coefficient α less than 1, indicating that the variance of the determined velocity is still high due to the limited number of measurement points. The confidence index CONF1 of the first obstacle parameter OP1[T+t1][n] is increased at time T+t1 by ΣCONF, the number of times the onboard sensors of vehicle 75 determined the first obstacle parameter of pedestrian 70.

[0128] At time T+t2, the vehicle's onboard sensors observe the pedestrian 70 for an extended period (indicated by the increased length of the dotted arrow surrounding the pedestrian 70), thereby allowing the pedestrian's speeds VX1,VY1 to be determined with appropriate accuracy at this point. That is, the confidence index CONF1 of the first obstacle parameter exceeds the first predetermined confidence threshold TM_CONF, and the collision margin can be reliably calculated based on the first obstacle parameter OP1[T+t2][n] at time T+t2.

[0129] Conversely, Figure 7b shows an example where multiple first obstacle parameters are similarly determined by the vehicle V's onboard sensor 100, and in addition, multiple second obstacle parameters are determined by an external sensor, such as the pedestrian 70's mobile device. The external sensor can determine the pedestrian 70's second obstacle parameters before the vehicle's onboard sensor first detects the pedestrian 70 at time T. This is indicated by a dotted line frame surrounding the pedestrian 70's position when the pedestrian is still located in the area behind the boundary 72 where the pedestrian is not visible to the vehicle V's onboard sensor. The position of the pedestrian 70 first detected by the onboard sensor is, in this case as well, marked by a solid line surrounding the pedestrian 70. At that time, the pedestrian has already been observed by the external sensor for a specific time, indicated by the length of the dotted arrow attached to the solid line frame surrounding the pedestrian 70.

[0130] In this case, the control device 1 has already calculated several third obstacle parameters OP3[T][q] at time T, including the position PX1,PY1 of pedestrian 70a determined by the onboard sensor and the velocity VX,VY of pedestrian 70 determined by, for example, the pedestrian's mobile device. This velocity is negatively affected by a coefficient α less than 1, which indicates that the variance of the determined velocity is still high due to the limited number of measurement points. However, it is possible to provide the velocity of pedestrian 70 at a first time as detected by the onboard sensor of the vehicle V. Since the third obstacle parameter is calculated based on the position parameter of the first obstacle parameter and the movement parameter of the second obstacle parameter, the confidence index is higher than the confidence index CONF1, CONF2 of the first and second obstacle parameters, and therefore higher than the confidence index CONF1 at time T in Figure 7a.

[0131] At time T+t1, the onboard sensor 100 determines the first obstacle parameter OP1[T+t1][n] at least one more time, thereby increasing the confidence index CONF1+CONF2 by ΣCONF, the number of times the onboard device determined the first obstacle parameter of the pedestrian 70. Therefore, the value of the confidence index already exceeds a predetermined threshold TH_CONF at time T+t1. As a result, the collision margin can already be calculated with high confidence at time T+t1 based on the third obstacle parameter OP3[T+t1][n].

[0132] Figure 8 is a schematic diagram showing a control device 1a according to another example of the disclosed subject matter. In addition to the control device 1 shown in Figure 1, the shown control device 1a includes a specification acquisition unit 802 that can receive specifications of a plurality of second obstacle parameters from an external sensor (second measuring device) 102 and / or a second obstacle parameter acquisition unit 103. Furthermore, the vehicle V is provided with a storage 800 that stores map information about the area around the vehicle V and a signal strength acquisition unit 801 that can acquire the signal strength of wireless communications around the vehicle V (as shown). The specification acquisition unit 802, the map information storage 800, and the signal strength acquisition unit 801 can also be included in the obstacle parameter calculation unit 104 and therefore in the control device 1a (not shown). Alternatively, the storage 800 and the signal strength acquisition unit 801 may be located remotely from the vehicle V (not shown).

[0133] In the example illustrated in Figure 8, the obstacle parameter calculation unit 104 can calculate a confidence index that takes into account the specifications of multiple second obstacle parameters determined by the external sensor (second measuring device) 102. Because the external sensor 102 is located remotely from the vehicle V, the influence of the method of detecting and transmitting the second obstacle parameters may be stronger than in the case of the onboard sensor 100. For example, if the external sensor transmits GNSS-based messages, the accuracy may depend on the environment of the external sensor 102, since GNSS cannot provide signals, for example, in tunnels.

[0134] Therefore, the setting / specification of multiple second obstacle parameters may include information (specification parameters) regarding the characteristics / characteristics / quality of the second obstacle parameters, including the characteristics / characteristics / quality of the external sensor 102. This specification may include, for example, information regarding the sensor type of the external sensor 102, message type, signal resolution, and timestamps of the second measurement parameters, as well as any other transmission information regarding the characteristics / characteristics / quality of multiple second obstacle parameters. The specification acquisition unit 802 may receive the specifications of multiple second obstacle parameters from the external sensor 102 and / or the second obstacle parameter acquisition unit 103 before transmitting them to the obstacle parameter calculation unit 104 for further processing by the obstacle parameter calculation unit 104. The obstacle parameter calculation unit 104 may then adjust the value of the confidence index based on the multiple specification parameters received from the specification acquisition unit 802.

[0135] In addition to the specification of the second obstacle parameter, the specification acquisition unit 802 may receive multiple map information from the map information storage 801, including, for example, location information of buildings, as well as traffic information such as congestion, which will serve as the basis for the obstacle parameter calculation unit 104 to conclude the quality of the second obstacle parameter determined by the external sensor 102.

[0136] Furthermore, the specification acquisition unit 802 may receive from the signal strength acquisition unit 801 the signal strength of the wireless communication around the vehicle, which serves as the basis for determining the stability of the communication path between the external sensor 102 and the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802.

[0137] Additionally or alternatively, the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802 may determine the stability of the communication path between the external sensor 102 and the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802 from the information provided in the map information. For example, if vehicle V is traveling through an area with tall buildings, the buildings may interfere with communication with the external sensor 102, thus reducing the stability / reliability / quality of the communication. The same applies if the vehicle is traveling through a busy area where data traffic may be very high. These environmental conditions, which can be derived from the map information, can be used to determine the communication stability between the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802 and the external sensor 102, and the obstacle parameter calculation unit 104 may increase or decrease the reliability index value based on each condition.

[0138] Figures 9a and 9b are flowcharts illustrating an example in which the specifications of multiple second obstacle parameters are received, and the control device 1a shown in Figure 8 adjusts the reliability index of the second obstacle parameters based on the received specifications.

[0139] In particular, Figure 9a shows a number of specification parameters received by the obstacle parameter calculation unit 104 and / or specification acquisition unit 802 of the control device shown in Figure 8. In this exemplary case, the obstacle is another vehicle that has its own onboard measurement device and transmits cooperative awareness messages regarding its own status. Thus, the vehicle that is the obstacle may also act as a second measurement device 102 that transmits the number of specification parameters. In steps S900 to S905 of Figure 9a, the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802 receive a timestamp indicating the latest determination of the second obstacle parameter of the other vehicle (S900), the tracking time (observation length) of the other vehicle including the number of times the second obstacle parameter was acquired (S901), the speed distribution of the other vehicle (S902), an AEB flag indicating whether the automatic emergency braking of the other vehicle is enabled or not (S903), the message type of each second obstacle parameter (S904), and the communication stability between the other vehicle and the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802 (S905).

[0140] Furthermore, Figure 9b shows the initialization of the prediction model of the obstacle parameter calculation unit 104 by step S207 in Figure 2, which is performed when the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802 receive multiple specification parameters from other vehicles.

[0141] After the above process has started, in step S910, the confidence offset CONF_OFFSET is set to 0 by the obstacle parameter calculation unit 104. The confidence offset CONF_OFFSET may change depending on the specification parameters and may be added to the confidence index CONF2 of the second obstacle parameter. In the subsequent steps S920 to S970, the confidence offset CONF_OFFSET is adjusted by the obstacle parameter calculation unit 104 based on each of the multiple specification parameters received from other vehicles in steps S900 to S905 in Figure 9a. Then, in step S980, the prediction model of the obstacle parameter calculation unit 104 is initialized taking into account the adjusted confidence offset CONF_OFFSET in the confidence index CONF2. Then, step 207 in Figure 2 is completed and the process described herein continues by verifying whether the confidence index OP3[t][q].CONF is greater than the confidence threshold TH_CONF. Naturally, instead of a vehicle, the second measuring device may be a different obstacle or a generally different entity.

[0142] Figures 10a and 10b are flowcharts illustrating an example of adjusting the reliability index based on specification parameters using the control device 1a shown in Figure 8.

[0143] In particular, the flowchart in Figure 10a shows the adjustment of the confidence offset CONF_OFFSET based on the received timestamp indicating the latest second obstacle parameter determination of another vehicle, which was performed in step S920 of Figure 9b. After the start of the process, in step S1001, the confidence offset due to the delay time OFFSET_DT is set to 0. Then, in the subsequent steps S1002 and S1003, the current time NOW_TM and the received timestamp OP2[t][n].TM indicating the latest second obstacle parameter determination of another vehicle are determined. Based on these, the delay time dt for transmitting the second obstacle parameter to the obstacle parameter calculation unit 104 is calculated in step S1004.

[0144] If the delay time dt is greater than a predetermined delay time threshold TH_DT, the confidence offset due to the delay time OFFSET_DT is set to the value DELAY_BIG (S1005), and if the delay time dt is less than a predetermined delay time threshold TH_DT, the confidence offset due to the delay time OFFSET_DT is set to the value DELAY_SMALL (S1006). Instead of a single value DELAY_BIG or DELAY_SMALL, the confidence offset due to the delay time OFFSET_DT may be determined using a characteristic curve or formula that depends on the delay time dt.

[0145] Finally, in step S1007, the confidence offset CONF_OFF is reduced by the determined confidence offset value due to the delay time OFFSET_DT. Since the value DELAY_SMALL is smaller than the value DELAY_BIG, the confidence offset CONF_OFF is reduced by a smaller amount when the delay time dt is smaller than a predetermined delay time threshold TH_DT, and by a larger amount when the delay time dt is larger than a predetermined delay time threshold TH_DT. The process of adjusting the confidence index based on the specifications of the second obstacle parameter then proceeds to step S930 in Figure 9b, as shown in the flowchart illustrated in Figure 10b.

[0146] The flowchart in Figure 10b shows the adjustment of the confidence offset CONF_OFFSET based on the received tracking time (observation length) of another vehicle, which is performed in step S930 of Figure 9b. After the start of the process, in step S1010, the confidence offset due to the tracking time OFFSET_TRTM of the other vehicle is set to 0. Then, in step S1020, the tracking time OP2[t][n].TRTM is received from the other vehicle and then compared with a predetermined tracking time threshold TH_TRTM.

[0147] If the tracking time OP2[t][n].TRTM is greater than a predetermined tracking time threshold TH_TRTM, the confidence offset due to the tracking time OFFSET_TRTM of other vehicles is set to the value TRTM_LONG (S1030). If the tracking time OP2[t][n].TRTM is less than a predetermined tracking time threshold TH_TRTM, the confidence offset due to the tracking time OFFSET_TRTM of other vehicles is set to the value TRTM_SHORT (S1040).

[0148] Finally, in step S1050, the confidence offset CONF_OFF is reduced by the determined confidence offset value due to the tracking time OFFSET_TRTM of the other vehicle (indicated by '-="). Since the value TRTM_LONG is smaller than the value TRTM_SHORT, the confidence offset CONF_OFF is reduced by a smaller amount when the tracking time OP2[t][n].TRTM of the other vehicle is greater than a predetermined tracking time threshold TH_TRTM, and by a larger amount when the tracking time OP2[t][n].TRTM of the other vehicle is less than a predetermined tracking time threshold TH_TRTM. Next, the process of adjusting the confidence index based on the specifications of the second obstacle parameter proceeds to step S940 in Figure 9b, as shown in the flowchart illustrated in Figure 11a.

[0149] Figures 11a and 11b are flowcharts illustrating an example of adjusting a reliability index based on different specification parameters using the control device 1a shown in Figure 8.

[0150] In particular, the flowchart in Figure 11a shows the adjustment of the confidence offset CONF_OFFSET based on the received variance of the other vehicle's speed, which is performed in step S940 of Figure 9b. After the start of the process, in step S1100, the confidence offset due to the variance of the other vehicle's speed OFFSET_VVAR is set to 0. Then, in step S1101, the variance of the other vehicle's speed OP2[t][n].VVAR is received from the other vehicle (or another entity acting as a second measuring device / having a second measuring device) and is then compared with a predetermined variance threshold TH_VAR.

[0151] If the received variance OP2[t][n].VVAR of the speed of another vehicle is smaller than a predetermined variance threshold TH_VAR, the confidence offset due to the variance OFFSET_VVAR of the speed of the other vehicle is set to the value VVAR_SMALL (S1102). If the variance of the speed OP2[t][n].VVAR of the speed of another vehicle is larger than a predetermined variance threshold TH_VAR, the confidence offset due to the variance OFFSET_VVAR of the speed of the other vehicle is set to the value VVAR_BIG (S1103).

[0152] Finally, in step S1104, the confidence offset CONF_OFF is reduced by the determined confidence offset value due to the variance of the other vehicle's speed OFFSET_VVAR. If the value VVAR_SMALL is smaller than the value VVAR_BIG, the confidence offset CONF_OFF is reduced by a smaller amount when the speed of the other vehicle OP2[t][n].VVAR is smaller than a predetermined variance threshold TH_VAR, and by a larger amount when the variance of the other vehicle's speed OP2[t][n].VVAR is larger than a predetermined variance threshold TH_VAR. Next, the process of adjusting the confidence index based on the specifications of the second obstacle parameter proceeds to step S950 in Figure 9b, as shown in the flowchart illustrated in Figure 11b.

[0153] The flowchart in Figure 11b illustrates the adjustment of the confidence offset CONF_OFFSET based on the received AEB flag, indicating whether the automatic emergency braking of the other vehicle has been activated or not, as performed in step S950 in Figure 9b. After the process begins, in step S1110, the confidence offset resulting from the setting of the other vehicle's AEB flag OFFSET_AEBFLG is set to 0. Then, in step S1120, the setting of the other vehicle's AEB flag is received. If the AEB flag OP2[t][n].AEB_FLG is set to 1, it means that the automatic emergency braking of the other vehicle has been activated, and the confidence offset resulting from the setting of the other vehicle's AEB flag OFFSET_AEBFLG is set to the value AEBFLG_ON in step S1130, and in step S1140, the confidence offset CONF_OFFSET is increased by this value ("+="). When the AEB flag OP2[t][n].AEB_FLG is set to 0, it means that the automatic emergency braking of other vehicles is disabled, and the confidence value CONF_OFFSET is not increased due to the setting of the other vehicles' AEB flags. The process of adjusting the confidence index based on the specifications of the second obstacle parameter then proceeds to step S960 in Figure 9b, as shown in the flowchart illustrated in Figure 12.

[0154] Figure 12 is a flowchart illustrating an example of adjusting the reliability index based on another specification parameter using the control device 1a shown in Figure 8.

[0155] In particular, the flowchart in Figure 12 shows the adjustment of the confidence offset CONF_OFFSET based on the received message type for each second obstacle parameter, which is performed in step S960 of Figure 9b. After the process starts, in step S1200, the confidence offset due to the message type OFFSET_MSGTYPE of the second obstacle parameter is set to 0. Then, in step S1201, the message type OP2[t][n].MSG.TYPE of the second obstacle parameter is received from another vehicle (or similar in other examples).

[0156] If the message type is a collaborative recognition message (MSG_TYPE=DIRECT_FROM_CAR) that provides information about other vehicles, the confidence offset caused by the message type OFFSET_MSGTYPE of the second obstacle parameter is set to the value OS_DIRECT_FROM_CAR in step S1202.

[0157] Otherwise, it is checked whether a message has been received from the roadside unit (MSG_TYPE=DETECT_FROM_RSU). If it has been received, in step S1203, the confidence offset due to the message type OFFSET_MSGTYPE of the second obstacle parameter is set to the value OS_DETECT_FROM_RSU.

[0158] If not received, it is checked whether the message is a collective recognition message received from another vehicle that provides information about another object (MSG_TYPE=DETECT_FROM_CAR). If so, in step S1204, the confidence offset due to the message type OFFSET_MSGTYPE of the second obstacle parameter is set to the value OS_DETECT_FROM_CAR.

[0159] Otherwise, it is checked whether the message was received from a mobile device (MSG_TYPE=DETECT_CELLULAR). If it is received, in step S1205, the confidence offset due to the message type OFFSET_MSGTYPE of the second obstacle parameter is set to the value OS_DETECT_CELLULAR.

[0160] If the message has not been received, it is checked whether the message has been received from any other vehicle, and the confidence offset due to the message type OFFSET_MSGTYPE of the second obstacle parameter is set to the value OS_DETECT_OTHERS in step S1206.

[0161] Depending on the message type, the confidence offset CONF_OFFSET is increased in step S1207 by the value of the confidence offset caused by the message type OFFSET_MSGTYPE of the second obstacle parameter. In particular, with respect to the value of the confidence offset caused by the message type OFFSET_MSGTYPE of the second obstacle parameter, the following order from largest to smallest is applicable: OS_DIRECT_FROM_CAR > OS_DETECT_FROM_RSS > OS_DETECT_FROM_CAR > OS_DETECT_CELLULAR > OS_DETECT_OTHERS.

[0162] In other words, the confidence offset CONF_OFFSET may be increased by a maximum amount when the message type is a collaborative recognition message that provides direct information about other vehicles, and by a minimum amount when the message is received from another vehicle, roadside unit, or device other than a mobile device.

[0163] Figure 13 is a flowchart illustrating an example of adjusting the confidence index based on map information using the control device 1a shown in Figure 8. In particular, Figure 13 shows how the confidence offset caused by the message type OFFSET_MSGTYPE of the second obstacle parameter can be changed according to the vehicle's environmental conditions determined from the map information stored in the map information storage 800. After the process starts, in step S1300, map information providing information about the presence of tall buildings or enclosed environments such as tunnels in the area surrounding the vehicle is received by the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802. Furthermore, in step S1301, information regarding whether the vehicle is traveling under congested road conditions is received as map information. Next, in step S1302, a confidence offset value attributable to the message type OFFSET_MSGTYPE of the second obstacle parameter, such as OS_DIRECT_FROM_CAR, OS_DETECT_FROM_RSS, OS_DETECT_FROM_CAR, OS_DETECT_CELLULAR, and / or OS_DETECT_OTHERS, is selected for each message type in the table. The table of OFFSET_MSGTYPE values ​​may be stored, for example, in the map information storage 800 of the control device. If the message type is a cooperative recognition message (MSG_TYPE=DIRECT_FROM_CAR) that provides information about another vehicle, it is checked whether the other vehicle is stopped. If it is stopped, the value OS_DIRECT_FROM_CAR is set to be equal to the value OS_DETECT_FROM_RSS, as the other vehicle acts like a roadside unit at that time. If it is not stopped, the OFFSET_MSGTYPE value may remain unchanged.

[0164] Figure 14 is a flowchart illustrating an example of adjusting the reliability index based on another specification parameter using the control device 1a shown in Figure 8.

[0165] In particular, the flowchart in Figure 14 shows the adjustment of the confidence offset CONF_OFFSET based on the received communication stability between other vehicles and the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802, which is performed in step S970 of Figure 9b. After the process starts, in step S1400, map information is loaded from the map information storage 800 and delivered to the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802. Then, in step S1401, the confidence offset OFFSET_COMST due to the communication stability between other vehicles and the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802 is set to 0. Subsequently, the map information is checked to see if tall buildings exist in the area surrounding the vehicle. If they do, the value of the confidence offset OFFSET_COMST due to communication stability is reduced by the value MINUS_BUILD in step S1402.

[0166] If it does not exist, it is checked whether congested traffic exists in the area surrounding the vehicle. If it does exist, the value of the reliability offset OFFSET_COMST due to communication stability is reduced by the value MINUS_TC in step S1403.

[0167] If not present, it is checked whether vehicle V is traveling in a smooth communication area free from interference from obstacles and / or other devices. If it is, the value of the reliability offset OFFSET_COMST due to communication stability is increased by the value PLUS_COMGOOD in step S1404.

[0168] If the vehicle is not moving, the signal strength of the wireless communication around the vehicle is received in step S1405, and it is checked whether the signal strength is low. If the signal strength is low, the value of the confidence offset OFFSET_COMST due to communication stability is decreased by the value MINUS_INTBAD in step S1406. Finally, in step S1407, the confidence offset CONF_OFFSET is increased by the value resulting from the confidence offset OFFSET_COMST due to communication stability. Next, the process of adjusting the confidence index based on the specifications of the second obstacle parameter proceeds to step S980 in Figure 9b, where the prediction model of the obstacle parameter calculation unit 104 is initialized using the confidence offset CONF_OFFSET.

[0169] Naturally, it is possible to combine some of the reliability adjustment methods described in relation to the above diagram, or to select one or some of them.

[0170] Figures 15a and 15b are flowcharts illustrating an example of how the control device 1a shown in Figure 8 processes multiple second obstacle parameters detected by more than one second measuring device.

[0171] In particular, Figure 15a shows the process performed in step S204 of the initialization procedure illustrated in Figure 2 when multiple sets of the second obstacle parameter OP2[t][N] are received from multiple second measuring devices. After the process starts, it is determined whether more than one set of the second obstacle parameter has been received from more than one second measuring device. If so, the multiple sets of the second obstacle parameter are processed in step S1500. Otherwise, the received sets of the second obstacle parameter are used in step S1501. The process then returns to step S205 in Figure 2, where the current second obstacle parameter is calculated / predicted.

[0172] Furthermore, Figure 15b illustrates how multiple sets of second obstacle parameters are handled in step S1500 of Figure 15a. After the process begins, multiple sets of second obstacle parameters are received in step S1510. Then, in step S1520, the priority of these sets is determined. Next, in step S1530, the set of second obstacle parameters with the highest priority is selected as the multiple second obstacle parameters. The process then returns to step S205 of Figure 2, where the current second obstacle parameters are calculated / predicted.

[0173] Figure 16 is a flowchart illustrating an example of how the control device 1a shown in Figure 8 prioritizes multiple second obstacle parameters detected by more than one second measuring device.

[0174] In particular, Figure 16 shows a preferred example of how the priority of multiple sets of second obstacle parameters may be determined in step 1520 of Figure 15b. After the start of the process, in step S1600, map information is received from the map information storage. Based on the received map information, it is determined whether vehicle V is traveling in a closed environment such as a tunnel. If it is traveling in a closed environment, the vehicle status is set to the value INSIDE, which represents the reduced certainty of wireless communication around the vehicle. Otherwise, it is further determined whether the vehicle is traveling in a congested environment. If it is traveling in a congested environment, in step S1602, the vehicle status is set to the value CROWDED, which similarly represents the reduced certainty of wireless communication around the vehicle. Otherwise, in step S1603, the vehicle status is set to the value NORMAL, which represents the average certainty of wireless communication around the vehicle. Next, in step S1604, the message type for each second obstacle parameter is received, and then in step 1605, the priority of each set of second obstacle parameters is determined based on the status value and message type. In other words, the set of second obstacle parameters that provides the highest certainty is the result of the highest priority. The process then returns to step S1530 in Figure 15b, where the set of second obstacle parameters with the highest priority is selected as one of the multiple second obstacle parameters to be used.

[0175] Figures 17a to 17c are flowcharts illustrating another example of how the control device 1a shown in Figure 8 processes multiple obstacle parameters detected by more than one second measuring device.

[0176] In particular, Figure 17a shows steps S1700 to S1702, which are equivalent to steps S1510 to S1530 in Figure 15b. In addition, Figure 17a includes a further step S1703 in which the confidence index OP2[t][N].CONF of the second obstacle parameter is adjusted based on a set of more than one of the second obstacle parameters.

[0177] The process for adjusting the confidence index OP2[t][N].CONF in step S1703 is shown in Figure 17b. After the start of the method, the obstacle parameter calculation unit 104 executes a calculation loop in which the confidence index CONF_A is determined based on each second obstacle parameter OP2[t][n] having at least two sets of second obstacle parameters n=1,···,N. In step S1710 of the calculation loop, the confidence index CONF_A is initially set to 0. Then, it is checked whether one set of second obstacle parameters OP2[t][n] and the other set OP2_s[t][ns] have been received, and the variables n and ns represent the one and the other second obstacle parameters, respectively. If they have been received, the two sets of second obstacle parameters are compared in step S1711 to determine whether two different second measuring devices 102 have detected the same obstacle. If the result is positive, the confidence index CONF_A is increased by the value of the confidence index OP2_s[t][n].CONF for the other set of the second obstacle parameters. If the result is negative, the confidence index CONF_A remains 0.

[0178] In step 1713, the confidence index OP2[t][n].CONF of one set of the second obstacle parameters is increased by the confidence index value CONF_A. Thus, the confidence index OP2[t][n].CONF of one set of the second obstacle parameters is increased if both second measuring devices 102 detect the same obstacle.

[0179] Figure 17c shows the process performed in step S1711 of Figure 17b to determine whether both second measuring devices 102 have detected the same obstacle. After the start of the process, one and the other second measuring device check whether they have detected the same type / class of obstacle, where CLS refers to the type / class of obstacle. If they have detected the same type / class of obstacle, in step S1720 the distance dis between the locations of the obstacles detected by one and the other second measuring device is calculated using the least squares method (sqrt: square root), where PX and PY refer to the x and y coordinates of the obstacle locations. If the calculated distance dis is less than a predetermined distance threshold TH_DISTANCE, in step S1721 the detected obstacles are recognized as the same obstacle, and the process shown in the flowchart of Figure 17b continues to step S1712, where the confidence index CONF_A is increased by the confidence index value OP_s[t][n].CONF of the other set of second obstacle parameters. However, if the distance dis is greater than a predetermined distance threshold TH_DISTANCE, two different obstacles are recognized in step S1722, and the confidence index CONF_A remains 0. The confidence index CONF_A also remains 0 if different types of obstacles are determined in one set and the other set of the second obstacle parameters at the start of the process.

[0180] Figures 18a and 18b are flowcharts illustrating an example of adjusting the reliability index based on different fields of view of one or more second measuring devices using the control device 1a shown in Figure 8.

[0181] As a result, steps S1800-S1802 and S1804 are identical to steps S1710-S1713 in Figure 17b. In addition, Figure 18a includes step S1803 in which the confidence index CONF_A is adjusted based on the field of view of the other second measuring device. Next, the process of adjusting the confidence index CONF_A is described in Figure 18b.

[0182] In particular, after the start of the process, the field of view (FOV) of the other second measuring device is received in step S1810 to determine the other set of second obstacle parameters OP_s[t][Ns], where Ns is the matrix of the other second obstacle parameters. Subsequently, it is checked whether the detected obstacle was recognized in the field of view of the other second obstacle device. If recognized, the confidence index CONF_A remains unchanged, and the process returns to step S1804 in Figure 18a. Otherwise, it is checked whether the determined obstacle was recognized in the field of view of the other second measuring device 102. If not recognized, the determined obstacle was not recognized in the field of view of either of the two second measuring devices, and therefore the confidence index CONF_A remains unchanged, and the process returns to step S1804 in Figure 18a.

[0183] However, if the determined obstacle is recognized within the field of view of one of the second measuring devices, a discrepancy between both second measuring devices is determined, and in step S1820 of Figure 18b, before the process returns to step S1804 of Figure 18a, the confidence index CONF_A is reduced by the value CONF_CONTRADICTION.

[0184] Figures 19a and 19b schematically illustrate an example of driver assistance utilization when an obstacle is detected by a second measuring device 102 using the control device 1a shown in Figure 8. In particular, Figure 19a shows a vehicle 75a traveling between two boundaries 72 (e.g., a building) such that a pedestrian 70 walking behind one of the boundaries 72 is undetectable by the onboard sensor 100 of the vehicle V (here, 75a). However, the pedestrian 70 is recognized in the field of view of the onboard sensor of another vehicle 75b, such as another vehicle 75b traveling in a different direction and / or position.

[0185] Figure 19b shows the field of view 190 of the onboard sensor of the other vehicle 75b and the detection result 191 obtained by that onboard sensor. Comparing Figure 19a and Figure 19b, it can be seen that the position of the pedestrian 70 is correctly captured by the detection result 191 of the onboard sensor of the other vehicle 75b, but vehicle 75a is unable to "see" the pedestrian 70 using its onboard sensor, such as a stereo camera.

[0186] Figures 20a to 20c schematically show an example of a case where more than one obstacle is detected by the second measuring device 102 using the control device 1a shown in Figure 8.

[0187] In particular, Figure 20a shows a vehicle V (here referred to as 75a), a pedestrian 70, a boundary 72, and another vehicle 75b, as already illustrated in Figure 19a. In addition, Figure 20a shows a roadside unit 80 (e.g., a traffic camera) having a field of view perpendicular to (e.g.) the field of view of the onboard sensor of the other vehicle 75b.

[0188] Figure 20b shows the field of view 190a of the onboard sensor of another vehicle 75b, along with the detection result 191a already shown in Figure 19b. In addition, Figure 20b shows the field of view 190b of the roadside unit 80. Based on the field of view 190b of the roadside unit, two different detection results 191b and 191f are captured. Comparing Figure 20a and Figure 20b, it can be seen that the position of the pedestrian 70 is correctly captured by detection result 191b and incorrectly captured by detection result 191f.

[0189] Finally, Figure 20c shows the results considering the fields of view 190a and 190b of two second measuring devices 102, namely the onboard sensor of another vehicle 75b and the roadside unit 80.

[0190] When the roadside unit detection result 190b and the onboard sensor detection result 190a are taken into consideration, the pedestrian's position may be correctly detected by both second measuring devices, and the confidence index may increase. However, when the roadside unit detection result 190f and the onboard sensor detection result 190a are taken into consideration, inconsistencies may arise between the two second measuring devices 75b and 80, and the confidence index may have to decrease.

[0191] Figure 21 is a schematic diagram illustrating a control device 1b according to another example of the disclosed subject matter. In addition to the control device shown in Figure 8, the control device in Figure 21 includes an intervention enable unit 105a and a warning enable unit 105b instead of a single enable unit, and related intervention enable unit 106a and a warning enable unit 106b instead of a single enable unit. The warning enable unit 105b can calculate a warning decision parameter based on a plurality of third obstacle parameters that, when they fall below a predetermined warning enable threshold, can cause a warning enable by the warning enable unit 106b. Thus, the intervention enable unit 105a can calculate an intervention decision parameter based on a plurality of third obstacle parameters that, when they fall below a predetermined intervention enable threshold, can cause an intervention enable by the intervention enable unit 106b. Preferably, the predetermined warning enable threshold may be greater than the predetermined intervention enable threshold. For example, if the warning and / or intervention decision parameter is collision margin time, the warning enable threshold may include a collision margin time value greater than the intervention enable threshold. Thereafter, the warning may be enabled earlier than the intervention. As described in relation to Figure 8, for example, storage 800 and / or signal strength acquisition unit 801 may be part of the control device 1b, or other variations not shown may be possible in which they are located remotely / outside the vehicle V.

[0192] Figure 22 is a flowchart illustrating an example in which the specifications of multiple second obstacle parameters are received, and the control device shown in Figure 21 adjusts the reliability index of the second obstacle parameters based on the received specifications.

[0193] In particular, Figure 22 shows the initialization of the prediction model of the obstacle parameter calculation unit 104 using the confidence offset CONF_OFFSET when the obstacle parameter calculation unit 104 and / or the specification acquisition unit 802 receive multiple specification parameters from a second measurement device 102 which may be (or may be included in) another vehicle (in this case, these parameters may also be received by the obstacle parameter calculation unit 104 from the respective obstacle parameter acquisition units described above and illustrated in Figure 8 or Figure 21). The specification parameters on which the confidence offset CONF_OFFSET is adjusted according to Figure 22 are the same as those shown in Figures 9a and 9b. In particular, steps S2200 to S2207 in Figure 22 are identical to steps S910 to S970 in Figure 9b, meaning that each received specification parameter is considered in relation to the adjustment of the confidence offset CONF_OFFSET in steps S2201 to S2207, and then in step S2207, the prediction model is initialized using the adjusted confidence offset CONF_OFFSET. In addition to the initialization process shown in Figure 9b, the obstacle parameter calculation unit 104 checks after initialization in step S2207 whether the confidence index OP3[t],[q].CONF of the third obstacle parameter is greater than the second predetermined threshold TH_SEPARATE. If the value is greater than the threshold, in step S2208, the predictive model is initialized with the same third obstacle parameter, regardless of whether the third obstacle parameter is used to calculate a warning decision parameter, an intervention decision parameter, or a general decision parameter (OP3W[t][q]=OP3I[t][q]=OP3[t][q]) on which driving assistance may be enabled. If the value is not greater than the threshold, two separate sets of third obstacle parameter units are initialized in step S2209, where the third obstacle parameter OP3W[t][q] used to calculate the warning decision parameter is based on the first and second obstacle parameters, and the third obstacle parameter OP3I[t][q] used to calculate the intervention decision parameter is based on the first obstacle parameter only.

[0194] This means that even if the confidence index OP3[t],[q].CONF is less than a second predetermined threshold TH_SEPARATE, a warning (or its activation) may be enabled / triggered based on a combination of position parameters obtained from a plurality of first obstacle parameters and movement parameters obtained from a plurality of second obstacle parameters. However, in this case, intervention in the driver's driving behavior may be performed based only on first obstacle parameters which may be determined by the vehicle's onboard measuring device. This ensures that the entire control of the driver assistance system can remain with the vehicle if the external measuring device may have an unknown certainty or a certainty below a predefined threshold.

[0195] Figure 23a is a flowchart illustrating an example of the initialization process of the control device shown in Figure 21. In particular, Figure 23a shows the initialization process of the prediction model included in the obstacle parameter calculation unit 104 of the control device shown in Figure 21, relating to the calculation result of the third obstacle parameter OP3W[t][q] used to calculate the warning judgment parameter.

[0196] To verify whether initialization of the prediction model is required, in step S2300 of Figure 23a, multiple / sets of previously calculated third obstacle parameters OP3W[t-1][Q] are loaded by the obstacle parameter calculation unit 104, where the variable Q represents the matrix of the third obstacle parameters and the variable t represents time.

[0197] In the subsequent step S2301, the prediction model of the obstacle parameter calculation unit 104 calculates the current set of third obstacle parameters OP3Wp[t][Q] based on the third obstacle parameter OP3W[t-1][Q] determined in the previous step. Then, in step S2302, the obstacle parameter calculation unit 104 receives the current set of first obstacle parameters OP1[t][M], where the variable M represents the matrix of first obstacle parameters.

[0198] Next, in step S2303, the obstacle parameter calculation unit 104 compares the obstacle positions from the current set of third obstacle parameters OP3Wp[t][Q] with the obstacle positions from the set of first obstacle parameters OP1[t][M].

[0199] If both positions are the same, in step S2308, the prediction model of the obstacle parameter calculation unit 104 is updated using the calculated current set OP3Wp[t][Q] of the third obstacle parameter and the position parameter OP1[t][m] of the multiple first obstacle parameters.

[0200] In addition, in step S2308, when the obstacle parameter calculation unit 104 receives a new set of first obstacle parameters from the onboard sensor 100, the confidence index OP3W[t][q].CONF of the third obstacle parameters is incremented (not shown). Each received set of first obstacle parameters from the onboard sensor 100 increases the certainty of obstacle detection, and therefore the confidence index OP3W[t][q].CONF is incremented each time the obstacle parameter calculation unit 104 receives a new set of first obstacle parameters from the onboard sensor 100.

[0201] Next, it is verified whether the confidence index OP3W[t][q].CONF is greater than a predetermined warning confidence threshold TH_CONF_W. If it is greater, in step S2309, the confidence flag OP3W[t][q].TGFLG for the third obstacle parameter is set to 1, indicating that multiple third obstacle parameters are available to the enable unit 105 as warning determination parameters (see Figure 24) to determine the collision margin time TTC[Q].

[0202] However, if the position parameters of the first and third obstacle parameters are not the same, in step S2304, the obstacle parameter calculation unit 104 receives a plurality of second obstacle parameters OP2[t][N] determined by the external sensor 102, where the variable N represents a matrix of second obstacle parameters. In the next step S205, because there is a delay in communication between the external sensor 102 and the obstacle parameter calculation unit 104, the prediction model of the obstacle parameter calculation unit 104 calculates the current set of second obstacle parameters OP2p[t][N] based on the determined second obstacle parameters OP2[t][N].

[0203] Next, in step S2306, the obstacle parameter calculation unit 104 compares the position of the obstacle from the current set of second obstacle parameters OP2p[t][N] with the position of the obstacle from the set of first obstacle parameters OP1[t][M].

[0204] If both positions are the same, in step S2307, the prediction model of the obstacle parameter calculation unit 104 is initialized using the movement parameter OP2p[t][n] of the current second obstacle parameter OP2p[t][N] and the position parameter OP1[t][m] of the first obstacle parameter.

[0205] The process then continues, as described above, by verifying whether the confidence index OP3W[t][q].CONF is greater than a predetermined warning confidence threshold TH_CONF_W. If so, in step S2309, the confidence flag OP3W[t][q].TGFLG for the third obstacle parameter is set to 1, indicating that multiple third obstacle parameters are available to the warning enable unit 105b as decision parameters (see Figure 24) to determine the collision margin time TTC[Q].

[0206] After the confidence flag OP3W[t][q].TGFLG for the third obstacle parameter is set to 1 in step S2309, the process continues as shown in Figure 23b.

[0207] Figure 23b shows the initialization process of the prediction model included in the obstacle parameter calculation unit 104 of the control device 1b shown in Figure 21, relating to the calculation results of the third obstacle parameter OP3I[t][q] used to calculate the intervention decision parameter.

[0208] To verify whether initialization of the prediction model is required, in step S2310, multiple / sets of previously calculated third obstacle parameters OP3I[t-1][Q] are loaded by the obstacle parameter calculation unit 104, where the variable Q represents the matrix of the third obstacle parameters and the variable t represents time.

[0209] In the subsequent step S2311, the prediction model of the obstacle parameter calculation unit 104 calculates the current set of third obstacle parameters OP3Ip[t][Q] based on the third obstacle parameter OP3I[t-1][Q] determined in the previous step. Then, in step S2312, the obstacle parameter calculation unit 104 receives the current set of first obstacle parameters OP1[t][M], where the variable M represents the matrix of first obstacle parameters.

[0210] Next, in step S2313, the obstacle parameter calculation unit 104 compares the obstacle positions from the current set of third obstacle parameters OP3Ip[t][Q] with the obstacle positions from the set of first obstacle parameters OP1[t][M].

[0211] If both positions are the same, in step S2318, the prediction model of the obstacle parameter calculation unit 104 is updated using the calculated current set OP3Ip[t][Q] of the third obstacle parameter and the position parameter OP1[t][m] of the multiple first obstacle parameters.

[0212] In addition, in step S208, when the obstacle parameter calculation unit 104 receives a new set of first obstacle parameters from the onboard sensor 100, the confidence index OP3I[t][q].CONF of the third obstacle parameters is incremented (not shown). Each received set of first obstacle parameters from the onboard sensor 100 increases the certainty of obstacle detection, and therefore the confidence index OP3I[t][q].CONF is incremented each time the obstacle parameter calculation unit 104 receives a new set of first obstacle parameters from the onboard sensor 100.

[0213] Next, it is verified whether the confidence index OP3I[t][q].CONF is greater than a predetermined intervention confidence threshold TH_CONF_1. If it is greater, in step S2319, the confidence flag OP3I[t][q].TGFLG for the third obstacle parameter is set to 1, indicating that multiple third obstacle parameters are available to the enable unit 105 as decision parameters (see Figure 24) to determine the collision margin time TTC[Q].

[0214] However, if the position parameters of the first and third obstacle parameters are not the same, in step S2314, the obstacle parameter calculation unit 104 receives a plurality of second obstacle parameters OP2[t][N] determined by the external sensor 102, where the variable N represents a matrix of second obstacle parameters. In the next step S2315, because there is a delay in communication between the external sensor 102 and the obstacle parameter calculation unit 104, the prediction model of the obstacle parameter calculation unit 104 calculates the current set of second obstacle parameters OP2p[t][N] based on the determined second obstacle parameters OP2[t][N].

[0215] Next, in step S206, the obstacle parameter calculation unit 104 compares the position of the obstacle from the current set of second obstacle parameters OP2p[t][N] with the position of the obstacle from the set of first obstacle parameters OP1[t][M].

[0216] If both positions are the same, in step S2317, the prediction model of the obstacle parameter calculation unit 104 is initialized using the movement parameter OP2p[t][n] of the current second obstacle parameter OP2p[t][N] and the position parameter OP1[t][m] of the first obstacle parameter.

[0217] The process then continues, as described above, by verifying whether the confidence index OP3I[t][q].CONF is greater than a predetermined intervention confidence threshold TH_CONF_I. If so, in step S2319, the confidence flag OP3I[t][q].TGFLG for the third obstacle parameter is set to 1, indicating that multiple third obstacle parameters are available to the enable unit 105 to determine the collision margin time TTC[Q] as intervention decision parameters (see Figure 24).

[0218] Figure 24 is a flowchart illustrating an example of enabling warnings and / or interventions as driver assistance using the control device 1b shown in Figure 21.

[0219] After this process is initiated, the warning activation flag WARN_FLG and the automatic emergency braking (AEB) activation flag AEB_FLG are set to 0 in steps S2400 and S2401, which means that the warning device and automatic emergency braking are disabled.

[0220] In the subsequent calculation loop, the warning enable unit 105a of the control device shown in Figure 21 checks whether the confidence flag TGFLG=1 is set for each of the multiple first obstacle parameters m=1,...,N. If the result is positive, it calculates the collision margin time TTC[m] based on each of the multiple first obstacle parameters m=1,...,N (S2402).

[0221] If the determined collision margin time TTC[m] is less than a predetermined warning activation threshold TH_TTC_W, in step S2403, the warning enable unit 105a activates the warning by setting the activation flag WARN_FLG to 1.

[0222] Next, the intervention enable unit 105b checks whether the collision margin time TTC[m] is less than a predetermined intervention enablement threshold TH_TTC_I. If it is less than the threshold, the automatic emergency braking enablement flag AEB_FLG is set to 1 in step S2404 in order to enable automatic emergency braking.

[0223] If any of the above checks are negative, the process terminates the current calculation loop and proceeds to a second calculation loop in which the collision margin time TTC[q] is calculated based on several third obstacle parameters q=1,…,Q.

[0224] In a second calculation loop including steps S2405 to S2408, the above process is performed for a plurality of third obstacle parameters q=1,...,Q. In step S2405, the collision margin time TTC[q] is calculated by the warning enable unit 105a based on the plurality of third obstacle parameters q=1,...,Q, and in step S604, if the determined collision margin time TTC[q] is smaller than a predetermined warning activation threshold TH_TTC_W, a warning is activated by the warning enable unit (WARN_FLG=1).

[0225] Next, it is verified whether the confidence flag TGFLG=1 is set for each of the third obstacle parameters OP3I[t][q]. If it is set, the intervention enable unit 105b calculates the collision margin time TTC[q] based on each of the multiple third obstacle parameters q=1,...,Q.

[0226] If the determined collision margin time TTC[q] is less than a predetermined intervention activation threshold TH_TTC_I, in step S2408, the intervention enable unit 105b activates the automatic emergency brake by setting the activation flag AEB_FLG to 1.

[0227] If any of the checks performed in the second calculation loop are negative, the process returns to step S2400 and proceeds further until the warning flag WARN_FLG and / or the automatic emergency braking enable flag AEB_FLG are set to 1.

[0228] This means that automatic emergency braking, along with a warning, can be activated either by a collision time tolerance (TTC) [m] calculated based on a first obstacle parameter, and / or by a collision time tolerance (TTC) [q] calculated based on a third obstacle parameter. The use of both sets of parameters, i.e., multiple first and third obstacle parameters, ensures, on the one hand, that warnings and automatic emergency braking are initialized even when a second measuring device is unavailable. On the other hand, by using a third obstacle parameter to calculate the collision time tolerance, early activation of warnings or automatic emergency braking is achieved when a second measuring device is available.

[0229] Figure 25a is a schematic diagram showing an example of driver assistance performed using a control device other than that shown in Figure 21, and Figure 25b is a schematic diagram showing an example of driver assistance performed using control device 1b shown in Figure 21.

[0230] In particular, Figure 25a shows an example in which warning and emergency braking (AEB) is subsequently performed only based on a plurality of first obstacle parameters determined by the vehicle's onboard sensors, and Figure 25b shows an example in which warning and emergency braking is subsequently performed based on a plurality of first and second obstacle parameters.

[0231] In both figures, a pedestrian 70, a boundary 72 (e.g., a wall or building), and a vehicle 75 (V is used in Figure 25b) having an onboard sensor 100 as the first measuring device are illustrated. At time T, the pedestrian 70 approaches the vehicle 75 from the area behind the boundary 72.

[0232] According to Figure 25a, the vehicle's onboard sensor determines the first obstacle parameter OP1[T][m] at time T when the pedestrian 70 is first detected.

[0233] The pedestrian's position, initially determined by the onboard sensor, is marked by a frame surrounding the pedestrian. The first obstacle parameter OP1[T][m] includes the x and y coordinates of this position PX1,PY1, but does not include the pedestrian's speed 70, as the pedestrian's previous position is unknown at this point, and the pedestrian's speed can be determined by the vehicle 75's onboard sensor based on this. The confidence index at time T in Figure 25a includes the confidence CONF1 of the first obstacle parameter at the current time, and an offset that may depend on environmental conditions, for example, affecting the certainty of the received message.

[0234] At time T+t1, the vehicle 75's onboard sensor determines the first obstacle parameter OP1[T+t1][n] at least one more time (indicated by the length of the dotted arrow attached to the frame surrounding the pedestrian 70), including the pedestrian 70's speed in the x and y directions VX,VY, which is adversely affected by a coefficient σ less than 1, indicating that the variance of the determined speed is still high due to the limited number of measurement points. The confidence index CONF1 of the first obstacle parameter OP1[T+t1][n] is increased at time T+t1 by the number of times ΣCONF the vehicle 75's onboard sensor determined the first obstacle parameter of the pedestrian 70. In particular, since the confidence index is higher than a predetermined warning confidence threshold TH_CONF_W, the warning determination parameter can be determined with sufficient certainty based on the first obstacle parameter present at time T+t1, and the warning can be activated by the warning enable unit 105a.

[0235] At time T+t2, the onboard sensors of the vehicle 75 observe the pedestrian 70 for a longer period (indicated by the increased length of the dotted arrows placed around the pedestrian 70), thereby allowing the pedestrian 70's speed VX1,VY1 to be determined with appropriate accuracy at this point. Furthermore, the confidence index CONF1 of the first obstacle parameter exceeds a predetermined intervention confidence threshold TM_CONF_1, thereby allowing the intervention decision parameter to be calculated with high confidence based on the first obstacle parameter OP1[T+t2][n] at time T+t2, and enabling the automatic emergency brake to be activated by the intervention enable unit 105b.

[0236] Conversely, Figure 25b shows an example in which warning and automatic emergency braking (AEB) are subsequently performed based on a plurality of first and second obstacle parameters. In other words, Figure 25b shows an example in which a plurality of first obstacle parameters are similarly determined by the vehicle V's onboard devices / sensors 100, and in addition, a plurality of second obstacle parameters are determined by an external sensor 102, such as the pedestrian 70's mobile device. The external sensor can determine the pedestrian 70's second obstacle parameters before the vehicle V's onboard sensors first detect the pedestrian 70 at time T. This is indicated by a dotted line frame surrounding the pedestrian 70's position when the pedestrian 70 is still located in an area behind a boundary 72 that is not visible to the vehicle V's onboard sensors. The pedestrian 70's position as first detected by the onboard sensors is, again, marked by a solid line surrounding the pedestrian 70. At that time, the pedestrian is observed by the external sensor for a specific period of time, indicated by the length of the dotted arrow attached to the solid line frame surrounding the pedestrian 70.

[0237] According to Figure 25b, the obstacle parameter calculation unit 104 of the control device 1b shown in Figure 21 determines a first set of a plurality of third obstacle parameters for determining warning judgment parameters and a second set of a plurality of third obstacle parameters for determining intervention judgment parameters. The first set of a plurality of third obstacle parameters is based on the first and second obstacle parameters, while the second set of a plurality of third obstacle parameters is based solely on the first obstacle parameters. This means that interventions in the driver's driving behavior can be performed based only on the first set of obstacle parameters, which are preferably determined by the vehicle V's onboard measuring device. This ensures that the entire control of the driver assistance system can remain in use with the vehicle when the external measuring device is not 100% reliable.

[0238] At time T, the first set of third obstacle parameters OP3W[T][q] includes the position PX1,PY1 of the pedestrian 70 determined by the onboard sensor and the velocity VX,VY of the pedestrian 70 determined by the external sensor. This velocity is adversely affected by a coefficient ω less than 1, which indicates that the variance of the determined velocity is still high due to the limited number of measurement points. Nevertheless, it is possible to provide the velocity of the pedestrian 70 at the first time as detected by the onboard sensor of the vehicle V. The confidence index at time T in Figure 25b includes the confidence indices CONF1,CONF2 of the first and second obstacle parameters at the current time, and the offset, and is therefore higher than the confidence index in Figure 25a.

[0239] The second set of third obstacle parameters OP3I[T][q] includes only the position PX1,PY1 of pedestrian 70 determined by the onboard sensor. Since these third obstacle parameters are based only on the first obstacle parameters, the pedestrian's velocity cannot be provided at time T. However, the confidence index of the second set of third obstacle parameters is identical to the confidence index of the first set of third parameters OP3W[T][q] when the second obstacle parameters are available in both cases. This is why the confidence of the second set of third obstacle parameters OP3I[T][q] also increases faster than the confidence of the first obstacle parameters.

[0240] At time T+t1', the first obstacle parameter has been determined at least one more time, and therefore the confidence index of the first and second multiple third obstacle parameters OP3W[T+t1'][q] and OP3i[T+t1'][q] has increased by ΣCONF the number of times the vehicle V's onboard sensor determined the first obstacle parameter of pedestrian 70 at time T+t1'. In addition, the variance of velocities VX,VY included in the first multiple third obstacle parameters OP3W[T+t1'][q] is reduced due to the increase in observation time. Velocities are still adversely affected by coefficient β, but can be higher than coefficient ω. The second multiple third obstacle parameters are calculated at this point based on the first obstacle parameter determined by the vehicle V's onboard sensor and include velocities adversely affected by coefficient α, which is smaller than coefficient β. The confidence index of the first plurality of third obstacle parameters exceeds a predetermined warning confidence threshold TH_CONF_W at time T+t1', and therefore the warning determination parameter can be determined with sufficient certainty based on the first plurality of third obstacle parameters OP3W[T+t1'][q] present at time T+t1', and the warning can be activated by the warning enable unit 105a.

[0241] Time t1' is less than time t1, meaning that in this case, the warning enable unit 105a can enable the warning earlier than shown in Figure 25a, and only the first obstacle parameter is used as a basis for determining the warning judgment parameter.

[0242] Since the confidence index CONF2 of the second obstacle parameter is considered for the first and second multiple third obstacle parameters OP3W[T+t2'][q] and OP3I[T+t2'][q], automatic emergency braking can also be performed earlier than in Figure 26a, i.e., at time T+t2'.

[0243] At this time, the confidence index of the second multiple third obstacle parameters exceeds the intervention confidence threshold TH_CONF_I, and therefore the intervention decision parameter can be determined with sufficient certainty based on the second multiple third obstacle parameters OP3I[T+T2'][q] present at time T+t2', and the automatic emergency brake can be activated by the intervention enable unit 105b.

[0244] Figure 26 schematically shows the results of the driver assistance examples in Figures 25a and 25b in the form of a timeline t, where the target time points T, T+t1, T+t1', T+t2, and T+t2' are marked, from the first observation at time T to the activation of automatic emergency braking at times T+t2 and T+t2'. Here, the time points for the example where only onboard sensors were used are shown at the top of the timeline, and the time points for the example where both onboard and external sensors were used are shown at the bottom of the timeline.

[0245] The combination of the onboard sensor 100 and the external sensor 102 is found to enable earlier triggering of automatic emergency braking (AEB), along with earlier triggering of warnings, compared to the use of the onboard sensor 100 alone. This is also applicable to parameter determination for automatic emergency braking where the parameters of the external sensor 102 are not used at all. However, since these parameters are available to enable warnings, this also increases the certainty of the parameters used for emergency braking.

[0246] Figure 27 is a schematic diagram illustrating a control device 1c according to another example of the disclosed subject matter. The control device in Figure 27 differs from the control device illustrated in Figure 1 in that the first and second measuring devices are external sensors 102a and 102b, i.e., both sensors are located outside the vehicle V. In this case, the external sensors 102a and 102b located closer to the vehicle V can function as the first measuring device, and the external sensors 102a and 102b located further away from the vehicle V can function as the second measuring device. Therefore, the external sensors 102a and 102b closer to the vehicle have a shorter latency period than the external sensors 102a and 102b located further away. On the other hand, the external sensors 102a and 102b located further away from the vehicle can detect obstacles earlier than the external sensors 102a and 102b located closer. It will be further understood that the two external sensors 102a and 102b provide their data to the respective first and second obstacle parameter acquisition units 101 and 103 inside the control device 1c.

[0247] For example, a roadside unit (e.g., a camera) immediately to the right of a vehicle may act as a first measuring device, and if a pedestrian with a smartphone appears as an obstacle around the vehicle, the pedestrian's smartphone may act as a second measuring device. The control device 1c receives signals from each of the external sensors 102a and 102b and may determine, for example, based on the signal strength, which of the two external sensors 102a and 102b should act as the first and second measuring devices. Subsequently, the obstacle parameter calculation unit 104 receives first and second obstacle parameters from both external sensors 102a and 102b (preferably via the first / second obstacle parameter acquisition units 101 and 103 as described above) and may calculate a third obstacle parameter based on the parameter with the highest certainty.

[0248] Figure 28 schematically illustrates an example of driver assistance when an obstacle is detected using the control device 1c shown in Figure 27. Specifically, Figure 28 shows a vehicle V, a roadside unit 80, a boundary 72 (wall, building, etc.), a pedestrian 70a, and a cellular base station 85. The pedestrian 70a carries a mobile device that sends and receives GNSS-based messages via a cellular network provided by the cellular base station 85 (indicated by two lightning bolt icons shown between the cellular base station 85 and the pedestrian). This cellular network also reaches the vehicle V (indicated by a lightning bolt icon between the cellular base station 85 and the vehicle V), thereby allowing the pedestrian 70a's mobile device to exchange messages with the vehicle V.

[0249] Pedestrian 70a approaches the vicinity of vehicle V from an area behind boundary 72, which is outside the vehicle V's field of view. Vehicle V is moving backward, causing its onboard sensor's field of view 190a to be oriented incorrectly, thereby rendering it unusable as a first measuring device. However, the roadside unit 80 is positioned next to vehicle V, thereby ensuring high-speed communication with a control device 1c that may be located on vehicle V (indicated by two lightning bolt icons between roadside unit 80 and vehicle V). Furthermore, the roadside unit can detect obstacles around vehicle V due to its field of view 190b capturing the entire area surrounding the vehicle. Thus, the roadside unit can serve as a first measuring device providing positional parameters for pedestrian 70a, while the pedestrian's mobile device can serve as a second measuring device providing movement parameters for pedestrian 70a. Thus, the third obstacle parameter for pedestrian 70a can be calculated with high reliability by the obstacle parameter calculation unit 104 of the control device based on a plurality of first obstacle parameters received from the roadside unit 80 and a plurality of second obstacle parameters received from the pedestrian's mobile device.

[0250] Figures 29a and 29b schematically illustrate an example of driver assistance performed using a control device other than that shown in Figure 27, in comparison to an example of driver assistance performed using control device 1c shown in Figure 27. Both figures show the situation already illustrated in Figure 28, where vehicle 75 / V is moving backward and pedestrians 70, 70a are approaching the rear of vehicle 75 / V from the area behind boundary 72.

[0251] In particular, Figure 29a shows an example where only a number of first obstacle parameters are determined by the roadside unit 80, and the pedestrian 70's mobile device is not used as a second measurement device (indicated by the absence of a lightning bolt icon between the cellular base station 85 and the pedestrian 70). At time T, the roadside unit 80 first recognizes the pedestrian 70 and determines the pedestrian's position PX1,PY1. The position where the roadside unit 80 first detected the pedestrian 70 is marked by a frame surrounding the pedestrian 70. Since the pedestrian 70's previous position, which could be the basis for determining the pedestrian's speed, is not known at that time, the pedestrian 70's speed is determined to be 0 by the roadside unit 80 at time T. Therefore, the confidence index CONF1 of the first obstacle parameter OP1[T][n] is low at time T.

[0252] At time T+t1, the roadside unit 80 has determined the first obstacle parameter OP1[T+t1][n] at least one more time, which includes the speed of pedestrian 70 in the x and y directions VX,VY, which is adversely affected by a coefficient α indicating that the speeds VX,VY have low confidence. The confidence index CONF1 of the first obstacle parameter OP1[T+t1][n] has increased at time T+t1 by SCONF, which is the number of times the roadside unit 80 has determined the first obstacle parameter for pedestrian 70.

[0253] At time T+t2, the roadside unit 80 has observed the pedestrian 70 for a longer period of time, thereby allowing the pedestrian's velocities VX1 and VY1 to be determined with appropriate accuracy at this point. That is, the confidence index of the first obstacle parameter CONF1 exceeds the first predetermined confidence threshold TM_CONF, and the collision margin can be calculated with high confidence based on the first obstacle parameter OP1[T+t2][n] at time T+t2.

[0254] Conversely, Figure 29b shows an example in which multiple first obstacle parameters are determined by the roadside unit 80, and in addition, multiple second obstacle parameters are determined by the pedestrian 70a's mobile device.

[0255] In this case, the control device 1c has already calculated several third obstacle parameters OP3[T][q] at time T, including the position PX1, PY1 of pedestrian 70a determined by the roadside unit 80 and the velocity VX, VY of pedestrian 70a determined by the pedestrian's mobile device. The pedestrian's previous position is marked with a dotted line frame to indicate that pedestrian 70a may have been observed before pedestrian 70a was first detected by roadside unit 80. This velocity is negatively affected by a coefficient β less than 1, which indicates that the variance of the determined velocity is still high due to the limited number of measurement points. Nevertheless, it is possible to provide the velocity of pedestrian 70a at a first time when pedestrian 70a was detected by roadside unit 80. The pedestrian's previous position is marked with a dotted line frame to indicate that pedestrian 70a may have been observed by pedestrian 70a's mobile device before pedestrian 70a was first detected by roadside unit 80.

[0256] Since the third obstacle parameter is calculated based on the position parameter of the first obstacle parameter and the movement parameter of the second obstacle parameter, the confidence index takes into account the confidence indices CONF1 and CONF2 of the first and second obstacle parameters, and is therefore higher than the confidence index CONF1 at time T in Figure 29a.

[0257] At time T+t1, the roadside unit 80 determines the first obstacle parameter OP1[T+t1][n] at least one more time, thereby increasing the confidence index CONF1+CONF2 by the number of times ΣCONF the roadside unit 80 determined the first obstacle parameter of the pedestrian 70. Therefore, the value of the confidence index already exceeds a predetermined threshold TH_CONF at time T+t1. As a result, the collision margin can already be calculated with high certainty at time T+t1 based on the third obstacle parameter OP3[T+t1][n].

[0258] Figure 30 is a schematic diagram showing a control device 1d according to another example of the disclosed subject matter. The control device of Figure 30 differs from the control device shown in Figure 1 in that the enable unit 3105 and activation unit 3105 enable / activate adaptive cruise control (ACC), and therefore the control device 1d further includes a camera recognition unit 3107 and a map information storage 3108. However, these units may be provided outside the control device 1d and inside or outside the vehicle V.

[0259] Figure 31 is a flowchart illustrating an example of a control process performed by the control device 1d shown in Figure 30. In particular, the ACC control enable / activation by the control device in Figure 30 is explained in Figure 31. After the process starts, in step S3200, the enable unit of ACC control 3105 may receive lane information from the camera recognition unit 3107. Alternatively or additionally, the ACC control enable unit 3105 may determine the lane information from the map information provided to the map information storage 3108. Next, in step S3201, ACC_Target_ID is set to 0, which means that the vehicle is following behind the vehicle in front. In the following step S3202, the target distance to the vehicle in front, ACC_Target_Distance, is set to 512.

[0260] In the subsequent calculation loop, the ACC control enable unit 3105 of the control device 1d shown in FIG. 30 checks whether the reliability flag TGFLG = 1 is set for each of the plurality of first obstacle parameters m = 1, ···, N, and whether the preceding vehicle providing the plurality of first obstacle parameters is in the same lane as the host vehicle. If so, in step S3203, the distance to the preceding vehicle is calculated by the ACC control enable unit 3105, and it is verified whether the calculated distance is greater than the target distance ACC_TARGET_DISTANCE. In the case of a positive result, the value of ACC_Target_ID is set to a constant speed m that can be set by the driver, and the target distance ACC_Target_Distance is set to the distance calculated in step S3203.

[0261] If any of the above-mentioned checks is negative, the process ends this calculation loop and further proceeds to the second calculation loop, where ACC_Target_ID and the target distance are calculated based on the plurality of third obstacle parameters.

[0262] According to the first calculation loop, in the second calculation loop, it is first checked whether the reliability flag TGFLG = 1 is set for each of the plurality of third obstacle parameters q = 1, ···, Q, and whether the vehicle ahead providing the plurality of third obstacle parameters is in the same lane as the host vehicle. If so, in step S3206, the distance to the vehicle ahead is calculated by the ACC control enable unit 3105, and it is verified whether the calculated distance is greater than the target distance ACC_TARGET_DISTANCE. In the case of a positive result, the value of ACC_Target_ID is set to a constant speed q that can be set by the driver, and the target distance ACC_Target_Distance is set to the distance calculated in step S3206.

[0263] If any of the above checks is negative, the process ends the second calculation loop, returns to step S3200, and repeats the process until a distance greater than the target distance ACC_Target_Distance is calculated by the ACC control enable unit 3105.

[0264] FIGS. 32a and 32b schematically show an example of driving assistance executed using a control device other than that illustrated in FIG. 30, compared with an example of driving assistance executed using the control device 1d illustrated in FIG. 30.

[0265] Specifically, FIG. 32a shows ACC control based only on a plurality of first obstacle parameters provided by a vehicle 75c traveling in a lane 90 in front of a vehicle 75a (host vehicle) executing ACC control. In front of the vehicle 75c, a low-speed vehicle 75d is traveling and is not visible to the ACC control of the host vehicle 75a.

[0266] At time T, the plurality of first obstacle parameters OP1[T][1] received by the host vehicle 75a include the position PX11, PY1 and the speeds VX11, VY11 from the vehicle 75c in front. At this time, the reliability index CONF11 of the first obstacle parameter depends only on the parameters currently received at time T.

[0267] At time T + t1, the vehicle 75c overtakes the low-speed vehicle 75d, whereby the low-speed vehicle becomes the vehicle providing a plurality of first obstacle parameters OP1[T][2] at this point. Since the low-speed vehicle 75d is still not visible to the host vehicle, the first obstacle parameter OP1[T][2] at time T + t1 does not include the speed of the low-speed vehicle 75d. Therefore, the reliability index CONF12 can only depend on the current first obstacle parameter OP1[T][2] that does not include information on the speed of the vehicle 75d in front of the host vehicle 75a at the current time, which may lead to a lower reliability index CONF12 at time T + t1.

[0268] Due to the lack of speed information, the host vehicle 75a may not be able to maintain the target distance d2 from the slower vehicle 75d ahead, and may need to maintain at least a short distance d1 from the slower vehicle 75d ahead and apply strong brakes at time T+t2 to avoid a collision.

[0269] Conversely, Figure 32b illustrates ACC control based on multiple first and second obstacle parameters, where the host vehicle V further receives a first obstacle parameter OP1[T][1] at time T from a vehicle 75c traveling in the lane 90 ahead. The second obstacle parameter OP2[T][1] at that time is provided by a slow vehicle 75d traveling in the lane 90 ahead of vehicle 75c. Thus, at time T, the host vehicle recognizes the positions PX11, PY11, PX21, PY21 and speeds VX11, VY11, VX21, VY21 from both vehicles 75c and 75d ahead.

[0270] When vehicle 75c overtakes slow vehicle 75d at time T+t1, the ACC control of the host vehicle V can calculate a third obstacle parameter OP3[T][1] based on the current position PX12,PY12 of slow vehicle 75d provided in its first obstacle parameter, and the speed VX21,VY21 of the slow vehicle, which was already determined at time T and provided as the second obstacle parameter.

[0271] This allows the confidence index at time T+t1 to depend on the confidence CONF12+CONF21 of the first and second obstacle parameters. With speed information at time T+t1, the host vehicle can maintain a target distance d2 to the slow vehicle at time T+t2 without requiring emergency braking, thereby improving driving comfort when using ACC control.

[0272] In summary, methods, devices, and / or computer program products may be provided that enhance the driving comfort of drivers of vehicles using this method / device or computer program product, particularly by reducing or avoiding sudden interventions from driver assistance systems.

[0273] Furthermore, it should be noted that embodiments of this disclosure may take the form of hardware embodiments as a whole, software embodiments as a whole (including firmware, resident software, microcode, etc.), or embodiments that combine software and hardware embodiments. Furthermore, embodiments of this disclosure may take the form of computer program products on computer-readable media having computer executable program code embodied in the medium.

[0274] Note that arrows may be used in drawings to represent communication, transfer, or other activities involving two or more entities. While bidirectional arrows generally indicate that an activity can occur in both directions (e.g., a command / request in one direction and a corresponding response in the other, or peer-to-peer communication initiated by either entity), in some situations, the activity does not necessarily have to occur in both directions.

[0275] While unidirectional arrows generally indicate only one-way or primarily one-way activity, it should be noted that in certain situations, such directional activity may include bidirectional activity (e.g., a message from source to destination, a notification of receipt from the destination to the source, or the establishment of a connection before forwarding and the termination of a connection after forwarding). Therefore, the type of arrow used in a particular drawing to represent a specific activity is illustrative and should not be considered limiting.

[0276] Examples of methods and apparatus are described above with reference to flowcharts and / or block diagrams. Each block in the flowchart or block diagram, or both, and any combination of blocks in the flowchart or block diagram, or both, will be understood to be implementable by computer executable program code.

[0277] The computer-executable program code described above may be provided to a general-purpose computer, a dedicated computer, or a processor of another programmable data processing device to manufacture a particular machine, so as to create means for the program code, which is executed via the processor of a computer or other programmable data processing device, to perform functions / operations / outputs explicitly shown in a flowchart, block diagram, block, figure, and / or described.

[0278] These computer-executable program codes may also be stored in computer-readable memory, and the program codes stored in computer-readable memory can be used in a particular manner to cause a computer or other programmable data processing device to function in a particular way to produce a product that includes instruction means to perform functions / operations / outputs explicitly shown in flowcharts, blocks of block diagrams, figures, and / or described descriptions.

[0279] The computer-executable program code described above may be further loaded into a computer or other programmable data processing device so that a set of operational steps is executed on the computer or other programmable device to create a computer-executed process, so that the program code executed on the computer or other programmable device provides steps for performing functions / operations / outputs explicitly shown in flowcharts, blocks of block diagrams, figures, and / or described descriptions. Alternatively, the computer program execution steps or operations may be combined with steps or operations performed by an operator or human to perform the embodiment.

[0280] A communication network may generally include a public network and / or a private network, and may include a local area, wide area, metropolitan area, storage, and / or other types of networks, and may use communication technologies including, but not limited to, analog technology, digital technology, optical technology, wireless technology (e.g., Bluetooth (registered trademark)), networking technology, and internetworking technology.

[0281] Also, note that the device may use communication protocols and messages (e.g., messages created, sent, received, stored, and / or processed by the device), and such messages may be carried by a communication network or medium.

[0282] Unless contextually required, the present disclosure should not be construed as limited to any particular communication message type, communication message format, or communication protocol. Thus, communication messages may generally include, without limitation, frames, packets, datagrams, user datagrams, cells, or other types of communication messages.

[0283] Unless contextually required, references to particular communication protocols are exemplary, and it should be understood that alternative embodiments may use variations of such communication protocols (e.g., modifications or extensions of the protocols that may be created from time to time) or any other protocol known or created in the future.

[0284] Also, note that when a logic flow is described herein for performing logic in various manners, the present disclosure should not be construed as limited to a particular logic flow or implementation of the logic. The described logic may be divided into different logic blocks (e.g., programs, modules, functions, or subroutines) without changing the overall result of the present disclosure.

[0285] In many cases, logic elements may be added, modified, deleted, or executed in different orders, or implemented using different logic structures (e.g., logic gates, looping primitives, conditional logic, and other logic structures) without altering the overall outcome of this disclosure.

[0286] This disclosure can be embodied in many different forms, including, but not limited to, computer program logic used with a processor (e.g., a microprocessor, microcontroller, digital signal processor, or general-purpose computer), programmable logic used with a programmable logic device (e.g., a field-programmable gate array (FPGA) or other PLD), individual components, integrated circuits (e.g., application-specific integrated circuits (ASICs)), or any other means including any combination thereof. Computer program logic that performs some or all of the functions described above is typically implemented as a set of computer program instructions that are themselves stored in a computer-readable medium and translated into a computer-executable form that is executed by a microprocessor under the control of an operating system. Hardware-based logic that performs some or all of the functions described above may be implemented using one or more appropriately configured FPGAs.

[0287] Computer program logic that performs all or part of the functions described above may be embodied in a variety of forms, including, but not limited to, source code form, computer executable form, and various intermediate forms (e.g., forms generated by an assembler, compiler, linker, or locator).

[0288] Source code may include a set of computer program instructions implemented in one of several programming languages ​​(e.g., object code, assembly language, or high-level languages ​​such as Fortran, C, C++, Java®, or HTML) used with various operating systems or operating environments. Source code may define and use various data structures and communication messages. Source code may have a computer executable form (e.g., by an interpreter) or can be converted into a computer executable form (e.g., by a translator, assembler, or compiler).

[0289] Computer executable program code for performing the operations of the embodiments of this disclosure may be written in an object-oriented, scripting, or non-scripting programming language such as Java, Perl, Smalltalk, or C++. However, computer executable program code for performing the operations of the embodiments may also be written in a conventional procedural programming language such as the C programming language or a similar programming language.

[0290] Computer program logic that performs all or part of the functions described above may run on a single processor (e.g., simultaneously) at different times, or on multiple processors at the same or different times, and may run under a single operating system process / thread or under different operating system processes / threads.

[0291] Therefore, the term “computer process” can generally refer to the execution of a set of computer program instructions, regardless of whether different computer processes run on the same or different processors, and regardless of whether different computer processes run under the same operating system process / thread or under a different operating system process / thread.

[0292] Computer programs can be fixed in any form (e.g., source code, computer executable, or intermediate form) permanently or temporarily on tangible storage media such as semiconductor memory devices (e.g., RAM, ROM, PROM, EEPROM, or flash programmable RAM), magnetic memory devices (e.g., diskettes or fixed disks), optical memory devices (e.g., CD-ROMs), PC cards (e.g., PCMCIA cards), or other memory devices.

[0293] Computer programs can be fixed in any form in signals that can be transmitted to a computer using any of a variety of communication technologies, including but not limited to analog, digital, optical, wireless (e.g., Bluetooth), networking, and internetworking technologies.

[0294] Computer programs can be distributed in any form as removable storage media (e.g., shrink-wrapped software) with printed or electronic documents attached, pre-loaded onto a computer system (e.g., on system ROM or on a fixed disk), or distributed from a server or electronic bulletin board via a communication system (e.g., the Internet or the World Wide Web).

[0295] Hardware logic (including programmable logic used with programmable logic devices) that performs all or part of the functions described herein may be designed using conventional manual methods, or it may be designed, captured, simulated, or documented electronically using various tools such as computer-aided design (CAD), hardware description languages ​​(e.g., VHDL or AHDL), or PLD programming languages ​​(e.g., PALASM, ABEL, or CUPL).

[0296] Any suitable computer-readable medium may be used. This computer-readable medium may, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, devices, or media.

[0297] More specific examples of computer-readable media include, but are not limited to, one or more wirings or electrical connections having other tangible storage media such as portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), compact disc read-only memory (CD-ROM), or other optical or magnetic storage devices.

[0298] Programmable logic can be permanently or temporarily fixed to a tangible storage medium such as a semiconductor memory device (e.g., RAM, ROM, PROM, EEPROM, or flash programmable RAM), a magnetic memory device (e.g., a diskette or fixed disk), an optical memory device (e.g., a CD-ROM), or other memory device.

[0299] Programmable logic can be fixed with signals that can be transmitted to a computer using any of a variety of communication technologies, including but not limited to analog, digital, optical, wireless (e.g., Bluetooth), networking, and internetworking technologies.

[0300] Programmable logic can be distributed as removable storage media (e.g., shrink-wrapped software) with printed or electronic documents attached, pre-loaded into a computer system (e.g., on system ROM or a fixed disk), or distributed from a server or electronic bulletin board via a communication system (e.g., the Internet or the World Wide Web). Naturally, some embodiments can be implemented as a combination of both software (e.g., a computer program product) and hardware. Still other embodiments can be implemented entirely as hardware or entirely as software.

[0301] While certain exemplary embodiments have been described and illustrated in the accompanying drawings, it should be understood that such embodiments are illustrative and that various other changes, combinations, deletions, modifications, and substitutions are possible in addition to those described in the preceding section. Therefore, embodiments are not limited to the specific structures and configurations illustrated and described.

[0302] Those skilled in the art will understand that various adaptations, modifications, and / or combinations of the embodiments described above are configurable. Therefore, it should be understood that within the scope of the appended claims, this disclosure may be practiced in ways other than those specifically described herein. For example, unless otherwise noted, the steps of the processes described herein may be performed in a different order than those described herein, and one or more steps may be combined, separated, or performed concurrently. Those skilled in the art will further understand, in view of this disclosure, that different embodiments or aspects described herein may be combined to form other embodiments. [Explanation of symbols]

[0303] 100 Onboard sensors for obstacle detection 101 First Obstacle Parameter Acquisition Unit 102 External Sensors 103 Second Obstacle Parameter Acquisition Unit 104 Obstacle Parameter Calculation Unit 105 Enable Unit 106 Activation Unit 800 Map Information Storage 801 Signal Strength Acquisition Unit 802 Specification Acquisition Unit 3107 Camera Recognition Unit 3108 Map Information Storage

Claims

1. A control device for controlling a vehicle's driver assistance system, - A first obstacle parameter acquisition unit configured to receive a plurality of first obstacle parameters of an obstacle in the area surrounding the vehicle detected by a first measuring device, wherein the plurality of first obstacle parameters include one or more parameters of a first category and one or more parameters of a second category, - A second obstacle parameter acquisition unit configured to receive a plurality of second obstacle parameters of an obstacle in the area surrounding the vehicle detected by a second measuring device, wherein the plurality of second obstacle parameters include one or more parameters of the first category and one or more parameters of the second category, - An obstacle parameter calculation unit configured to receive the plurality of first and second obstacle parameters from the first and second obstacle parameter acquisition units, and to calculate a plurality of third obstacle parameters of the detected obstacle, including one or more parameters of the first category and one or more parameters of the second category, based on the plurality of first and second obstacle parameters, The obstacle parameter calculation unit is configured to calculate one or more parameters of the first category based on the plurality of first obstacle parameters, and one or more parameters of the second category based on the plurality of second obstacle parameters, - Based on the plurality of third obstacle parameters, calculate the first determination parameter, An enable unit configured to enable driving assistance when the first determination parameter is lower than a predetermined activation threshold, A control device equipped with the following features.

2. The control device according to claim 1, wherein the obstacle parameter calculation unit is configured to determine whether the first and second measuring devices have detected the same obstacle based on the comparison result of at least one of the plurality of first and second obstacle parameters, and calculates the plurality of third obstacle parameters only if the determination is positive.

3. The first measuring device is configured to communicate with the control device at a higher speed than the second measuring device, but to detect the obstacle after the second measuring device. The second measuring device is configured to detect the obstacle before the first measuring device, but to communicate with the control device at a slower speed than the first measuring device. A control device according to at least one of claims 1 to 2.

4. The control device according to at least one of claims 1 to 3, wherein the parameters of the first category are position parameters of the obstacle, including static information relating to the obstacle, and the parameters of the second category are movement parameters of the obstacle, including dynamic information relating to the obstacle.

5. The control device according to at least one of claims 1 to 4, wherein the obstacle parameter calculation unit includes a predictive model for calculating the plurality of third obstacle parameters, the predictive model is configured to calculate the plurality of third obstacle parameters when the obstacle is first detected, using one or more parameters of the second category from the plurality of second obstacle parameters as one or more initial parameters.

6. The obstacle parameter calculation unit is configured to calculate a reliability index representing the reliability of the plurality of third obstacle parameters and to transmit the calculated reliability index, along with the third obstacle parameters, to the enable unit. The enable unit is configured to enable the driving assistance when the first determination parameter is lower than the predetermined activation threshold and the value of the reliability index is higher than the first predetermined reliability threshold. A control device according to at least one of claims 1 to 5.

7. The enable unit receives the plurality of first obstacle parameters from the first obstacle parameter acquisition unit. Based on the plurality of first obstacle parameters, a second determination parameter is calculated. If the first and / or second determination parameters are lower than the predetermined activation threshold, the driving assistance is enabled. A control device according to at least one of claims 1 to 6, configured as described above.

8. The enable unit receives the plurality of first obstacle parameters from the first obstacle parameter acquisition unit. Based on the plurality of first obstacle parameters, a second determination parameter is calculated. The system is configured to enable the driving assistance if the first and / or second determination parameters are lower than the predetermined activation threshold. The control device according to claim 6, wherein the obstacle parameter calculation unit is configured to increase the value of the reliability index based on the number of times the obstacle has been detected by the first measuring device.

9. The control device according to claim 8, wherein the obstacle parameter calculation unit is configured to calculate the reliability index taking into account the specifications of the plurality of second obstacle parameters.

10. The specification of the plurality of second obstacle parameters includes a plurality of specification parameters, The obstacle parameter calculation unit is configured to adjust the value of the reliability index based on the value of each specification parameter. The control device according to claim 9.

11. The control device according to at least one of claims 8 to 10, wherein the obstacle parameter calculation unit is configured to receive a plurality of map information of an area surrounding the vehicle and to adjust the value of the reliability index based on the plurality of map information.

12. The obstacle parameter calculation unit is configured to receive multiple map information of the area surrounding the vehicle and to adjust the value of the reliability index based on the multiple map information. The obstacle parameter calculation unit receives the plurality of second obstacle parameters detected by one or more second measuring devices, Based on at least one of the plurality of specification parameters and at least one of the plurality of map information, the plurality of second obstacle parameters received from the one or more second measuring devices are selected. Whether an obstacle detected by one second measuring device is the same as an obstacle detected by another second measuring device is determined based on at least one of the plurality of second obstacle parameters of the one second measuring device and the other second measuring device. If the above determination is correct, The plurality of second obstacle parameters are received from at least one of the second measuring devices. If the above determination is negative, The system is configured to receive the plurality of second obstacle parameters detected by one of the second measuring devices that detects an obstacle identical to the obstacle detected by the first measuring device. The control device according to claim 10.

13. The obstacle parameter calculation unit increases the value of the confidence index if the obstacle detected by one second measuring device and the other second measuring device are the same. The system is configured to reduce the value of the confidence index if the obstacles detected by one second measuring device and the other second measuring device are different. The control device according to claim 12.

14. The obstacle parameter calculation unit receives the field of view of one second measuring device and the other second measuring device, The control device according to claim 12 or 13, configured to reduce the value of the confidence index if the field of view of one second measuring device overlaps with the field of view of the other second measuring device.

15. The enable unit calculates a warning determination parameter based on the plurality of third obstacle parameters, If the calculated warning determination parameter is lower than a predetermined warning threshold, the warning is enabled as a driver assistance feature. A warning enable unit configured as follows, Based on the plurality of third obstacle parameters, the intervention decision parameters are calculated. An intervention enable unit is configured to enable an intervention as driving assistance when the calculated intervention determination parameter is lower than a predetermined intervention threshold, A control device according to at least one of claims 1 to 14, comprising:

16. A method for controlling a driver assistance system for a vehicle, - A step of determining a plurality of first obstacle parameters of a detected obstacle, wherein the obstacle is detected by a first measuring device, and the plurality of first obstacle parameters include one or more parameters of a first category and one or more parameters of a second category. - A step of determining a plurality of second obstacle parameters of the detected obstacle, wherein the obstacle is detected by a second measuring device, and the plurality of second obstacle parameters include one or more parameters of the first category and one or more parameters of the second category. - The steps of receiving the plurality of first and second obstacle parameters by the obstacle parameter calculation unit, - A step of calculating a plurality of third obstacle parameters of the detected obstacle using the obstacle parameter calculation unit, wherein the plurality of third obstacle parameters include one or more parameters of a first category and one or more parameters of a second category based on the plurality of first and second obstacle parameters, the one or more parameters of the first category are calculated based on the plurality of first obstacle parameters, and the one or more parameters of the second category are calculated based on the plurality of second obstacle parameters. - A step of calculating a determination parameter based on the plurality of third obstacle parameters, - If the determination parameter is lower than a predetermined threshold, the enable unit enables the driving assistance. A method that includes this.

17. A computer program product that, when executed by a computer, includes instructions that cause the computer to perform the method according to claim 16, and which can be stored in memory.

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