Method, device and equipment for providing auxiliary driving signal under abnormal environment and storage medium

Abstract

The application discloses a method, device and equipment for providing an auxiliary driving signal in an abnormal environment and a storage medium. The method comprises the following steps: collecting positioning information of each vehicle, determining vehicle direction information of each vehicle according to the positioning information; determining a cloud vehicle system of all vehicles in the same direction according to the vehicle direction information; extracting vehicle running information of all vehicles in the same direction by using the cloud vehicle system of all vehicles in the same direction, and obtaining driving information of front and rear vehicles of an auxiliary driving vehicle in the same lane; calculating and generating simulated radar information based on the driving information of the front and rear vehicles; comparing the simulated radar information with radar information to determine whether the radar information is abnormal; and when the radar information is abnormal, writing the simulated radar information into a corresponding storage address through the cloud vehicle system of the auxiliary driving vehicle. In the case that the sensor collected data is not accurate in the case of bad weather, the auxiliary driving function can still be realized, and the driving safety of the vehicle in the bad environment is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of electronic information technology, and in particular to a method, apparatus, device and storage medium for providing assisted driving signals under abnormal conditions. Background Technology

[0002] With societal progress, automobiles have become an essential mode of transportation for almost every household, making people's lives increasingly convenient. Furthermore, with the development of various advanced technologies in the automotive field, increasingly intelligent devices and functions are being integrated into cars. Advanced Driving Assistance Systems (ADAS) utilize various sensors installed in the vehicle (millimeter-wave radar, lidar, monocular / dual-lens cameras, and satellite navigation) to continuously sense the surrounding environment while the car is in motion, collect data, identify, detect, and track static and dynamic objects, and combine this data with navigation map data for system calculation and analysis. This allows the driver to anticipate potential dangers, effectively increasing driving comfort and safety.

[0003] In the process of developing this invention, the inventors discovered the following technical problem: Existing driver assistance systems rely on various onboard sensors to sense the environment, acquire information, and then perform analysis and calculations to provide corresponding prompts for safe driving. However, under some severe natural conditions, such as short-term adverse weather conditions like thunderstorms, strong winds, and hail, or in poor lighting conditions, the data collected by the sensors is affected by external factors, resulting in inaccurate data acquisition and impaired visibility. In such situations, the driver assistance function cannot be achieved, and the driver's visibility is also affected, impacting vehicle driving safety. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for providing assisted driving signals under abnormal conditions, in order to solve the technical problem in the prior art where the driving assistance system cannot obtain accurate data under abnormal conditions, thus failing to realize the assisted driving function.

[0005] In a first aspect, embodiments of the present invention provide a method for providing assisted driving signals under abnormal conditions, including:

[0006] Collect the location information of each vehicle, and determine the vehicle's direction information based on the location information;

[0007] The cloud-based vehicle system identifies all vehicles traveling in the same direction based on the vehicle direction information.

[0008] The vehicle operation information of all vehicles traveling in the same direction is extracted using the cloud vehicle system, and the driving information of the vehicles in front and behind the assisted driving vehicle in the same lane is obtained.

[0009] Simulated radar information is generated based on the driving information of vehicles in front and behind.

[0010] The simulated radar information is compared with the radar information extracted from the ECU of the driver assistance vehicle to determine whether the radar information is abnormal.

[0011] In case of an anomaly, the simulated radar information is written into the storage address corresponding to the ECU radar information of the assisted driving vehicle through the cloud vehicle system of the assisted driving vehicle.

[0012] Secondly, embodiments of the present invention also provide a device for providing assisted driving signals under abnormal conditions, comprising:

[0013] The data acquisition module is used to collect the location information of each vehicle and determine the vehicle's orientation information based on the location information.

[0014] The determination module is used to determine the cloud vehicle system of all vehicles traveling in the same direction based on the vehicle direction information;

[0015] The extraction module is used to extract the vehicle operation information of all vehicles in the same direction using the cloud vehicle system of all vehicles in the same direction, and to obtain the driving information of the vehicles in front and behind the assisted driving vehicle in the same lane.

[0016] The calculation module is used to calculate and generate simulated radar information based on the driving information of the vehicles in front and behind;

[0017] The judgment module is used to compare the simulated radar information with the radar information extracted from the ECU of the assisted driving vehicle to determine whether the radar information is abnormal.

[0018] The writing module is used to write the simulated radar information into the storage address corresponding to the ECU radar information of the assisted driving vehicle through the cloud vehicle system in the event of an anomaly.

[0019] Thirdly, embodiments of the present invention also provide an apparatus, comprising:

[0020] One or more processors;

[0021] Storage device for storing one or more programs;

[0022] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for providing assisted driving signals in abnormal environments as provided in the above embodiments.

[0023] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for providing assisted driving signals under abnormal conditions as provided in the above embodiments.

[0024] The present invention provides a method, apparatus, device, and storage medium for providing assisted driving signals under abnormal conditions. This involves collecting the positioning information of each vehicle and determining the vehicle's direction information based on the positioning information; determining the cloud-based vehicle system for all vehicles traveling in the same direction based on the direction information; extracting vehicle operation information from the cloud-based vehicle system of all vehicles traveling in the same direction, and obtaining the driving information of vehicles in front and behind the assisted driving vehicle in the same lane; calculating and generating simulated radar information based on the driving information of the vehicles in front and behind; comparing the simulated radar information with radar information extracted from the ECU of the assisted driving vehicle to determine whether the radar information is abnormal; and, if abnormal, writing the simulated radar information into the storage address corresponding to the radar information in the ECU of the assisted driving vehicle through the cloud-based vehicle system of the assisted driving vehicle. This effectively utilizes the fast communication capability of the cloud-based vehicle system to quickly and accurately acquire vehicle operation information and generate simulated radar information based on the vehicle operation information. Furthermore, it allows the use of simulated radar information to replace invalid radar information caused by severe weather conditions, enabling assisted driving functions to still be achieved even when sensor data collection is inaccurate in severe weather conditions. This improves vehicle driving safety under adverse environmental conditions. Attached Figure Description

[0025] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0026] Figure 1 A flowchart of a method for providing assisted driving signals under abnormal conditions according to Embodiment 1 of the present invention;

[0027] Figure 2 This is a flowchart of a method for providing assisted driving signals under abnormal conditions, as provided in Embodiment 2 of the present invention;

[0028] Figure 3 This is a flowchart of the method for providing assisted driving signals under abnormal conditions provided in Embodiment 3 of the present invention;

[0029] Figure 4 This is a structural diagram of the device for providing assisted driving signals under abnormal conditions provided in Embodiment 5 of the present invention;

[0030] Figure 5 This is a structural diagram of the device provided in Embodiment 5 of the present invention. Detailed Implementation

[0031] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a method for providing assisted driving signals under abnormal conditions according to Embodiment 1 of the present invention. This embodiment is applicable to situations where various sensors of the vehicle cannot obtain real and effective signals under abnormal weather or lighting conditions, thus failing to provide assisted driving signals. This method can be executed by a device for providing assisted driving signals under abnormal conditions and is correspondingly set in a communication base station that can establish a network connection with all vehicles in the area. Specifically, it includes the following steps:

[0034] Step 110: Collect the location information of each vehicle and determine the vehicle direction information of each vehicle based on the location information.

[0035] In this embodiment, the vehicle-mounted system can be a cloud-based computer system. The cloud computer is a comprehensive service solution, including cloud resources, transmission protocols, and cloud terminals. Using an open cloud terminal and transmission protocols, resources such as desktops, applications, and hardware are provided to users in an on-demand, flexibly allocated service model. The cloud-based computer system can provide users with a wide range of multimedia services, application services, and navigation services.

[0036] In addition to a vehicle infotainment system equipped with Android or other desktop operating systems, vehicles also have an ECU (Electronic Control Unit). ECUs typically employ an embedded design. This embedded ECU system is used to store vehicle information and control the vehicle.

[0037] Optionally, in this embodiment, the base station can utilize its communication capabilities within a certain area to establish network connections with all vehicles passing through that area and obtain the location information of all connectable vehicles. This location information can be provided by a positioning system installed in the vehicle, such as BeiDou. After obtaining the location information of all connected vehicles, the current road and corresponding direction of each connected vehicle can be determined based on the location coordinates provided by the positioning system and the direction obtained from the continuous location coordinates, combined with a high-definition navigation map within the base station's coverage area.

[0038] Step 120: Determine the cloud vehicle system for all vehicles traveling in the same direction based on the vehicle direction information.

[0039] In this embodiment, the direction and corresponding road of each connected vehicle can be determined by the aforementioned method. Based on this, all vehicles traveling in the same direction on the same road can be determined, and then the container number of the cloud vehicle system is used to determine all vehicles traveling in the same direction.

[0040] Step 130: Use the cloud vehicle system of all vehicles traveling in the same direction to extract the vehicle operation information of all vehicles traveling in the same direction, and obtain the vehicle operation information of the vehicles in front and behind the assisted driving vehicle in the same lane.

[0041] The cloud-based vehicle system extracts vehicle operation information from the ECU system for all vehicles traveling in the same direction, and uses this information to calculate the radar signals of adjacent vehicles.

[0042] In this embodiment, the assisted driving vehicle can be any one of all vehicles in the same lane, and it must be a vehicle using assisted driving functions. Once the assisted driving vehicle is identified, its specific location and corresponding cloud-based vehicle system can be clearly determined. Based on its specific location, adjacent vehicles can be identified, and then the vehicle's operating information can be obtained through the cloud-based vehicle systems of those adjacent vehicles.

[0043] The vehicle operation information may include: vehicle direction, speed, position, wheel rotation, and other driving-related information.

[0044] Step 140: Calculate and generate simulated radar information based on the vehicle operation information of the vehicles in front and behind.

[0045] Adaptive cruise control (ACC) is a commonly used feature in driver assistance systems. When the distance to the vehicle in front is too small, the ACC control unit, in coordination with the anti-lock braking system (ABS) and engine control system, can appropriately brake the wheels and reduce engine output power to maintain a safe distance. It primarily uses radar installed at the front of the vehicle to detect slow-moving vehicles on the road ahead. If a slow-moving vehicle is detected, the ACC system will reduce its speed and control the gap or time difference with the vehicle in front.

[0046] Therefore, the operating information of the vehicle in front of the assisted driving vehicle can be used as a reference. For example, by combining position and speed, the real-time accurate position can be calculated and updated. By using the real-time position of the vehicle in front and the real-time position of the assisted driving vehicle, the real-time distance and orientation of the vehicle in front relative to the vehicle radar can be determined. Based on the real-time distance and orientation, corresponding simulated radar information, i.e., simulated radar echo signal information, can be generated.

[0047] If the assisted driving vehicle is traveling too slowly, the distance between it and the following vehicle will be too close, thus affecting driving safety. Therefore, the above method can also be used to determine the real-time distance and position of the following vehicle relative to the rear radar, and generate simulated radar echo signal information for the rear radar.

[0048] Step 150: Compare the simulated radar information with the radar information extracted from the ECU of the assisted driving vehicle to determine whether the radar information is abnormal.

[0049] Assisted driving vehicles can perceive their environment through their own sensors and obtain sensor information, which may include radar information, image information, and information such as temperature and rainfall. This information is typically stored in a specific storage location within the ECU.

[0050] For example, radar information can be extracted from a specific storage location in the ECU of the assisted driving vehicle, and then compared with the simulated radar information calculated above. Under normal circumstances, the difference between the two should be small, and the difference should be within a certain range. If the difference is greater than a preset difference threshold, it may indicate that the radar information is abnormal.

[0051] Furthermore, other information collected by the assisted driving vehicle can be combined with the comparison results to determine whether the radar information is abnormal. For example, rain sensors and photoresistors can be used to determine whether there is abnormal weather. If the difference is greater than a set difference threshold in the case of an impending abnormality, then the radar information is determined to be abnormal.

[0052] Step 160: In case of an anomaly, the simulated radar information is written into the storage address corresponding to the radar information in the ECU of the assisted driving vehicle through the cloud vehicle system of the assisted driving vehicle.

[0053] In this embodiment, vehicle operation control is still handled by the local ECU. Therefore, it is necessary to replace erroneous radar information collected by radar sensors due to severe weather with correct simulated radar information. This requires writing the calculated simulated radar information into the ECU. Specifically, based on the fixed storage address of the radar information in the ECU's memory, the calculated simulated radar information can be written into the ECU system of the assisted driving vehicle using a cloud-based vehicle system. This allows the ECU to correctly control the vehicle based on the simulated radar information, reducing the probability of safety accidents caused by the inability to obtain accurate radar information in severe weather conditions.

[0054] This embodiment collects the positioning information of each vehicle and determines the vehicle's direction information based on the positioning information. It then determines the cloud-based vehicle system for all vehicles traveling in the same direction based on the direction information. Using the cloud-based vehicle system for all vehicles traveling in the same direction, it extracts the vehicle operation information of all vehicles in the same direction and obtains the driving information of the vehicles in front and behind the assisted driving vehicle in the same lane. Based on the driving information of the vehicles in front and behind, it calculates and generates simulated radar information. The simulated radar information is compared with the radar information extracted from the ECU of the assisted driving vehicle to determine if the radar information is abnormal. If abnormal, the simulated radar information is written to the storage address corresponding to the radar information in the ECU of the assisted driving vehicle through the cloud-based vehicle system. This effectively utilizes the fast communication capability of the cloud-based vehicle system to quickly and accurately acquire vehicle operation information and generate simulated radar information based on it. Furthermore, it can use simulated radar information to replace invalid radar information caused by severe weather conditions, enabling assisted driving functions to continue even when sensor data collection is inaccurate in severe weather conditions. This improves vehicle driving safety in adverse environmental conditions.

[0055] In a preferred embodiment of this example, the calculation of simulated radar information based on the driving information of vehicles ahead and behind can be optimized as follows: when multiple lanes exist, the presence of the same lane can be determined based on the collected images and the driving data of the vehicles ahead. If the vehicles are in the same lane, simulated radar information is calculated based on the driving information of vehicles ahead and behind. Since multiple lanes exist on the same road, the vehicles in front and behind the assisted driving vehicle cannot be determined solely by location information. Therefore, in this embodiment, information from other sensors in the vehicle can be used to determine the vehicles in front and behind, thereby improving the accuracy of the simulated radar information. For example, image data collected by cameras positioned at the front and rear of the assisted driving vehicle can be used to determine the vehicles in front and behind in the current lane, and then the corresponding simulated radar information can be calculated based on the vehicles in front and behind in the same lane. This can further improve the accuracy of the simulated radar information calculation.

[0056] In addition, the method can be further enhanced with the following steps: calculating the speed and position of the following vehicle, calculating the overtaking probability based on the speed and position of the following vehicle, and when an overtaking probability exists, adjusting the simulated vehicle radar echo signal to a preset overtaking radar echo signal when the distance between the two vehicles is less than a set distance threshold, and determining whether the vehicles are not in the same lane after overtaking through image analysis. Since overtaking is a special situation where the distance to the assisted driving vehicle is relatively close, it can easily affect the ACC adaptive cruise control driving experience. Therefore, in this embodiment, the vehicle operation information of the following vehicle is first extracted, and the possibility of overtaking is determined based on its speed and position. For example, the possibility of overtaking can be determined by the speed difference between the following vehicle and the assisted driving vehicle, and the overtaking initiation action can be determined by extracting the wheel rotation information of the following vehicle. When the distance between the two vehicles is less than a set distance threshold, the simulated vehicle radar echo signal is adjusted to a preset overtaking radar echo signal. Furthermore, it can be determined through image analysis whether the vehicle returns to its original lane after overtaking; if it does not return, the overtaking radar echo signal is deleted. If the process returns to normal, the simulated radar echo signal will be recalculated according to the above procedure to further ensure accurate radar echo signals when overtaking in adverse conditions, thereby improving the safety of assisted driving.

[0057] Example 2

[0058] Figure 2 This is a flowchart illustrating the method for providing assisted driving signals under abnormal conditions according to Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiment. In this embodiment, the method may further include the following steps: establishing a cloud vehicle system for each vehicle, with an information extraction process pre-embedded in the image of the cloud vehicle system. The information extraction process is used to read values ​​from at least two specified memory addresses in the ECU system. Correspondingly, the extraction of vehicle operation information for all vehicles traveling in the same direction using the cloud vehicle system of all vehicles is specifically optimized as follows: using each information extraction process to read values ​​from at least two specified memory addresses in the ECU system of all vehicles traveling in the same direction; determining the sensor type based on the specified memory addresses; and obtaining the vehicle operation information for each vehicle traveling in the same direction based on the sensor type and the values.

[0059] Accordingly, the method for providing assisted driving signals under abnormal conditions provided in this embodiment specifically includes:

[0060] Step 210: Establish a cloud vehicle system for each vehicle. An information extraction process is pre-embedded in the image of the cloud vehicle system. The information extraction process is used to read values ​​from at least two specified memory addresses in the ECU system.

[0061] High speed is required for reading and writing data from the ECU. Therefore, in this embodiment, container technology can be used to implement a cloud-based vehicle system, thereby achieving the goal of quickly reading or writing data to the ECU. The base station can utilize its own computing resources to establish a cloud-based vehicle system for each vehicle using container technology, enabling effective management and information extraction for vehicles within its range. Furthermore, considering the high universality of vehicle systems, the same or similar mirror image can be used for all systems. An information extraction process can be pre-embedded in this mirror image.

[0062] Since the memory in the ECU is fixed, the content stored in each memory unit is also fixed to facilitate the operation of the embedded system program. Correspondingly, the data read by each sensor is stored in a specific memory unit. The storage address corresponding to each sensor can be clearly determined according to the program, and then the information extraction process can read the data from the specified memory unit address. This information extraction process can start with the init process, ensuring its continued existence and normal operation during the active period of the cloud-based vehicle system. This information extraction process can correspond to multiple addresses, achieving the goal of simultaneously extracting data from multiple or various sensors.

[0063] To facilitate the timely reading of the extracted sensor data, in this embodiment, a resource allocation process is pre-embedded in the image of the cloud vehicle system. The resource allocation process is used to allocate the memory resources of the cloud vehicle system, so as to facilitate the reading of the corresponding sensor data from the ECU system and the writing of analog radar information to the ECU system without affecting the normal operation of the cloud vehicle system.

[0064] Optionally, the memory can be divided into a first memory area, a second memory area, and a third memory area using the resource allocation process. The first memory area is used to support the operation of the cloud vehicle system; the second memory area is used to store analog sensor information according to the memory address of the ECU; and the third memory area is used to synchronize the real data collected by each sensor in the ECU.

[0065] The resource allocation process is used to partition the memory of the cloud-based vehicle system, forming at least one cloud-based vehicle system memory area, i.e., the first memory area, which supports the operation of the cloud-based vehicle system. The second memory area is used to store the simulated sensor information calculated above, such as simulated radar information and simulated image information. Furthermore, it can also be stored according to the ECU's memory addresses. That is, corresponding memory addresses are partitioned according to the ECU's memory. Using the same method facilitates direct writing to the ECU's memory addresses. In addition, a third memory area is provided for synchronizing the real data collected by various sensors in the ECU. The extracted sensor information can be stored in the third memory area, facilitating the reading of data from the third memory area and the calculation of simulated sensor data based on the aforementioned sensor information. Furthermore, the Memory Management Unit (MMU) technology can be used to implement memory space domain isolation techniques using bitmap methods, paging methods, segmentation methods, multi-level paging methods, segmented paging methods, and short-circuit segment tree methods. Physical memory can be partitioned, and physical page access management can be performed using specific registers in the MMU and methods such as specifying access permissions with bits.

[0066] Step 220: Collect the location information of each vehicle and determine the vehicle direction information of each vehicle based on the location information.

[0067] Step 230: Use each information extraction process to read values ​​from at least two specified memory addresses in the ECU systems of all vehicles traveling in the same direction.

[0068] In this embodiment, the information extraction process of the cloud vehicle system is used to read the corresponding data, and the specified memory address can be set by the information extraction process.

[0069] Step 240: Determine the sensor type based on the specified memory address.

[0070] Each memory address corresponds to a different sensor type; therefore, the corresponding sensor type can be determined based on the memory address.

[0071] Step 250: Obtain vehicle operation information for each vehicle traveling in the same direction based on the sensor type and value, and obtain vehicle operation information for the vehicles in front and behind the assisted driving vehicle in the same lane.

[0072] Based on the sensor type, the corresponding operating information of vehicles traveling in the same direction is obtained and the numerical values ​​are determined. This operating information can then be used to calculate simulated sensor information.

[0073] Step 260: Calculate and generate simulated radar information based on the vehicle operation information of the vehicles in front and behind.

[0074] Step 270: Compare the simulated radar information with the radar information extracted from the ECU of the driver assistance vehicle to determine whether the radar information is abnormal.

[0075] Step 280: In case of an anomaly, the simulated radar information is written into the storage address corresponding to the radar information in the ECU of the assisted driving vehicle through the cloud vehicle system of the assisted driving vehicle.

[0076] This embodiment adds the following steps: A cloud-based vehicle system is established for each vehicle. An information extraction process is pre-embedded in the image of the cloud-based vehicle system. This information extraction process reads values ​​from at least two specified memory addresses in the ECU system. Correspondingly, the extraction of vehicle operation information for all vehicles traveling in the same direction using the cloud-based vehicle system is optimized as follows: Each information extraction process reads values ​​from at least two specified memory addresses in the ECU system of all vehicles traveling in the same direction; the sensor type is determined based on the specified memory addresses; and the vehicle operation information for each vehicle traveling in the same direction is obtained based on the sensor type and the values. Container technology can be used to implement the cloud-based vehicle system, thereby achieving the goal of quickly reading data from or writing data to the ECU. This enables the rapid generation of simulated radar information in adverse weather conditions, realizing assisted driving functions.

[0077] Example 3

[0078] Figure 3 This is a flowchart illustrating the method for providing assisted driving signals under abnormal conditions according to Embodiment 3 of the present invention. This embodiment is an optimization based on the above embodiment. In this embodiment, the method may further include the following steps: The method further includes: acquiring a high-definition navigation map within a corresponding range; determining lane and road width information based on the high-definition navigation map information; extracting an image of the vehicle's front using the vehicle's cloud-based vehicle system; blurring the image of the vehicle's front according to the lane and road width information to generate initial simulated lane markings; extracting vehicle radar information; using the vehicle radar information to determine whether there are vehicles in the same lane; determining whether there is a deviation in the initial simulated lane markings based on the driving trajectory of vehicles in the same lane and the road surface turning direction in the map; and, if there is no deviation, performing lane keeping monitoring according to the initial simulated lane.

[0079] Accordingly, the method for providing assisted driving signals under abnormal conditions provided in this embodiment specifically includes:

[0080] Step 310: Determine the cloud vehicle system for all vehicles traveling in the same direction based on the vehicle direction information.

[0081] Step 320: Use the cloud vehicle system of all vehicles traveling in the same direction to extract the vehicle operation information of all vehicles traveling in the same direction, and obtain the vehicle operation information of the vehicles in front and behind the assisted driving vehicle in the same lane.

[0082] Step 330: Calculate and generate simulated radar information based on the vehicle operation information of the vehicles in front and behind.

[0083] Step 340: Compare the simulated radar information with the radar information extracted from the ECU of the assisted driving vehicle to determine whether the radar information is abnormal.

[0084] Step 350: In case of an anomaly, the simulated radar information is written into the storage address corresponding to the radar information in the ECU of the assisted driving vehicle through the cloud vehicle system of the assisted driving vehicle.

[0085] Step 360: Obtain a high-definition navigation map within the corresponding range, and determine the lane and road width information based on the high-definition navigation map information.

[0086] In this embodiment, a lane-keeping assist driving method can also be provided in abnormal environments, such as poor lighting conditions or when road markings have not yet been painted. First, the current road conditions of the assisted driving vehicle are obtained. In poor lighting conditions or when road markings have not yet been painted, the width of the current road and the number of lanes according to national standards need to be determined based on the corresponding high-definition navigation map.

[0087] Step 370: Extract the image in front of the vehicle using the cloud vehicle system of the driving vehicle, and perform fuzzy segmentation of the image in front of the vehicle according to the lane and road width information to generate initial simulated lane markings.

[0088] For example, the second memory area of ​​the cloud-based vehicle system can be used to extract the image information of the front of the vehicle from the memory unit corresponding to the image acquisition sensor through an information extraction process. Since the image of the front can include all lanes, it can be deformed according to the principle of equal division and the location of the camera. For example, the deformation can be performed by projection. Then, the lanes are simulated and divided according to the number of lanes to generate initial simulated lane markings.

[0089] Step 380: Extract vehicle radar information, use vehicle radar information to determine whether there are vehicles in the same lane, and determine whether the initial simulated lane markings are deviated based on the driving trajectory of vehicles in the same lane and the road surface turning on the map.

[0090] In this embodiment, the initial simulated lane markings can also be corrected by observing the driving trajectories of vehicles traveling on the same road. For example, information from onboard radar sensors can be extracted to determine whether there are vehicles in the same lane ahead or behind the current assisted driving vehicle. Specifically, the distance and orientation of the radar signal relative to the current vehicle can be determined using onboard radar sensor information. If the distance is greater than a preset distance and the orientation is less than a preset azimuth angle, then these vehicles can be identified as being in the same lane. Driving information of vehicles in the same lane, such as wheel rotation information, is extracted to determine if there is a possibility of lane changing. If there is no lane changing, a reference vehicle can be further identified. Based on the position of the reference vehicle, the corresponding lane range is determined, and the lane markings are corrected using this lane range. If vehicles in different lanes exist, their lanes can be determined based on their positions, and the boundaries of those lanes can be defined. The initial simulated lane markings are then corrected based on these boundaries.

[0091] Step 390: When there is no deviation, perform lane keeping monitoring according to the initial simulated lane.

[0092] If there is no deviation, lane keeping monitoring is performed according to the initial simulated lane, that is, lane guide lines are set according to the direction of the road, and lane keeping is monitored based on the lane guide lines.

[0093] This embodiment adds the following steps: First, acquire a high-definition navigation map within the corresponding range; second, determine lane and road width information based on the high-definition navigation map information; third, extract an image of the vehicle's front using the vehicle's cloud-based vehicle system, and perform fuzzy segmentation of the image according to the lane and road width information to generate initial simulated lane markings; fourth, extract vehicle radar information, use the vehicle radar information to determine if there are vehicles in the same lane, and determine if there is any deviation in the initial simulated lane markings based on the driving trajectory of vehicles in the same lane and the road surface turning on the map; fifth, if there is no deviation, perform lane keeping monitoring according to the initial simulated lane. This allows for the setting of simulated lanes using relevant vehicle driving information and road information, even in low-light conditions or when road markings have not yet been painted, thereby achieving lane keeping assistance and further improving road driving safety.

[0094] Example 4

[0095] Figure 4 This is a schematic diagram of the device for providing assisted driving signals under abnormal conditions according to Embodiment 4 of the present invention, as shown below. Figure 4 As shown, the device includes:

[0096] The acquisition module 410 is used to acquire the positioning information of each vehicle and determine the vehicle direction information of each vehicle based on the positioning information.

[0097] The determination module 420 is used to determine the cloud vehicle system of all vehicles traveling in the same direction based on the vehicle direction information;

[0098] The extraction module 430 is used to extract the vehicle operation information of all vehicles in the same direction using the cloud vehicle system of all vehicles in the same direction, and to obtain the driving information of the vehicles in front and behind the assisted driving vehicle in the same lane.

[0099] The calculation module 440 is used to calculate and generate simulated radar information based on the driving information of the vehicles in front and behind;

[0100] The judgment module 450 is used to compare the simulated radar information with the radar information extracted from the ECU of the assisted driving vehicle, and to determine whether the radar information is abnormal.

[0101] The writing module 460 is used to write the simulated radar information into the storage address corresponding to the ECU radar information of the assisted driving vehicle through the cloud vehicle system in the event of an anomaly.

[0102] This embodiment provides a device for providing assisted driving signals in abnormal environments. It collects the positioning information of each vehicle and determines the vehicle's direction information based on this information. It then determines the cloud-based vehicle system for all vehicles traveling in the same direction based on the direction information. Using the cloud-based vehicle system for all vehicles traveling in the same direction, it extracts the vehicle operation information of all vehicles and obtains the driving information of vehicles in front and behind the assisted driving vehicle in the same lane. Based on the driving information of the vehicles in front and behind, it calculates and generates simulated radar information. The simulated radar information is compared with radar information extracted from the ECU of the assisted driving vehicle to determine if the radar information is abnormal. If abnormal, the simulated radar information is written to the storage address corresponding to the radar information in the ECU of the assisted driving vehicle through the cloud-based vehicle system. This effectively utilizes the fast communication capabilities of the cloud-based vehicle system to quickly and accurately acquire vehicle operation information and generate simulated radar information based on this information. Furthermore, it can use simulated radar information to replace invalid radar information caused by severe weather conditions, enabling assisted driving functions to continue even when sensor data collection is inaccurate in severe weather. This improves vehicle driving safety in adverse environmental conditions.

[0103] Based on the above embodiments, the device further includes:

[0104] A module is established to establish a cloud vehicle system for each vehicle. An information extraction process is pre-embedded in the image of the cloud vehicle system. The information extraction process is used to read values ​​from at least two specified memory addresses in the ECU system.

[0105] Accordingly, the extraction module includes:

[0106] The reading unit is used to read values ​​from at least two specified memory addresses in the ECU systems of all vehicles traveling in the same direction using each information extraction process;

[0107] The determining unit is used to determine the sensor type based on the specified address in the memory;

[0108] The calculation unit is used to obtain vehicle operation information for each vehicle traveling in the same direction based on the sensor type and value.

[0109] Based on the above embodiments, a resource allocation process is pre-embedded in the image of the cloud vehicle system. The resource allocation process is used to allocate memory resources of the cloud vehicle system so as to facilitate reading the corresponding sensor data from the ECU system and writing analog radar information to the ECU system without affecting the normal operation of the cloud vehicle system.

[0110] Based on the above embodiments, the device further includes:

[0111] The partitioning module is used to divide the memory into a first memory area, a second memory area, and a third memory area using the resource allocation process. The first memory area is used to support the operation of the cloud vehicle system; the second memory area is used to store analog sensor information according to the memory address of the ECU; and the third memory area is used to synchronize the real data collected by each sensor in the ECU.

[0112] Based on the above embodiments, the device further includes:

[0113] The lane and road width information acquisition module is used to acquire a high-definition navigation map within the corresponding range and determine the lane and road width information based on the high-definition navigation map information;

[0114] The image extraction module is used to extract images of the front of the vehicle using the cloud vehicle system of the driving vehicle, and to perform fuzzy segmentation of the images of the front of the vehicle according to the lane and road width information to generate initial simulated lane markings.

[0115] The deviation determination module is used to extract vehicle radar information, use vehicle radar information to determine whether there are vehicles in the same lane, and determine whether the initial simulated lane markings have deviations based on the driving trajectory of vehicles in the same lane and the road surface turning in the map.

[0116] The lane-keeping module is used to monitor lane keeping in accordance with the initial simulated lane when there is no deviation.

[0117] Based on the above embodiments, the device further includes:

[0118] The elimination module is used to calculate the speed and position of the following vehicle, calculate the overtaking probability based on the speed and position of the following vehicle, and when there is an overtaking probability, if the distance between the two vehicles is less than a set distance threshold, adjust the simulated vehicle radar echo signal to a preset overtaking radar echo signal, and eliminate the overtaking radar echo signal when it is determined from the image that the overtaking vehicle is not in the same lane.

[0119] The device for providing assisted driving signals under abnormal conditions provided in the embodiments of the present invention can execute the method for providing assisted driving signals under abnormal conditions provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0120] Example 5

[0121] Figure 5 This is a schematic diagram of the structure of a device provided in Embodiment 5 of the present invention. Figure 5 A block diagram of an exemplary device 12 suitable for implementing embodiments of the present invention is shown. Figure 5 The device 12 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0122] like Figure 5 As shown, device 12 is represented as a general-purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0123] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0124] Device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by device 12, including volatile and non-volatile media, removable and non-removable media.

[0125] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache 32. Device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 5Not shown; usually referred to as a "hard drive"). Although Figure 5 As not shown, disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0126] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0127] Device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with device 12, and / or with any device that enables device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0128] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the method for providing assisted driving signals in abnormal environments provided in the embodiments of the present invention.

[0129] Example 6

[0130] Embodiment 6 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for providing assisted driving signals under abnormal conditions as described in any of the above embodiments.

[0131] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0132] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0133] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0134] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or device. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0135] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for providing assisted driving signals under abnormal conditions, characterized in that, include: Collect the location information of each vehicle, and determine the vehicle's direction information based on the location information; The cloud-based vehicle system identifies all vehicles traveling in the same direction based on the vehicle direction information. The vehicle operation information of all vehicles traveling in the same direction is extracted using the cloud vehicle system, and the vehicle operation information of the vehicles in front and behind the assisted driving vehicle in the same lane is obtained. Simulated radar information is generated based on the vehicle operation information of the vehicles in front and behind. The simulated radar information is compared with the radar information extracted from the ECU of the driver assistance vehicle to determine whether the radar information is abnormal. In case of an anomaly, the simulated radar information is written into the storage address corresponding to the radar information in the ECU of the assisted driving vehicle through the cloud vehicle system of the assisted driving vehicle; The speed and position of the following vehicle are calculated, and the overtaking probability is calculated based on the speed and position of the following vehicle. When there is an overtaking probability, and the distance between the two vehicles is less than a set distance threshold, the simulated vehicle radar echo signal is adjusted to a preset overtaking radar echo signal. When it is determined from the image that the overtaking vehicle is not in the same lane, the overtaking radar echo signal is eliminated. The process of generating simulated radar information based on the driving information of the vehicles in front and behind includes: When there are multiple lanes, the system can determine whether they are in the same lane based on the collected images and the driving data of the vehicles ahead. If they are in the same lane, the system can calculate and generate simulated radar information based on the driving information of the vehicles in front and behind.

2. The method according to claim 1, characterized in that, The method further includes: A cloud-based vehicle system is established for each vehicle. An information extraction process is pre-embedded in the image of the cloud-based vehicle system. The information extraction process is used to read values ​​from at least two specified memory addresses in the ECU system. Accordingly, the step of extracting vehicle operation information of all vehicles traveling in the same direction using the cloud vehicle system includes: Each information extraction process reads values ​​from at least two specified memory addresses in the ECU systems of all vehicles traveling in the same direction; The sensor type is determined based on the specified memory address; The vehicle operation information for each vehicle traveling in the same direction is obtained based on the sensor type and values.

3. The method according to claim 2, characterized in that, The cloud vehicle system image is pre-embedded with a resource allocation process, which is used to allocate memory resources of the cloud vehicle system so as to facilitate the reading of corresponding sensor data from the ECU system and the writing of analog radar information to the ECU system without affecting the normal operation of the cloud vehicle system.

4. The method according to claim 3, characterized in that, The method further includes: The memory is divided into a first memory area, a second memory area, and a third memory area using the resource allocation process. The first memory area is used to support the operation of the cloud vehicle system; the second memory area is used to store analog sensor information according to the memory address of the ECU; and the third memory area is used to synchronize the real data collected by each sensor in the ECU.

5. The method according to claim 1, characterized in that, The method further includes: Obtain a high-definition navigation map within the corresponding range, and determine lane and road width information based on the high-definition navigation map information; The cloud-based vehicle system of the driving vehicle is used to extract the image in front of the vehicle, and the image in front of the vehicle is blurred according to the lane and road width information to generate initial simulated lane markings. Extract vehicle radar information, use vehicle radar information to determine whether there are vehicles in the same lane, and determine whether the initial simulated lane markings have deviations based on the driving trajectory of vehicles in the same lane and the road surface turning in the map. When there is no deviation, lane keeping monitoring is performed according to the initial simulated lane.

6. A device for providing assisted driving signals under abnormal conditions, characterized in that, include: The data acquisition module is used to collect the location information of each vehicle and determine the vehicle's orientation information based on the location information. The determination module is used to determine the cloud vehicle system of all vehicles traveling in the same direction based on the vehicle direction information; The extraction module is used to extract the vehicle operation information of all vehicles in the same direction using the cloud vehicle system of all vehicles in the same direction, and to obtain the driving information of the vehicles in front and behind the assisted driving vehicle in the same lane. The calculation module is used to calculate and generate simulated radar information based on the driving information of the vehicles in front and behind; The judgment module is used to compare the simulated radar information with the radar information extracted from the ECU of the assisted driving vehicle to determine whether the radar information is abnormal. The writing module is used to write the simulated radar information into the storage address corresponding to the ECU radar information of the assisted driving vehicle through the cloud vehicle system in case of an anomaly. The elimination module is used to calculate the speed and position of the following vehicle, calculate the overtaking possibility based on the speed and position of the following vehicle, and when there is an overtaking possibility, if the distance between the two is less than a set distance threshold, adjust the simulated vehicle radar echo signal to a preset overtaking radar echo signal, and eliminate the overtaking radar echo signal when it is determined by the image that the overtaking vehicle is not in the same lane. The computing module includes: The judgment unit is used to determine whether the vehicles are in the same lane when there are multiple lanes, based on the collected images and the driving data of the vehicles in front. If they are in the same lane, it calculates and generates simulated radar information based on the driving information of the vehicles in front and behind.

7. A device, characterized in that, The device includes: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method of providing assisted driving signals in abnormal environments as described in any one of claims 1-5.

8. A storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the method of providing assisted driving signals in an abnormal environment as described in any one of claims 1-5.

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