Advanced driver assistance systems, methods, and vehicles that assist a driver

Abstract

An advanced driver assistance system, method and vehicle for assisting a driver of a vehicle is provided. The system includes a sensor unit, a processing unit and a display unit. The sensor unit is configured to sense an environment of the vehicle. The processing unit is configured to determine at least one feature of the environment based on the sensing output. The processing unit is configured to determine a risk zone for the feature for a current time by estimating a respective risk with respect to the feature at each of two or more virtual positions of the vehicle based on at least one parameter of the vehicle at the current time, and forming a risk zone based on the two or more risks. The display unit is configured to display the environment of the vehicle with the feature and the risk zone of the feature.

Description

Technical Field

[0001] The present invention relates to an advanced driver assistance system for assisting the driver of a vehicle, a vehicle including such an advanced driver assistance system, and a method for assisting the driver of the vehicle. Background Technology

[0002] This invention belongs to the field of advanced driver assistance systems for assisting drivers of vehicles. Such advanced driver assistance systems assist drivers by informing them of the risks present in the vehicle's environment when driving (in separate uses). For example, an advanced driver assistance system may include a display that highlights the location of obstacles (e.g., people) in the vehicle's environment, thereby informing the driver of the obstacle's presence and allowing the vehicle to adapt its driving to the obstacle. For example, the driver may adapt the vehicle's driving to the obstacle by reducing the vehicle's speed and / or changing the vehicle's direction of travel, thereby avoiding a collision with the obstacle.

[0003] Advanced driver assistance systems (ADAS) can estimate risks at a given time. To this end, ADAS typically include sensors for detecting the vehicle's environment at its current location. This enables the ADAS to assess the vehicle's environment and thus calculate an estimated risk of the environment at the vehicle's current location. For example, this estimated risk could be the risk of the vehicle colliding with an environmental obstacle at its current location. Methods for estimating the environmental risk of a vehicle at its current location (i.e., calculating the estimated risk) based on sensing results of the vehicle's environment at its current location are well known in the art and are not the focus of this invention.

[0004] The drawback of the aforementioned advanced driver assistance systems (ADAS) is that they do not consider human psychology, particularly how people perceive risks relevant to themselves in real-life situations. Therefore, the risk assessment information provided by ADAS may not be intuitively understood by the driver, increasing the driver's reaction time to mitigate the risk.

[0005] Therefore, the object of the present invention is to provide an improved advanced driver assistance system that overcomes the aforementioned disadvantages. Specifically, the object of the present invention is to provide an improved advanced driver assistance system for assisting the driver of a vehicle, which enables improved estimation of risks present in the current time environment for future driving of the vehicle. More specifically, the object of the present invention is to provide an advanced driver assistance system that improves assistance to the driver of a vehicle, particularly by adapting to human psychology (especially adapting to how a person perceives risks relevant to themselves in real life). Summary of the Invention

[0006] According to a first aspect of the present invention, an advanced driver assistance system is provided for assisting the driver of a vehicle. The system includes a sensor unit, a processing unit, and a display unit. The sensor unit is configured to sense the environment of the vehicle and provide a sensing output to the processing unit. The processing unit is configured to determine at least one feature of the environment based on the sensing output. The processing unit is configured to determine a risk zone of the feature for the current time using the following method:

[0007] - Based on at least one parameter of the vehicle at the current time, estimate a corresponding risk regarding the feature at each of two or more virtual locations of the vehicle, to estimate two or more risks for the two or more virtual locations; and

[0008] - The risk zone is formed based on the two or more risks mentioned above.

[0009] The display unit is configured to display the environment of the vehicle having the characteristics and the risk zone having the characteristics.

[0010] In other words, according to a first aspect of the invention, the processing unit is configured to estimate, for the current time and based on at least one parameter of the vehicle at the current time, a theoretical risk regarding features detected in the vehicle's environment, assuming that the vehicle is not at its actual location at the current time but at a virtual location. The virtual location corresponds to a hypothetical location of the vehicle that differs from its actual location, and is therefore a theoretical location. Specifically, the two or more virtual locations include or correspond to locations that are different from each other and different from the vehicle's actual location at the current time. The terms "sensing" and "detection" can be used synonymously.

[0011] The risk regarding a feature of the vehicle's virtual position is estimated based on at least one parameter of the vehicle at the current time, rather than on at least one parameter of the time when the vehicle's actual position equals its virtual position. That is, to estimate the risk regarding a feature at the current time based on said at least one parameter of the vehicle at the current time, the vehicle's virtual position is used instead of its actual position at the current time. The virtual position corresponds to the vehicle's theoretical position at the current time.

[0012] Therefore, according to a first aspect of the invention, the risk regarding the at least one feature can be estimated for the virtual position of the vehicle at the current time. That is, the risk regarding the at least one feature is estimated at the current time by assuming that the vehicle is in a virtual position rather than an actual position at the current time. Although the risk regarding the at least one feature is estimated at the current time using a virtual (hypothetical) position instead of an actual position, the risk estimation is based on the value of the at least one parameter of the vehicle present at the current time. According to the first aspect of the invention, the value of the at least one parameter of the vehicle that will exist at the time when the vehicle is actually in a virtual position (i.e., at the time when the vehicle's actual position equals its hypothetical virtual position) is not used to estimate the risk at the current time. Therefore, the advanced driver assistance system of the first aspect differs from another system that estimates the risk regarding at least one feature (of the vehicle's environment) at the current time based on the value of at least one parameter of the vehicle that will exist at the time when the vehicle is actually in a virtual position.

[0013] In other words, the advanced driver assistance system of the first aspect enables the estimation of risk regarding the at least one characteristic for a theoretical scenario where the vehicle is present at a virtual location at the current time. Therefore, the risk is estimated for the theoretical scenario using the at least one parameter of the vehicle at the current time. In contrast, the aforementioned other system is configured only to estimate the risk regarding the at least one characteristic for a future time (i.e., the time when the vehicle will actually be present at the virtual location). Therefore, the other system uses the at least one parameter of the vehicle at that future time to estimate the risk for the future time.

[0014] Therefore, if the vehicle's actual position equals its virtual position at the current time, the estimated risk about the feature estimated based on the vehicle's at least one parameter at the current time and its virtual position corresponds to the actual risk about the feature that should have been estimated based on the vehicle's at least one parameter at the current time. Thus, the corresponding risk is a theoretical risk, and the two or more risks are each theoretical risks.

[0015] By forming risk zones based on the two or more estimated risks and displaying the vehicle's environment with the aforementioned characteristics and the risk zones of those characteristics, a risk space (respectively risk areas) is visualized relative to the features. The risk zones can represent dangerous areas in which the vehicle should not be at any given time. The terms "zone," "space," and "area" are used synonymously. The risk zones of the features can represent the personal (i.e., near-body) space of the feature. Since everyone has their own personal space that they do not wish to be intruded upon, this allows the driver to visually identify the vehicle's current risk status from the display unit based on the displayed vehicle environment, the displayed features, and the displayed risk zones of the features. Therefore, by indicating the personal space of the feature to the driver in the form of risk zones, the driver not only receives information about the two or more estimated risks of the feature from the display of the feature's risk zones, but the driver also becomes aware of the feature's personal space, which, due to human psychology, is intuitively focused upon. In other words, the risk zones of the features inform the driver of areas in which the vehicle should not intrude.

[0016] The display unit of the system according to the first aspect differs from another system in which the risk of at least one characteristic of the vehicle's environment is displayed only for a future time. That is, according to the first aspect, the display unit is configured to display a risk zone for the at least one characteristic, wherein the risk zone is formed based on two or more risks, each of which is estimated for a corresponding virtual location based on the at least one parameter of the vehicle at the current time. In other words, according to the first aspect, the display unit can be configured to display the estimated risk of the two or more virtual locations for the current time.

[0017] Furthermore, displaying features associated with risk zones within the context of the environment allows drivers to better plan future driving. At the current time, drivers can perceive an estimated risk about a feature at a location different from the vehicle's actual location at the current time (a virtual location) based on the displayed risk zones. This enables drivers to visually assess the risk level of the vehicle within its environment at the current time.

[0018] In addition, the display features of the risk zone and its characteristics enable the display of the risk in the risk zone as well as the source or cause of the risk, allowing the driver to intuitively understand why they should pay attention to the risk zone.

[0019] Specifically, the sensor unit is configured to sense the vehicle's environment at the current time, and the display unit is configured to display the vehicle's environment with the stated characteristics and the risk zone of the stated characteristics at the current time. The sensor unit, processing unit, and display unit are each configured to perform their respective functions in real time. That is, the sensor unit can be configured to continuously sense and monitor the vehicle's environment and provide the sensing output to the processing unit. The processing unit can be configured to continuously determine at least one characteristic of the environment based on the sensing output and determine the risk zone of the at least one characteristic at the current time. The display unit can be configured to continuously display the vehicle's environment with the at least one characteristic and the risk zone of the at least one characteristic.

[0020] Optionally, the processing unit is configured to estimate the actual risk of the feature at the vehicle's actual location at the current time based on the at least one parameter of the vehicle at the current time. In addition to displaying the at least one feature and its risk zone, the display unit may be configured to display the estimated actual risk of the feature.

[0021] Compared to the vehicle's actual location at the current time, the two or more virtual locations are spatially closer to the feature. At least a portion of the virtual locations may be located at different distances from the feature.

[0022] The processing unit is configured to form a risk zone based on the corresponding risk estimated for each of the two or more virtual locations.

[0023] Because the driver of the vehicle can be assisted by an advanced driver assistance system, the vehicle can also be referred to as an autonomous vehicle. The vehicle can be any vehicle known in the art that can be driven by a driver on land, near land, or in water, such as automobiles, motorcycles, trucks, bicycles (e.g., electric bicycles), airplanes, helicopters, boats, submarines, etc. Optionally, the vehicle is at least a ground vehicle (capable of moving on land). Optionally, the vehicle can also be a water vehicle (capable of moving in water and optionally underwater) and / or an air vehicle (capable of moving in the air, i.e., capable of flight). The vehicle can be equipped with a motor, such as an internal combustion engine, an electric motor, or a hybrid motor. If the vehicle is an air vehicle (e.g., an airplane or vehicle), the following description of streets may be relevant when the air vehicle is moving on or near the ground (e.g., a helicopter flying near the ground). If the vehicle is a water vehicle (e.g., a boat), the following description of streets may be relevant to waterways (e.g., waterways, water channels, etc.).

[0024] The sensor unit may include or correspond to one or more sensors for sensing the environment of the vehicle. For example, the sensor unit may include or correspond to at least one camera, at least one radar sensor, at least one lidar sensor, at least one ultrasonic sensor, at least one infrared sensor, and / or at least one presence and / or motion sensor. Alternatively, the sensor unit may include or correspond to any other one or more sensors known in the art for sensing and monitoring the environment, respectively.

[0025] Advanced driver assistance systems may include a positioning unit configured to determine the vehicle's actual position at the current time. The positioning unit may be part of a processing unit. The positioning unit may be part of an optional navigation system. The positioning unit may include or correspond to at least one gyroscope and / or at least one accelerometer. Alternatively or as an alternative, the positioning unit may be configured to use a global navigation satellite system (GNSS) (e.g., Global Positioning System (GPS)) and / or radio communications (e.g., mobile communication systems (e.g., cellular networks)) to determine the vehicle's actual position. The positioning unit may be implemented in any manner known in the art.

[0026] Advanced driver assistance systems (ADAS) may include a navigation system configured to provide navigation assistance to the driver of a vehicle. The navigation system may be part of a processing unit. The navigation system may be implemented in any manner known in the art. A display unit may be configured to display a recommended driving route suggested by the navigation system. ADAS may also include a map unit configured to provide map data for the vehicle's environment, particularly the vehicle's actual location at the current time. The map unit may be part of the processing unit. The map unit may be configured to store map data in the ADAS's data storage (particularly in the form of one or more lookup tables) and / or may be configured to receive map data from an external source (e.g., from an external database). The data storage may include or correspond to a portable data storage device, such as a universal serial bus (USB) flash drive (USB strip), external hard disk drive, or optical disc (e.g., Blu-ray disc, digital versatile disk, DVD, compact disc, CD). The map unit may be configured to receive map data wirelessly from an external source.

[0027] The processing unit may include or correspond to a controller, microcontroller, processor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or any combination thereof. The processing unit may include a risk framework for calculating estimated risks (particularly for the two or more estimated risks at the two or more virtual locations of the vehicle). The risk framework may correspond to software executable by the processing unit. The processing unit may include a risk engine configured to calculate estimated risks for the corresponding virtual locations of the vehicle. The processing unit may include a risk mapper configured to perform risk estimation with respect to a path. The path may include or correspond to: the path between the vehicle and a feature; if the feature is a movable obstacle, the estimated travel path of the vehicle at the current time and / or the estimated movement path of the feature. The processing unit may include a situation classifier configured to predict the driver's intentions, particularly predicting the driver's expected driving behavior. The risk engine, risk mapper, and situation classifier may form a complete risk framework. The risk engine, risk mapper, and situation classifier can correspond to software executable by the processing unit. The risk framework (especially the risk engine, risk mapper, and situation classifier) ​​can be implemented in any manner known in the relevant field. That is, the processing unit can be configured to estimate risk based on at least one parameter of the vehicle provided to the processing unit as input data and any location of the vehicle, in any manner known in the relevant field.

[0028] The display unit may include or correspond to one or more displays (also referred to as screens). Alternatively, the display unit may include or correspond to a head-up display (i.e., a transparent display) for displaying information. The information may include or correspond to the vehicle's environment, features, risk zones of the features, and / or additional information about other characteristics of the features and / or environment. The display unit may be configured to display navigation based on map data. The display unit may be configured to display the environment based on map data. The display unit may be configured to display information about the vehicle's status, particularly information about at least one parameter of the vehicle. The display unit may be configured to display information instructing the driver's driving behavior (e.g., instructions to decelerate, brake, etc.) based on the vehicle's actual position relative to the risk zone of the at least one feature.

[0029] The display unit can be configured to display the at least one feature and its risk area in a two-dimensional (2D) bird's-eye view of the vehicle's environment. Alternatively, the display unit can be configured to display the at least one feature and its risk area in a first-person perspective within a virtual reality display encompassing the vehicle's environment. Alternatively, the display unit can be configured to display the at least one feature and its risk area using an augmented reality display in a first-person perspective. Alternatively, the display unit can be configured to display the at least one feature and its risk area such that the at least one feature and its risk area are projected onto a 2D plane of the street on which the vehicle is traveling, and such that the at least one feature and its risk area are constrained by street geometry (e.g., visible only in drivable areas).

[0030] Advanced driver assistance systems may include a human-machine interface (HMI). A display unit may be part of or corresponding to the HMI. The HMI may be configured to output driving behavior suggestions or instructions (e.g., deceleration, braking, etc.) and warnings based on the risk of a risk zone based on at least one of the aforementioned features. Alternatively, the HMI may be configured to output driving behavior suggestions or instructions and warnings based on the distance between the vehicle's current position and the boundary of the risk zone. The HMI may be configured to provide visual output (particularly using a display unit), audio output (e.g., using a speaker), and / or haptic output (e.g., using a vibrating element, such as a vibrating element located at the steering wheel). The HMI may be implemented in any manner known in the art. Suggestions and warnings may be communicated to the driver through the HMI to support the driver's driving behavior.

[0031] If the processing unit determines two or more features of the vehicle's environment, the above and following descriptions for one feature are equally valid for each of the two or more features.

[0032] Optionally, the processing unit is configured to determine the risk zone of the feature for the current time by iteratively performing a risk estimation process on a plurality of virtual locations of the vehicle, including or corresponding to the two or more virtual locations of the vehicle, to estimate the two or more risks for the two or more virtual locations. In each iteration of the risk estimation process, a corresponding risk with respect to the feature is estimated for a corresponding virtual location among the plurality of virtual locations based on at least one parameter of the vehicle at the current time. The risk estimation process stops after an iteration in which the corresponding risk is equal to or less than a risk threshold.

[0033] If the number of two or more risks estimated during the risk assessment process is less than a threshold, the processing unit can be configured to repeat the risk assessment process, wherein...

[0034] The distances of the virtual locations from the feature used in repeated risk assessments are reduced compared to the distances used during the initial risk assessment process. Initially, the virtual locations can be arbitrarily selected within constraints. These constraints can be regions surrounding the feature, and the virtual locations will be situated within these regions.

[0035] If the risk estimation process stops after the first iteration, the processing unit can be configured to repeat the risk estimation process, wherein the distance of the plurality of virtual locations used in the repeated risk estimation process from the feature is reduced compared to the distance of the plurality of virtual locations from the feature used in the risk estimation process.

[0036] If the risk assessment process stops after the first iteration, the processing unit can be configured to repeat the risk assessment process in the first iteration of the repeated risk assessment process using a different virtual location than the virtual location used in the first iteration of the risk assessment process.

[0037] The processing unit can be configured to iteratively perform a risk estimation process on the vehicle's plurality of virtual locations, starting with the virtual location that is spatially closest to the feature, in order of distance from the feature among the plurality of virtual locations. Optionally, the plurality of virtual locations are spatially closer to the feature than the vehicle's actual location at the current time.

[0038] The processing unit can be configured to store the virtual location of the iteration as a key location for defining the start or end of the risk zone of the feature, wherein the corresponding risk is equal to or less than a risk threshold.

[0039] Optionally, the processing unit may use all risks estimated by the risk estimation process, except for the risks estimated in the last iteration of the risk estimation process (after which the risk estimation process stops). That is, the two or more risks may correspond to risks estimated by the risk estimation process other than the risks estimated in the last iteration of the risk estimation process (after which the risk estimation process stops).

[0040] If the processing unit determines two or more features of the environment, then: the processing unit may be configured to determine the risk area of ​​at least one of the two or more features for the current time, and the display unit may be configured to display the environment of the vehicle having the two or more features and the risk area of ​​at least one of the two or more features. Alternatively, the processing unit may be configured to determine the risk area of ​​each of the two or more features for the current time, and the display unit may be configured to display the environment of the vehicle having the two or more features and the risk area of ​​at least one of the two or more features.

[0041] The feature may include or correspond to at least one of the following: obstacles present in the environment, street features present in the environment, and indicators indicating traffic rules.

[0042] For example, as a traffic rule, such an indicator can instruct you to stop at a certain location, give way to another vehicle at a certain location, or limit your speed to a speed limit.

[0043] The obstacles may include other vehicles, people, and other physical objects. The street characteristics may include street curves, street intersections, street slopes exceeding a slope threshold, weather-affected street areas, and street areas with damaged surfaces. The traffic indicator may include traffic signs, street markings, and traffic lights.

[0044] The at least one parameter may include or correspond to at least one of the following: the vehicle's direction of travel, the vehicle's speed, the vehicle's acceleration, the vehicle's acceleration time, the vehicle's braking time, the vehicle's size, and the vehicle's shape.

[0045] An advanced driver assistance system may include one or more sensors configured to sense at least one parameter of the vehicle and provide that parameter to a processing unit. Alternatively, the processing unit may be configured to receive the at least one parameter of the vehicle from an external source (e.g., from an external database). The processing unit may be configured to receive the at least one parameter of the vehicle wirelessly from an external source.

[0046] Depending on the type of the feature, the corresponding risk may include or correspond to time-based risk. The time-based risk may be one of the following: headway, collision time, braking time, number of braking threads, steering time, and liability-sensitive safety. Alternatively, depending on the type of the feature, the corresponding risk may include or correspond to probabilistic risk. The probabilistic risk may be a risk estimated using a Gaussian method or a risk estimated using survival analysis.

[0047] Depending on the type of the feature and optionally on the estimated travel path of the vehicle at the current time, the corresponding risk may include or correspond to the following risks:

[0048] - Collision risk if the feature corresponds to an obstacle, street intersection, or indicator indicating at least one traffic rule present in the environment (the indicator indicating at least one traffic rule may optionally be a traffic sign, a road marking on the street, or a traffic light).

[0049] - Lane departure risk if the features are street curves, street intersections, street slopes greater than a slope threshold, weather-affected street areas, or street areas with damaged surfaces;

[0050] - Lateral acceleration risk if the feature is a street bend or street intersection;

[0051] - Acceleration risk if the feature is a street slope greater than a slope threshold;

[0052] - Risk of loss of control of the vehicle if the features are street bends, street intersections, street slopes greater than a slope threshold, weather-affected street areas, or street areas with damaged surfaces.

[0053] - Risk of violating traffic rules if the feature corresponds to an indicator that indicates at least one traffic rule (the indicator that indicates at least one traffic rule may optionally be a traffic sign, a road marking, or a traffic light); and / or

[0054] - The risk of damage to the vehicle if the feature corresponds to a street area with a damaged surface.

[0055] In other words, the two or more risks (estimated at the two or more virtual locations of the vehicle) may include or correspond to collision risk, lane departure risk, lateral acceleration risk, acceleration risk, vehicle loss of control risk, traffic violation risk and / or vehicle damage risk.

[0056] Optionally, the virtual positions of the vehicle form a grid.

[0057] The processing unit may be configured to arrange the virtual locations in the environment according to the type of the feature. Alternatively, the processing unit may be configured to arrange the virtual locations in the environment such that the virtual locations are at least located in the region between the vehicle and the feature at the current time. Alternatively, the processing unit may be configured to arrange the virtual locations in the environment such that the virtual locations are located in the region between the vehicle and the feature at the current time. Alternatively, the processing unit may be configured to arrange the virtual locations in the environment such that at least one of the virtual locations is equal to the actual location of the feature at the current time or a portion of the actual location of the feature at the current time. Alternatively, the processing unit may be configured to arrange the virtual locations in the environment such that at least one of the virtual locations is equal to a location associated with the actual location of the feature at the current time or a location associated with the actual location of a portion of the feature at the current time.

[0058] Optionally, the processing unit is configured to arrange the virtual location in the environment such that the virtual location is arranged along a path. The path may be the path between the vehicle and the feature at the current time. Alternatively, the path may be an estimated travel path of the vehicle at the current time. Alternatively, if the feature is a movable obstacle, the path may be an estimated future movement path of the feature. Alternatively, the path may be adapted to the street on which the vehicle and / or the movable obstacle is located at the current time, and optionally is moving. Alternatively, the path may be adapted to the feature. The path may be provided by map data.

[0059] The estimated driving path can be determined based on the driver's driving behavior and / or the navigation system in operation (the path corresponds to a recommended driving route recommended by the navigation system). The expression "suitable for streets" can be understood as "routes and / or shapes suitable for streets".

[0060] The display unit may be configured to display the risk area of ​​the feature by changing the color, shading, and / or pattern of the risk area based on the distance between the boundary of the risk area and the actual position of the vehicle at the current time. Alternatively, the display unit may be configured to display the risk area of ​​the feature such that the risk area is divided into segments of different colors, shading, and / or patterns, wherein the segments correspond to continuous risk ranges. Alternatively, the display unit may be configured to display the risk area of ​​the feature such that the risk area is appropriate to the feature and / or the street where the feature is located at the current time.

[0061] If the feature is a street curve, the processing unit may be configured to estimate, based on at least one parameter of the vehicle at the current time, the lateral acceleration caused by the curvature of the street curve at each of the vehicle's virtual positions as the corresponding risk; the virtual positions may be equal to the actual positions of different portions of the street curve. The corresponding risk may be a lane departure risk, wherein the greater the lateral acceleration, the greater the lane departure risk.

[0062] If the feature is an indicator indicating at least one traffic rule (which may be a traffic sign, a road marking, or a traffic light) or a street intersection, the processing unit may be configured to estimate a collision risk as the corresponding risk at each of the vehicle's virtual locations based on the vehicle's at least one parameter at the current time. The virtual locations may be located at least in the area between the vehicle and the feature, and at least one of the virtual locations may be equal to the actual location of the feature or the actual location of a portion of the feature. This is possible when the feature is a road marking (as an indicator indicating at least one traffic rule) or a street intersection. Alternatively, the virtual locations may be located at least in the area between the vehicle and the feature, and at least one of the virtual locations may be equal to a location associated with the actual location of the feature or a location associated with the actual location of a portion of the feature. This is possible when the feature is a traffic sign or a traffic light (as an indicator indicating at least one traffic rule).

[0063] The processing unit may be configured to determine driving behavior recommendations based on the two or more risks and / or risk areas of the at least one feature. Alternatively, the processing unit may be configured to determine driving behavior instructions based on the two or more risks and / or risk areas of the at least one feature. Alternatively, the processing unit may be configured to determine warnings based on the two or more risks and / or risk areas of the at least one feature. The display unit may be configured to display the driving behavior recommendations, the driving behavior instructions, and / or the warnings. For this purpose, the advanced driver assistance system may include a human-machine interface (HMI), as described above. The display unit may be part of or correspond to the HMI. The above descriptions regarding driving behavior recommendations, driving behavior instructions, warnings, and the HMI are accordingly applicable.

[0064] As described above, the display unit can be configured to display the at least one feature and the risk area of ​​the at least one feature:

[0065] - A two-dimensional bird's-eye view of the environment of the vehicle is shown;

[0066] - Displayed from a first-person perspective included in the virtual reality display of the environment of the vehicle;

[0067] - Displayed from a first-person perspective using an augmented reality display; and / or

[0068] - The at least one feature and the risk area of ​​the at least one feature are projected onto a 2D plane of the street on which the vehicle travels, and the at least one feature and the risk area of ​​the at least one feature are constrained by the street geometry.

[0069] To achieve the advanced driver assistance system according to the first aspect of the present invention, some or all of the above-mentioned optional features may be combined with each other.

[0070] According to a second aspect of the invention, a vehicle is provided, wherein the vehicle includes an advanced driver assistance system according to the first aspect of the invention described above for assisting the driver of the vehicle.

[0071] The above description of the advanced driver assistance system according to the first aspect of the present invention is also valid for vehicles according to the second aspect of the present invention.

[0072] The vehicle according to the second aspect of the invention achieves the same advantages as the advanced driver assistance system according to the first aspect of the invention.

[0073] The advanced driver assistance system for the vehicle is implemented according to the advanced driver assistance system of the first aspect of the present invention described above.

[0074] According to a third aspect of the invention, a method for assisting a driver of a vehicle is provided. The method includes: sensing the environment of the vehicle; providing a sensing output; and determining at least one feature of the environment based on the sensing output. The method further includes determining a risk zone for the feature for a current time by: estimating a corresponding risk for the feature for each of two or more virtual locations of the vehicle based on at least one parameter of the vehicle at the current time, to estimate two or more risks for the two or more virtual locations; and forming the risk zone based on the two or more risks. The method further includes: displaying the environment of the vehicle having the feature and the risk zone of the feature.

[0075] The above description of the advanced driver assistance system according to the first aspect of the present invention is also effective for the method according to the third aspect of the present invention.

[0076] Optionally, the method includes determining the risk zone of the feature for the current time by iteratively performing a risk estimation process on a plurality of virtual locations of the vehicle, including or corresponding to the two or more virtual locations of the vehicle, to estimate the two or more risks for the two or more virtual locations. In each iteration of the risk estimation process, a corresponding risk with respect to the feature is estimated at a corresponding virtual location among the plurality of virtual locations based on at least one parameter of the vehicle at the current time. The risk estimation process stops after an iteration in which the corresponding risk is equal to or less than a risk threshold.

[0077] If two or more features of the environment are determined, the method may include: determining a risk area for at least one of the two or more features with respect to the current time; and displaying the environment of the vehicle having the two or more features and the risk area for the feature among the two or more features. Alternatively, the method may include: determining a risk area for each of the two or more features with respect to the current time; and displaying the environment of the vehicle having the two or more features and the risk area for at least one of the two or more features.

[0078] The method may include: arranging the virtual locations in the environment according to the type of the feature. Alternatively, the method may include: arranging the virtual locations in the environment such that the virtual locations are at least optionally located in the region between the vehicle and the feature at the current time. Alternatively, the method may include: arranging the virtual locations in the environment such that at least one of the virtual locations is equal to the actual location of the feature at the current time or a portion of the actual location of the feature at the current time. Alternatively, the method may include: arranging the virtual locations in the environment such that at least one of the virtual locations is equal to a location associated with the actual location of the feature at the current time or a location associated with the actual location of a portion of the feature at the current time.

[0079] Optionally, the method includes arranging the virtual location in the environment such that the virtual location is arranged along a path. The path may be the path between the vehicle and the feature at the current time. Alternatively, the path may be an estimated travel path of the vehicle at the current time. Alternatively, if the feature is a movable obstacle, the path may be an estimated movement path of the feature. Alternatively, the path may be adapted to the street on which the vehicle and / or the movable obstacle is located at the current time, and optionally is moving. Alternatively, the path may be adapted to the feature.

[0080] The method may include displaying the risk area of ​​the feature by changing the color, shading, and / or pattern of the risk area based on the distance between the boundary of the risk area and the actual position of the vehicle at the current time. Alternatively, the method may include displaying the risk area of ​​the feature such that the risk area is divided into segments of different colors, shading, and / or patterns, wherein the segments correspond to continuous risk ranges. Alternatively, the method may include displaying the risk area of ​​the feature such that the risk area is adapted to the feature and / or the street where the feature is located at the current time.

[0081] If the feature is a street curve, the method may include: estimating, based on at least one parameter of the vehicle at the current time, the lateral acceleration caused by the curvature of the street curve as the corresponding risk for each of the vehicle's virtual positions. The virtual positions may be equal to the actual positions of different portions of the street curve.

[0082] If the feature is an indicator indicating at least one traffic rule (which may be a traffic sign, street markings, or traffic lights) or a street intersection, the method may include: estimating a collision risk as the corresponding risk at each of the vehicle's virtual locations based on the vehicle's at least one parameter at the current time. The virtual locations may be located at least in the area between the vehicle and the feature, and at least one of the virtual locations may be equal to the actual location of the feature or the actual location of a portion of the feature. Alternatively, the virtual locations may be located at least in the area between the vehicle and the feature, and at least one of the virtual locations may be equal to a location associated with the actual location of the feature or a location associated with the actual location of a portion of the feature.

[0083] The method according to the third aspect of the invention achieves the same advantages as the advanced driver assistance system according to the first aspect of the invention.

[0084] To implement the method according to the third aspect of the invention, some or all of the above optional features may be combined with each other.

[0085] As described above, the fourth aspect of the present invention provides program code for implementing the method according to the third aspect of the present invention.

[0086] A fifth aspect of the invention provides a computer program including program code, which, when implemented on a processor, is used to perform the method according to the third aspect of the invention as described above.

[0087] A sixth aspect of the present invention provides a computer including a memory and a processor, the memory and processor being configured to store program code and execute the program code to perform the method according to the third aspect of the present invention as described above.

[0088] The program code of the fourth aspect, the computer program according to the fifth aspect, and the computer according to the sixth aspect each achieve the same advantages as the advanced driver assistance system according to the first aspect of the present invention. Attached Figure Description

[0089] The invention is illustrated below with reference to the accompanying drawings, in which:

[0090] Figure 1 A flowchart of an embodiment of the method according to the third aspect of the present invention is shown as an example.

[0091] Figure 2 A block diagram of an embodiment of an advanced driver assistance system according to a first aspect of the present invention is shown as an example.

[0092] Figure 3 Two scenarios are illustrated exemplarily using embodiments of an advanced driver assistance system according to the first aspect of the present invention.

[0093] Figure 4 Exemplary scenarios are shown for using an advanced driver assistance system according to the first aspect of the present invention under different conditions.

[0094] Figure 5 Exemplary scenarios are shown for using an advanced driver assistance system according to the first aspect of the present invention under different conditions.

[0095] Figure 6 Two scenarios are illustrated exemplarily using embodiments of an advanced driver assistance system according to the first aspect of the present invention.

[0096] Figures 7 to 12 An example of output that can be displayed by a display unit of an embodiment of an advanced driver assistance system according to a first aspect of the invention is shown. Detailed Implementation

[0097] Figure 1 A flowchart illustrating an embodiment of a method according to a third aspect of the present invention is provided. In step 100, the environment of the vehicle can be sensed and a sensing output can be provided. In step 200, following step 100, at least one feature of the environment can be determined based on the sensing output. In step 300, following step 200, a corresponding risk for the feature can be estimated at each of two or more virtual locations of the vehicle based on at least one parameter of the vehicle at the current time, to estimate the two or more risks for the two or more virtual locations. In step 400, following step 300, a risk zone for the feature can be formed based on the two or more risks. In step 500, following step 400, the environment of the vehicle having the feature and the risk zone for the feature can be displayed. Steps 300 and 400 enable the determination of the risk zone for the feature at the current time.

[0098] In the case where two or more features are identified in step 200, steps 300 and 400 can be performed on at least one of the two or more features to generate a risk zone for the at least one feature. In step 500, the vehicle's environment, the two or more features, and the risk zone for at least one of the two or more features can be displayed. Alternatively, steps 300 and 400 can be performed on each of the two or more features. In this case, in step 500, the vehicle's environment, the two or more features, and the risk zone for at least one of the two or more features can be displayed. Alternatively, in step 500, the vehicle's environment, the two or more features, and the risk zone for each of the two or more features can be displayed.

[0099] for Figure 1 A more detailed description of the method shown is provided above with reference to the method according to the third aspect of the present invention and the advanced driver assistance system according to the first aspect of the present invention.

[0100] Figure 2 A block diagram of an embodiment of an advanced driver assistance system according to a first aspect of the present invention is shown as an example.

[0101] Figure 2 An advanced driver assistance system 1 is shown for assisting the driver of a vehicle. For example... Figure 2 As shown, the advanced driver assistance system 1 may include a sensor unit 2, a processing unit 3, and a display unit 4. The sensor unit 2 may include or correspond to one or more sensors 2a for sensing the vehicle's environment. The sensor unit 2 may be configured to perform... Figure 1 Method step 100 of the method shown. Processor unit 3 can be configured to execute Figure 1 Steps 200, 300, and 400 of the method shown. The display unit 4 can be configured to implement... Figure 1 Step 500 of the method shown.

[0102] To provide a more detailed description of the advanced driver assistance system 1 (especially) Figure 2 The sensor unit 2, processing unit 3, and display unit 4 shown above refer to the description of the advanced driver assistance system according to the first aspect of the present invention. In particular, reference is made to the description of the sensor unit, processing unit, and display unit of the advanced driver assistance system according to the first aspect of the present invention.

[0103] Figure 3 , Figure 4 , Figure 5 and Figure 6 An exemplary scenario is shown using an embodiment of an advanced driver assistance system according to the first aspect of the present invention.

[0104] Figure 3 (a) illustrates a scenario in which a further vehicle 12 is traveling in front of a vehicle 11 (autonomous vehicle) driven by a driver assisted by an advanced driver assistance system according to an embodiment of the invention. Therefore, the processing unit of the advanced driver assistance system is configured to determine, based on the sensing output of a sensor unit (sensing the environment of vehicle 11), at least one characteristic of the environment of vehicle 11 as defined by the further vehicle 12. Figure 3 , Figure 4 , Figure 5 and Figure 6 In the text, the reference marker "AP1" indicates the actual position of vehicle 11 (i.e., the autonomous vehicle) at the current time, and in... Figure 3 , Figure 4 and Figure 5 In this context, the reference marker "AP2" is used to indicate the actual position of the other vehicle 12 at the current time. Figure 3 , Figure 4 and Figure 5 In the scene shown, the feature present in the environment of vehicle 11 is another vehicle 12. Figure 6 In the scene shown in (a), the feature present in the environment of vehicle 11 is the street curve 13, and in Figure 6 In the scenario shown in (b), the features present in the environment of vehicle 11 are indicators 14 that indicate at least one traffic rule, in particular ground markings and / or traffic signs.

[0105] The positions of the black circles on vehicle 11 and the other vehicle 12, used to indicate actual or virtual locations, are merely illustrative and may vary from other locations. Figure 3 , Figure 4 , Figure 5 and Figure 6 The positions shown may differ. For example, the black circle of vehicle 11 used to indicate the actual or virtual position of vehicle 11 may be located at the front, rear, or any other position of vehicle 11.

[0106] like Figure 3As shown in scenario (a), the processing unit can estimate the corresponding risk to another vehicle 12 at the current time for three virtual positions VP1, VP2, and VP3 of vehicle 11 based on at least one parameter of the vehicle at the current time. The number of virtual positions is merely an example and therefore does not limit the invention. The number may be two or more virtual positions. The at least one parameter of the vehicle is exemplarily assumed to be the speed of vehicle 11. The at least one parameter of the vehicle may include or correspond to one or more parameters, as described above with respect to the advanced driver assistance system of the first aspect of the invention. The corresponding risk to another vehicle is exemplarily assumed to be the collision risk between vehicle 11 and another vehicle 12.

[0107] like Figure 3 As shown in (a), the virtual positions VP1, VP2, and VP3 of vehicle 11 are arranged in the area between vehicle 11 and another vehicle 12, and the virtual positions VP1, VP2, and VP3 are spatially closer to the actual position AP2 of the other vehicle 12 than the actual position AP1 of vehicle 11. Figure 3 As shown in (a), virtual locations VP1, VP2, and VP3 can be arranged along a path (indicated by dashed lines) between vehicle 11 and another vehicle 12. Optionally, the path is an estimated travel path of vehicle 11 at the current time. The three virtual locations can be arranged arbitrarily or in an ordered manner (e.g., the virtual locations are equidistant from each other). Specifically, the virtual locations can form a grid.

[0108] The processing unit can be configured to form a risk zone RZ for another vehicle 12 based on three estimated risks estimated at the three virtual locations VP1, VP2 and VP3 for the current time (i.e., the corresponding risk among the three estimated risks estimated at each of the three virtual locations VP1, VP2 and VP3 for the current time).

[0109] like Figure 3 As shown in (a), optionally, the risk of forming a risk zone RZ for another vehicle 12 can be estimated by iteratively performing a risk estimation process on a plurality of virtual positions VP1, VP2, VP3, and VP4 of vehicle 11. In each iteration of the risk estimation process, a corresponding risk (e.g., the risk of collision between vehicle 11 and another vehicle 12) is estimated at a corresponding virtual position among the plurality of virtual positions based on at least one parameter of vehicle 11 at the current time (e.g., the speed of vehicle 11). The risk estimation process stops after an iteration in which the corresponding risk is equal to or less than a risk threshold.

[0110] exist Figure 3In scenario (a), it is exemplarily assumed that the processing unit iteratively performs a risk estimation process on the plurality of virtual locations VP1, VP2, VP3, and VP4 of vehicle 11, starting from the virtual location Vp1 that is spatially closest to the other vehicle 12, according to the order of their distances from the other vehicle 12. Furthermore, it is exemplarily assumed that the estimated risk of the other vehicle 12 for virtual location VP3 at the current time is equal to a risk threshold. Therefore, after estimating the risk of the other vehicle 12 for virtual location VP3 at the current time, the processing unit stops performing the risk estimation process. Therefore, the risk of the other vehicle is estimated only for virtual locations VP1, VP2, and VP3 at the current time, but not for virtual location VP4 at the current time. Therefore, a risk zone RZ for the other vehicle 12 is formed based on the three estimated risks for the three virtual locations VP1, VP2, and VP3. Virtual location VP3 can be stored as critical location CP, which defines the start or end point of the risk zone RZ of another vehicle 12.

[0111] like Figure 4 As shown in (a), the display unit of the advanced driver assistance system can be configured to display the risk zone RZ of the additional vehicle 12 such that the risk zone RZ of the additional vehicle 12 is divided into segments of different colors, shades, and / or patterns, wherein the segments correspond to continuous risk ranges. Since the risk zone RZ of the additional vehicle is based on... Figure 3 The three virtual locations VP1, VP2 and VP3 of vehicle 11 shown in (a) are formed based on the estimated three risks, so the risk zone RZ of the other vehicle 12 can be divided into three segments, wherein the boundaries of the three segments correspond to the three estimated risks.

[0112] The risk zone RZ segment between the other vehicle 12 and virtual location VP1 ( Figure 4 (a) shows the densest dashed segment) and corresponds to a risk range greater than or equal to the risk estimated for the current time and for virtual location VP1. If the estimated risk corresponds to a collision risk, the risk range lies between the 100% risk of another vehicle 12 being at the boundary of the other vehicle 12's risk zone RZ and the collision risk estimated for the current time and for virtual location VP1. That is, if vehicle 11 and another vehicle 12 are located at the boundary of the risk zone RZ, a collision will occur between vehicle 11 and the other vehicle 12. The continuous segments of the risk zone RZ between virtual location VP1 and virtual location VP2 ( Figure 4The second dense dashed segment shown in (a) corresponds to a risk range that is less than the risk estimated for the current time and for virtual location VP1, but greater than or equal to the risk estimated for the current time and for virtual location VP2. The continuous segments of the risk zone RZ between virtual location VP2 and virtual location VP3 ( Figure 4 The least dense dashed segment shown in (a) corresponds to the risk range that is less than the risk estimated for the current time and for virtual location VP2, but greater than or equal to the risk estimated for the current time and for virtual location VP3.

[0113] Therefore, as Figure 3 (a) and Figure 4 As shown in (a), the risk zone RZ of the other vehicle 12 corresponds to the personal space of the other vehicle 12. As long as vehicle 11 does not intrude into the risk zone RZ, the risk to the other vehicle 12 (e.g., the risk of a collision between vehicle 11 and the other vehicle 12) is less than the risk estimated at the current time for virtual location VP3, and therefore less than the risk threshold. The risk zone RZ of vehicle 12 (when displayed on the display unit) allows the driver of vehicle 11 to visually assess the risk of vehicle 11 to the other vehicle 12 by visualizing the theoretical risk to virtual locations VP1, VP2, and VP3. Virtual locations VP1, VP2, and VP3 differ from the actual location AP1 of vehicle 11 at the current time, and are spatially closer to the other vehicle 12 compared to the actual location AP1 of vehicle 11 at the current time.

[0114] like Figure 4 (b) Figure 4 (c) and Figure 4 As shown in (d), or alternatively, the display unit of the advanced driver assistance system may be configured to display the risk zone RZ of another vehicle 12 by changing the color, shading, and / or pattern of the risk zone based on the distance between the boundary of the risk zone RZ and the actual position of vehicle 11 at the current time. The boundary is in Figure 4 of (b) Figure 4 (c) and Figure 4 The critical position CP is indicated in (d). Figure 4 In (b), vehicle 11 is spatially furthest from the boundary of the risk zone RZ of the other vehicle 12. Figure 4 In (c), with Figure 4 Compared to (b), vehicle 11 is spatially closer to the boundary of the risk zone RZ, but still does not intersect with the boundary. Figure 4In (d), vehicle 11 is crossing the boundary of the risk zone RZ. The closer vehicle 11 is spatially to another vehicle 12, the greater the risk (e.g., collision risk) to the other vehicle 12. Therefore, Figure 4 The risk zone RZ shown in (d) can be colored with a warning color (e.g., red). Figure 4 The risk zone shown in (c) can be colored to raise awareness (e.g., orange or yellow). Figure 4 The risk zone RZ shown in (b) can be colored to indicate the low-risk (safe) state of vehicle 11 relative to another vehicle 12 (e.g., green). Figure 4 In the diagram, the different colors, shadows, and / or patterns of the risk zone RZ segments or the risk zone itself are indicated by different densely dotted areas.

[0115] The display unit of the advanced driver assistance system can be configured to... Figure 4 The embodiment shown in (a) and Figure 4 of (b) Figure 4 (c) and Figure 4 The embodiment shown in (d) alternately displays the risk areas. Alternatively, or as another option, the driver can target... Figure 3 The scenario shown in (a) selects the display type for the risk area RZ of the display unit.

[0116] Figure 3 (b) exemplarily illustrates another scenario using an embodiment of an advanced driver assistance system according to the first aspect of the invention. Figure 3 In the scenario shown in (b), a vehicle 11 (autonomous vehicle) driven by a driver assisted by an advanced driver assistance system and another vehicle 12 travel in a manner such that the travel paths of vehicle 11 and the other vehicle 12 intersect each other perpendicularly, for example, at a street intersection. Regarding Figure 3 (a) and Figure 4 of (a) Figure 4 of (b) Figure 4 (c) and Figure 4 The above explanation of (d) is for the purpose of elucidating Figure 3 The scenario shown in (b) is valid accordingly.

[0117] according to Figure 3In the scenario shown in (b), the processing unit of the advanced driver assistance system estimates the risk of another vehicle 12 at five virtual locations VP1, VP2, VP3, VP4, and VP5 of vehicle 11 at the current time, based on at least one parameter of vehicle 11 at the current time. The virtual locations VP1, VP2, VP3, VP4, and VP5 can be arranged along a path, which is the estimated movement path of the other vehicle 12. The processing unit can form a risk zone RZ for the other vehicle 12 based on the virtual locations VP1, VP2, VP3, VP4, and VP5.

[0118] like Figure 5 of (a) Figure 5 (b) and Figure 5 As shown in (c), the display unit of the advanced driver assistance system can be configured to display the risk zone RZ of another vehicle 12 by changing the color, shading, and / or pattern of the risk zone based on the distance between the boundary of the risk zone RZ and the actual position of vehicle 11 at the current time. Figure 5 In (a), vehicle 11 is spatially furthest from the boundary of the risk zone RZ of the other vehicle 12. Figure 5 In (b), with Figure 5 Compared to (a), vehicle 11 is spatially closer to the boundary of the risk zone RZ. Figure 5 In (c), with Figure 5 (a) and Figure 5 Compared to (b), vehicle 11 is spatially closer to the boundary of the risk zone RZ. (Regarding...) Figure 4 of (b) Figure 4 (c) and Figure 4 The above explanation of (d) is for the purpose of elucidating Figure 5 of (a) Figure 5 (b) and Figure 5 The scenario of (c) (especially illustrating the different colors of the risk zone RZ of the other vehicle 12) is correspondingly effective.

[0119] Figure 6 (a) exemplarily illustrates another scenario using an embodiment of an advanced driver assistance system according to the first aspect of the invention. Figure 6 In the scenario shown in (a), a vehicle 11 (autonomous vehicle) driven by a driver assisted by an advanced driver assistance system travels along a street with a street curve 13 ahead. Therefore, the processing unit of the advanced driver assistance system is configured to determine at least one feature of the street curve 13 as the environment of the vehicle 11 based on the sensing output of the sensor unit (sensing the environment of the vehicle 11).

[0120] like Figure 6As shown in scenario (a), the processing unit can estimate the corresponding risk of vehicle 11 at the current time with respect to street curve 13 based on at least one parameter of the vehicle at the current time for three virtual positions VP1, VP2, and VP3. The number of virtual positions is merely an example and therefore not a limitation of the invention. The number may be two or more virtual positions. The at least one parameter of the vehicle is exemplarily assumed to be the speed of vehicle 11 at the current time. The at least one parameter of the vehicle may include or correspond to one or more parameters, as described above with respect to the advanced driver assistance system of the first aspect of the invention. The corresponding risk with respect to other vehicles is exemplarily assumed to be the lateral acceleration risk of vehicle 11 and / or the lane departure risk of vehicle 11.

[0121] like Figure 6 As shown in (a), the virtual positions VP1, VP2, and VP3 of vehicle 11 are arranged in the environment of vehicle 11 such that each virtual position VP1, VP2, and VP3 is equal to the actual position of a portion of street bend 13. Figure 3 As shown in (a), virtual locations VP1, VP2, and VP3 can be arranged along a path suitable for street curve 13. Optionally, the path is an estimated travel path of vehicle 11 at the current time. The three virtual locations can be arranged arbitrarily or in an ordered manner (e.g., the virtual locations are equidistant from each other). Specifically, the virtual locations can form a grid.

[0122] The processing unit can be configured to form a risk zone RZ for street curve 13 based on the three estimated risks estimated at the three virtual locations VP1, VP2 and VP3 for the current time (i.e., the corresponding risk among the three estimated risks estimated at each of the three virtual locations VP1, VP2 and VP3 for the current time).

[0123] like Figure 6 As shown in (a), optionally, the risk estimation of the risk zone RZ forming the street curve 13 can be performed by iteratively performing a risk estimation process on multiple virtual positions VP1, VP2, VP3, and VP4 of vehicle 11. In each iteration of the risk estimation process, a corresponding risk (e.g., lateral acceleration risk and / or lane departure risk of vehicle 11) with respect to the street curve 13 is estimated at the corresponding virtual position among the multiple virtual positions based on at least one parameter of vehicle 11 at the current time (e.g., the speed of vehicle 11). The risk estimation process stops after an iteration in which the corresponding risk is equal to or less than a risk threshold. Figure 6In the scenario shown in (a), it is exemplarily assumed that the processing unit performs a risk estimation process on the plurality of virtual locations VP1, VP2, VP3, and VP4 of the vehicle 11 in an iterative manner, starting with the virtual location VP1 that is spatially farthest from the actual location AP1 of the vehicle 11, according to the order of their distances from the actual location AP1 of the vehicle 11. Furthermore, it is exemplarily assumed that the estimated risk regarding the street curve 13 for the current time and for the virtual location VP3 is equal to a risk threshold. Therefore, after estimating the risk regarding the street curve 13 for the current time and for the virtual location VP3, the processing unit stops performing the risk estimation process. Therefore, the risk regarding the street curve 13 is estimated only for the current time and for the virtual locations VP1, VP2, and VP3, but not for the current time and for the virtual location VP4. Therefore, the risk zone RZ of the street curve 13 is formed based on the three estimated risks for the three virtual locations VP1, VP2, and VP3.

[0124] like Figure 6 As shown in (a), the display unit of the advanced driver assistance system can be configured to display the risk zone RZ of a street curve such that the risk zone RZ can be divided into segments of different colors, shades, and / or patterns, wherein the segments correspond to continuous risk ranges. Since the risk zone RZ of the other vehicle is formed based on the three risks estimated for the three virtual positions VP1, VP2, and VP3 of vehicle 11, the risk zone RZ can be divided into three segments. The segmentation of the risk zone RZ including virtual position VP2 ( Figure 6 The densest dashed segment shown in (a) corresponds to the high-risk range, which includes the risk estimated for the current time and virtual location VP2. The consecutive segments to the left and right correspond to the lower-risk range, which includes the risk estimated for the current time and virtual location VP1 (see the right-hand segment) and the risk estimated for the current time and virtual location VP3 (see the left-hand segment), respectively. The lower-risk range includes risks that are smaller than those in the high-risk range.

[0125] For example, if the estimated risk is the risk of lateral acceleration (lateral or transverse acceleration in the direction of motion) caused by the curvature of street curve 13, the estimated risk at the location of maximum curvature will be greater than the estimated risk at the location of minimum curvature. The curvature of street curve 13 at virtual location VP2 is greater than the curvature of street curve 13 at virtual locations VP1 and VP3. Therefore, the risk of lateral acceleration estimated for virtual location VP2 is greater than the risk of lateral acceleration estimated for virtual locations VP1 and VP3. For example, assume that the curvature of street curve 13 at virtual location VP1 is greater than the curvature of street curve 13 at virtual location VP3. Therefore, the risk of lateral acceleration estimated for virtual location VP1 is greater than the risk of lateral acceleration estimated for virtual location VP3.

[0126] according to Figure 6 (a) The risk zone RZ is suitable for street curves 13, and therefore suitable for the route and shape of the street. The risk zone RZ can be arranged in different ways, and therefore can be displayed in different ways.

[0127] Therefore, as Figure 6 As shown in (a), the risk zone RZ of street curve 13 corresponds to the personal space of street curve 13. As long as vehicle 11 does not intrude into the risk zone, the risk associated with street curve 13 (e.g., the risk of lateral acceleration caused by the curvature of the street curve) is less than the risk estimated for virtual position VP3 at the current time, and therefore less than the risk threshold. The risk zone RZ of street curve 13 (when displayed on the display unit) allows the driver of vehicle 11 to visually assess the risk associated with street curve 13 by visualizing the theoretical risk associated with street curve 13 for virtual positions VP1, VP2, and VP3. Virtual positions VP1, VP2, and VP3 differ from the actual position AP1 of vehicle 11 at the current time.

[0128] Figure 6 (b) illustrates a scenario in which an indicator 14 indicating at least one traffic rule (e.g., a street marking and / or traffic sign) is located in front of a vehicle 11 (driven by a driver assisted by an advanced driver assistance system according to an embodiment of the present invention). For further illustration, it is exemplarily assumed that the indicator 14 indicating at least one traffic rule is a traffic sign 14. This is not a limitation of the description, and therefore the description is also valid for any other indicator indicating at least one traffic rule. Thus, the processing unit of the advanced driver assistance system is configured to determine, based on the sensing output of the sensor unit (sensing the environment of the vehicle 11), the traffic sign 14 as said at least one feature of the environment of the vehicle 11.

[0129] like Figure 6As shown in scenario (b), the processing unit can estimate the corresponding risk of the vehicle 11 with respect to traffic sign 14 at the current time based on at least one parameter of the vehicle at the current time for four virtual positions VP1, VP2, VP3, and VP4. The number of virtual positions is merely an example and therefore does not limit the invention. The number may be two or more virtual positions. The at least one parameter of the vehicle is exemplarily assumed to be the speed of vehicle 11 at the current time. The at least one parameter of the vehicle may include or correspond to one or more parameters, as described above with respect to the advanced driver assistance system of the first aspect of the invention. The corresponding risk with respect to other vehicles is exemplarily assumed to be the risk of violating traffic rules (indicated by traffic signs).

[0130] like Figure 6 As shown in (b), the virtual locations VP1, VP2, VP3, and VP4 of vehicle 11 are at least arranged in the area between vehicle 11 and traffic sign 14, and the virtual address VP1 of the virtual locations is equal to the location associated with the actual location of traffic sign 14. The virtual locations are spatially closer to the actual location of traffic sign 14 than the actual location AP1 of vehicle 11. Figure 6 As shown in (b), virtual locations VP1, VP2, VP3, and VP4 can be arranged along a path between the vehicle 11 and the locations associated with the actual locations of traffic signs (indicated by dashed lines). Optionally, the path is an estimated travel path of the vehicle 11 at the current time. The four virtual locations can be arranged arbitrarily or in an ordered manner (e.g., the virtual locations are equidistant from each other). Specifically, the virtual locations can form a grid.

[0131] The processing unit can be configured to form the risk zone RZ of traffic sign 14 based on the four estimated risks estimated at the four virtual locations VP1, VP2, VP3 and VP4 for the current time (i.e., the corresponding risk among the four estimated risks is estimated at each of the four virtual locations VP1, VP2, VP3 and VP4 for the current time).

[0132] like Figure 6 As shown in (b), optionally, the risk assessment for forming the risk zone RZ of the street sign 14 can be performed by iteratively performing a risk assessment process on a plurality of virtual locations VP1, VP2, VP3, VP4, and VP5 of the vehicle 11. In each iteration of the risk assessment process, a corresponding risk (e.g., risk of violating traffic rules indicated by traffic sign 14) with respect to the traffic sign 14 is estimated at the corresponding virtual location among the plurality of virtual locations based on at least one parameter of the vehicle 11 at the current time (e.g., the speed of the vehicle 11). The risk assessment process stops after an iteration in which the corresponding risk is equal to or less than a risk threshold.

[0133] exist Figure 6 In the scenario shown in (b), it is exemplarily assumed that the processing unit iteratively performs a risk estimation process on the plurality of virtual locations VP1, VP2, VP3, VP4, and VP5 of the vehicle 11, starting from the virtual location VP1 that is spatially closest to the traffic sign 14, according to the order of their distances from the traffic sign 14. Furthermore, it is exemplarily assumed that the estimated risk of the traffic sign 14 at virtual location VP4 at the current time is equal to a risk threshold. Therefore, after estimating the risk of the traffic sign 14 at virtual location VP4 at the current time, the processing unit stops performing the risk estimation process. Thus, the risk of the traffic sign 14 is estimated only at virtual locations VP1, VP2, VP3, and VP4 at the current time, but not at virtual location VP5 at the current time. Therefore, the risk zone RZ of the traffic sign 14 is formed based on the four estimated risks estimated for the four virtual locations VP1, VP2, VP3, and VP4. Virtual location VP4 can be stored as a critical location CP that defines the start or end point of the risk zone RZ of traffic sign 14.

[0134] like Figure 6 As shown in (b), the display unit of the advanced driver assistance system can be configured to display the risk zone RZ of traffic sign 14 such that the risk zone RZ is divided into segments of different colors, shades, and / or patterns, wherein the segments correspond to continuous risk ranges. The risk zone RZ can be divided into three segments, wherein the boundaries of the three segments correspond to four estimated risks estimated for the four virtual positions VP1, VP2, VP3, and VP4 of vehicle 11. The segmentation of the risk zone RZ between virtual position VP1 and virtual position VP2 ( Figure 6 (b) shows the densest dashed segment, which corresponds to a risk range that is less than or equal to the risk estimated for the current time and for virtual location VP1, but greater than the risk estimated for the current time and for virtual location VP2. The continuous segments of the risk zone RZ between virtual location VP2 and virtual location VP3 ( Figure 6 (b) shows the second dense dashed segment, which corresponds to a risk range that is less than or equal to the risk estimated for the current time and for virtual location VP2, but greater than the risk estimated for the current time and for virtual location VP3. The continuous segmentation of the risk zone RZ between virtual location VP3 and virtual location VP4 ( Figure 6 (b) shows the least dense dashed segment, which corresponds to a risk range that is less than or equal to the risk estimated for the current time and for the virtual location VP3, but greater than the risk estimated for the current time and for the virtual location VP4.

[0135] Therefore, as Figure 6 As shown in (b), the risk zone RZ of traffic sign 14 corresponds to the personal space of traffic sign 14. The risk zone RZ of traffic sign 14 (when displayed on the display unit) allows the driver of vehicle 11 to visually assess the risk of vehicle 11 with respect to traffic sign 14 by visualizing the theoretical risk of traffic sign 14 with respect to virtual locations VP1, VP2, VP3, and VP4. Virtual locations VP1, VP2, VP3, and VP4 differ from the actual location AP1 of vehicle 11 at the current time, and are spatially closer to traffic sign 14, especially compared to the actual location AP1 of vehicle 11 at the current time.

[0136] The description of the advanced driver assistance system according to the first aspect of the present invention Figure 3 , Figure 4 , Figure 5 and Figure 6 The illustrated embodiments are correspondingly effective.

[0137] Figures 7 to 12 An example of output that can be displayed by a display unit of an embodiment of an advanced driver assistance system according to a first aspect of the invention is shown.

[0138] exist Figures 7 to 12 In this context, a vehicle driven by a driver assisted by an advanced driver assistance system according to an embodiment of the present invention (autonomous vehicle) is associated with reference marker "11". Risk zones characterized by features present in the environment of vehicle 11 are associated with reference marker "RZ". For example... Figure 7 , Figure 8 and Figure 9 As shown, the different segments of the risk zone RZ are associated with reference markers “S1”, “S2”, “S3”, and “S4”. Figure 7 , Figure 8 , Figure 9 , Figure 10 and Figure 11 In this context, one or two features of the environment of vehicle 11, determined by the processing unit of the advanced driver assistance system, correspond to another vehicle 12 (see [reference]). Figure 7 , Figure 10 and Figure 11 ) or two other vehicles 12 and 15 (see Figure 8 and Figure 9 ).exist Figure 10 In this context, the additional environmental features of the vehicle 11 determined by the processing unit correspond to an indicator 14 that indicates at least one traffic rule. Figure 11 In this context, the additional environmental features of vehicle 11 determined by the processing unit correspond to street intersection 14. Figure 12 In this context, the environmental characteristics of vehicle 11 determined by the processing unit correspond to street curves 13.

[0139] As in Figure 7 As exemplarily shown, the display unit can display the risk zone RZ of another vehicle 12 traveling in front of vehicle 11 (autonomous vehicle), wherein the risk zone RZ has been generated by the processing unit of the advanced driver assistance system. Figure 7 As shown, the risk zone RZ of the additional vehicle 12 may include the additional vehicle 12. Therefore, the risk with respect to the additional vehicle 12 can be estimated at one or more virtual locations of vehicle 11, said virtual locations being arranged not only in the area between vehicle 11 and the additional vehicle 12, but also on the side and / or front of the additional vehicle 12. Furthermore, as... Figure 7 As shown, the display unit can be configured to display information INF (e.g., instructions to decelerate, brake, etc.) instructing the driver's driving behavior. The instructions may depend on the actual position of vehicle 11 relative to the risk zone RZ of another vehicle 12; specifically, the instructions may depend on the distance between the actual position of vehicle 11 and the boundary of the risk zone RZ of the other vehicle 12. For example, an arrow INF may instruct the driver of vehicle 11 to decelerate, wherein the color and / or size of the arrow INF may indicate different speed reduction magnitudes.

[0140] Figure 8 In essence, it corresponds to Figure 7 The auxiliary vehicle 15 traveling on the left side of vehicle 11 is identified by the processing unit of the advanced driver assistance system as another feature of the environment of vehicle 11. Therefore, the processing unit also generates a risk zone RZ for the auxiliary vehicle 15, and the display unit displays another vehicle 12 with the corresponding risk zone RZ and the auxiliary vehicle 15 with the corresponding risk zone RZ.

[0141] Figure 9 In essence, it corresponds to Figure 8 Among them Figure 9 The current time no longer displays additional vehicle 15 (in Figure 8 (As shown in the image), and only a portion of the risk zone RZ for add-on vehicle 15 is displayed. This may be due to add-on vehicle 15 being in... Figure 9 The current time is no longer located on the left side of vehicle 11 due to the movement of vehicle 11. For example... Figure 9 As shown, the display unit can additionally display the estimated travel path EP of the vehicle 11.

[0142] Figure 10An exemplary scenario is illustrated where vehicle 11 (autonomous vehicle) is traveling to a street intersection. The processing unit of the advanced driver assistance system identifies another vehicle 12 traveling from the right side of vehicle 11 to the street intersection as a feature present in the environment of vehicle 11. The travel direction of vehicle 11 intersects perpendicularly with the travel direction of the other vehicle 12. The processing unit also identifies an indicator 14 indicating at least one traffic rule (e.g., road markings and / or traffic signs) as another feature present in the environment of vehicle 11. The processing unit can determine a risk zone RZ for each feature (i.e., the other vehicle 12 and the indicator 14 indicating at least one traffic rule). Figure 10 As shown, the display unit can display the additional risk zone RZ of vehicle 12 and the risk zone indicated by indicator 14 of at least one traffic rule. The display unit can also display additional information, such as the type of indicator 14. Figure 10 In the example, indicator 14 is a traffic sign that requires drivers to give way to another vehicle at a street intersection.

[0143] Figure 11 Corresponding to Figure 10 ,in Figure 11 and Figure 10 The difference is that the processing unit generates a risk zone RZ for street intersection 14, rather than a risk zone for an indicator that indicates at least one traffic rule. Therefore, according to Figure 11 In addition to the risk zone of the other vehicle 12, the display unit also displays the risk zone RZ of the street intersection 14.

[0144] Figure 12 An exemplary scenario is illustrated in which a vehicle 11 (autonomous vehicle) is traveling on a street including a street curve 13 located in front of the vehicle 11. The processing unit can identify the street curve 13 as a feature present in the environment of the vehicle 11. Furthermore, the processing unit can determine a risk zone RZ of the street curve 13, and the display unit can display the risk zone RZ of the street curve 13. The display unit can display additional information INF. This additional information may, for example, indicate the location within the risk zone RZ of the street curve 13 where the risk associated with the street curve (e.g., the risk of lateral acceleration caused by the curvature of the street curve 13) is greatest. Figure 12 As shown, the display unit can additionally display the estimated travel path EP of the vehicle 11.

[0145] right Figure 3 (a) and Figure 4 The explanation for Figure 7 , Figure 8 and Figure 9 The illustrated embodiment can be effective. Figure 3 of (b) Figure 5 and Figure 6 The explanation of (b) is for Figure 10 and Figure 11 The illustrated embodiment can be effective. Figure 6 The explanation of (a) is for Figure 12 The illustrated embodiments are effective.

[0146] The description of the advanced driver assistance system according to the first aspect of the present invention is as follows: Figures 7 to 12 The illustrated embodiments are correspondingly effective.

Claims

1. An advanced driver assistance system for assisting the driver of a vehicle, wherein... The advanced driver assistance system includes a sensor unit, a processing unit, and a display unit; The sensor unit is configured to sense the environment of the vehicle and provide the sensing output to the processing unit; The processing unit is configured to determine at least one feature of the environment based on the sensing output; The processing unit is configured to determine the risk zone of the feature for the current time using the following method: Based on at least one parameter of the vehicle at the current time, a corresponding risk regarding the feature is estimated at each of two or more virtual locations of the vehicle, to estimate two or more risks for the two or more virtual locations, and The risk zone is formed based on the two or more risks mentioned above; as well as The display unit is configured to display the environment of the vehicle having the aforementioned characteristics and the risk zone having those characteristics. The processing unit is further configured to determine the risk zone of the feature for the current time using the following method: An iterative risk assessment process is performed on multiple virtual locations, including or corresponding to the two or more virtual locations of the vehicle, to estimate the two or more risks for the two or more virtual locations, wherein... In each iteration of the risk estimation process, based on the vehicle's at least one parameter at the current time, a corresponding risk regarding the feature is estimated at a corresponding virtual location among the plurality of virtual locations, and The risk estimation process stops after an iteration in which the corresponding risk is equal to or less than the risk threshold.

2. The advanced driver assistance system according to claim 1, wherein... If the processing unit determines two or more features of the environment, then: The processing unit is configured to determine the risk zone for at least one of the two or more features for the current time, and The display unit is configured to display the environment of the vehicle having two or more of the features and the risk zone of one of the two or more features; or The processing unit is configured to determine the risk region for each of the two or more features for the current time, and The display unit is configured to display the environment of the vehicle having two or more of the features and the risk zone having at least one of the two or more features.

3. The advanced driver assistance system according to claim 1 or 2, wherein the feature includes or corresponds to at least one of the following Obstacles present in the environment The street characteristics present in the environment, and Indicators that show traffic rules.

4. The advanced driver assistance system according to claim 3, wherein The obstacles include other vehicles and people. The street characteristics include street curves, street intersections, street slopes exceeding a slope threshold, weather-affected street areas, and street areas with damaged surfaces. The indicators that indicate traffic rules include traffic signs, road markings, and traffic lights.

5. The advanced driver assistance system according to claim 1 or 2, wherein the at least one parameter includes or corresponds to at least one of the following The direction of travel of the vehicle The speed of the vehicle The acceleration of the vehicle, The acceleration time of the vehicle, The braking time of the vehicle, The size of the vehicle, and The shape of the vehicle.

6. The advanced driver assistance system according to claim 1 or 2, wherein, depending on the type of the feature, the corresponding risk includes or corresponds to at least one of the following risks: Risk based on time, and Probabilistic risk.

7. The advanced driver assistance system according to claim 1 or 2, wherein, depending on the type of the feature, the corresponding risk includes or corresponds to at least one of the following risks: Collision, if the feature corresponds to an obstacle, street intersection, or indicator indicating at least one traffic rule in the environment, wherein the indicator indicating at least one traffic rule is a traffic sign, a road marking on the street, or a traffic light; Lane departure if the features are street curves, street intersections, street slopes greater than a slope threshold, weather-affected street areas, or street areas with damaged surfaces. Lateral acceleration, if the feature is a street curve or street intersection; Acceleration, if the feature is a street slope greater than the slope threshold; The loss of control of the vehicle is characterized by street bends, street intersections, street slopes greater than the slope threshold, weather-affected street areas, or street areas with damaged surfaces. A violation of traffic rules if the feature corresponds to an indicator that indicates at least one traffic rule, wherein the indicator indicating at least one traffic rule is a traffic sign, a road marking, or a traffic light; and The vehicle is damaged if the feature corresponds to a street area with a damaged surface.

8. The advanced driver assistance system according to claim 1 or 2, wherein the virtual position of the vehicle forms a grid.

9. The advanced driver assistance system of claim 1 or 2, wherein the processing unit is configured to arrange the virtual location in the environment: Based on the type of the described feature, at least one of the following arrangements may be made; Arranged such that the virtual location is at least located between the vehicle and the feature in the region at the current time; Arranged such that at least one of the virtual locations is equal to the actual location of the feature at the current time or a portion of the actual location of the feature at the current time, and Arranged such that at least one of the virtual locations is equal to a location associated with the actual location of the feature at the current time or a location associated with a portion of the feature at the actual location at the current time.

10. The advanced driver assistance system of claim 1 or 2, wherein the processing unit is configured to arrange the virtual position in the environment such that the virtual position is arranged along at least one of the following paths: The path is the path between the vehicle and the feature at the current time. The path is the estimated travel path of the vehicle at the current time. If the feature is a movable obstacle, then the path is the estimated movement path of the feature. The path is suitable for the street where at least one of the vehicle and the movable obstacle is located at the current time. The path is suitable for the features, and The path is provided by map data.

11. The advanced driver assistance system according to claim 1 or 2, wherein The display unit is configured to display the risk area of ​​the feature by at least one of the following methods: The color, shadow, and pattern of the risk zone are changed based on the distance between the boundary of the risk zone and the actual position of the vehicle at the current time. The risk area is divided into segments of at least one of different colors, shades, and patterns, wherein the segments correspond to continuous risk ranges; and This makes the risk zone suitable for at least one of the feature and the street where the feature is located at the current time.

12. The advanced driver assistance system according to claim 1 or 2, wherein if the feature is a street curve, then: The processing unit is configured to estimate, based on at least one parameter of the vehicle at the current time, the lateral acceleration caused by the curvature of the street curve at each of the vehicle's virtual positions as the corresponding risk, wherein... The virtual location is equal to the actual location of different parts of the street bend.

13. The advanced driver assistance system according to claim 1 or 2, wherein if the feature is an indicator indicating at least one traffic rule, then: The processing unit is configured to estimate a collision risk as the corresponding risk at each of the plurality of virtual locations of the vehicle based on at least one parameter of the vehicle at the current time; wherein The plurality of virtual locations are arranged at least in the area between the vehicle and the feature, and at least one of the plurality of virtual locations is equal to the actual location of the feature or the actual location of a portion of the feature, or The plurality of virtual locations are arranged at least in the area between the vehicle and the feature, and at least one of the plurality of virtual locations is equal to a location associated with the actual location of the feature or a location associated with the actual location of a portion of the feature.

14. The advanced driver assistance system according to claim 1 or 2, wherein The processing unit is configured to determine at least one of driving behavior suggestions, driving behavior instructions, and warnings based on at least one of the two or more risks and the risk zone of the at least one feature, and The display unit is configured to display at least one of the driving behavior suggestions, the driving behavior instructions, and the warnings.

15. The advanced driver assistance system of claim 1 or 2, wherein the display unit is configured to display the at least one feature and the risk zone of the at least one feature by at least one of the following: A two-dimensional bird's-eye view of the environment of the vehicle is shown. Displayed in the first-person perspective included in the virtual reality display of the environment of the vehicle; Displayed from a first-person perspective using an augmented reality display; and The at least one feature and the risk area of ​​the at least one feature are projected onto a two-dimensional plane of the street on which the vehicle travels, and the at least one feature and the risk area of ​​the at least one feature are constrained by the street geometry.

16. A vehicle comprising an advanced driver assistance system as described in any one of claims 1 to 15 for assisting the driver of the vehicle.

17. A method for assisting a driver of a vehicle, wherein the method comprises: Sensing the environment of the vehicle and providing sensing output; At least one feature of the environment is determined based on the sensing output; The risk zone of the feature at the current time is determined by the following method: Based on at least one parameter of the vehicle at the current time, a corresponding risk regarding the feature is estimated at each of two or more virtual locations of the vehicle, to estimate two or more risks for the two or more virtual locations, and The risk zone is formed based on the two or more risks mentioned above; as well as The environment of the vehicle exhibiting the aforementioned characteristics and the risk zone exhibiting those characteristics are displayed. The risk zone for the feature determined by the processing unit for the current time includes: An iterative risk assessment process is performed on multiple virtual locations, including or corresponding to the two or more virtual locations of the vehicle, to estimate the two or more risks for the two or more virtual locations, wherein... In each iteration of the risk estimation process, based on the vehicle's at least one parameter at the current time, a corresponding risk regarding the feature is estimated at a corresponding virtual location among the plurality of virtual locations, and The risk estimation process stops after an iteration in which the corresponding risk is equal to or less than the risk threshold.

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