CN116923432A

CN116923432A - Driving assistance system and method

Info

Publication number: CN116923432A
Application number: CN202210333655.5A
Authority: CN
Inventors: 郝黎明
Original assignee: Bayerische Motoren Werke AG
Current assignee: Bayerische Motoren Werke AG
Priority date: 2022-03-30
Filing date: 2022-03-30
Publication date: 2023-10-24

Abstract

The invention provides a driving assistance system and a driving assistance method, wherein the driving assistance system comprises the steps of acquiring the real-time position of a vehicle; in response to detecting that a vehicle is traveling proximate a risk area, the risk area is prompted to a driver of the vehicle, wherein the risk area is identified based on a brake trace density. Identifying the risk area further includes collecting brake trace data of the road surface; determining a brake trace density for the different regions based on the collected brake trace data, wherein the brake trace density indicates a number of collected brake traces within each region; and identifying an area having a brake mark density reaching a threshold as a risk area.

Description

Driving assistance system and method

Technical Field

The present invention relates to the field of road traffic safety technology, and more particularly, to a driving assistance system and method.

Background

In recent years, more and more automobiles are equipped with various degrees of driving assistance systems and automatic driving capabilities (L0 to L5). Such vehicles are typically equipped with a plurality of different types of sensors, such as cameras, radars, lidars, etc., so that the vehicle may acquire ambient sensing information via its own onboard sensors, thereby providing support for driving assistance and automated driving decisions.

However, current driving assistance or autopilot functions rely mainly on real-time sensing and computation of sensors in sensing road traffic safety risk, and therefore have extremely high performance requirements for various sensing devices and onboard processors, resulting in very expensive overall cost of the vehicle.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

In view of the above, the present invention provides a driving assistance system and method based on brake mark density. The brake trace information on the road surface can be acquired through the vehicle-mounted sensing equipment, and the risk area can be identified through analyzing the brake trace density of different areas. When the vehicle is traveling near the risk area, the driving assistance system may issue a reminder to the driver, thereby reducing traffic safety risks.

According to one aspect of the present invention, there is provided a method for identifying a traffic safety risk area, comprising:

collecting brake trace data of a road surface;

determining a brake trace density for the different regions based on the collected brake trace data, wherein the brake trace density indicates a number of collected brake traces within each region; and

areas with a brake mark density reaching a threshold are identified as risk areas.

According to a further embodiment of the invention, the areas are dynamically divided according to collected brake trace data.

According to a further embodiment of the invention, the method further comprises:

verifying the identified risk area; and

and adjusting the identification of the risk area based on the verification result.

and marking the risk level of the risk area according to the brake trace density.

According to another aspect of the present invention, there is provided a system for identifying traffic safety risk areas, comprising:

a brake trace data collection module configured to collect brake trace data of a road surface;

a brake trace density determination module configured to determine brake trace densities for different regions based on collected brake trace data, wherein the brake trace densities indicate a number of collected brake traces within each region; and

a risk region identification module configured to identify a region having a brake trace density reaching a threshold as a risk region.

According to a further embodiment of the invention, the system further comprises:

and the brake trace identification training module is configured to train a brake trace identification model for identifying brake traces from road surface images acquired by the vehicle-mounted sensor and acquiring corresponding brake trace data.

According to yet another aspect of the present invention, there is provided a method for driving assistance, comprising:

acquiring a real-time position of a vehicle; and

in response to detecting that a vehicle is traveling proximate a risk area, the risk area is prompted to a driver of the vehicle, wherein the risk area is identified based on a brake trace density.

acquiring a planning path for navigation;

determining whether a risk area is included in the planned path; and

in response to determining that the planned path includes a risk region, marking the included risk region in a navigation interface.

acquiring safe vehicle speeds of risk areas, wherein the safe vehicle speeds are estimated based on brake trace data in each risk area; and

in response to detecting that the vehicle is traveling proximate to a risk zone and that the real-time vehicle speed is above a safe vehicle speed for the risk zone, the risk zone and a corresponding safe vehicle speed are prompted to a driver of the vehicle.

According to still another aspect of the present invention, there is provided a vehicle including:

a positioning unit configured to determine a real-time position of the vehicle; and

a navigation unit configured to:

acquiring a planning path for navigation;

determining whether a risk area is included in the planned path, wherein the risk area is identified based on a brake trace density; and

According to a further embodiment of the invention, the navigation unit is further configured to:

acquiring a safe vehicle speed of each risk area, wherein the safe vehicle speed is estimated based on brake trace data in each risk area; and

in response to detecting that the vehicle is traveling near a risk zone and the real-time vehicle speed is above a safe vehicle speed for the risk zone, the risk zone and the corresponding safe vehicle speed are prompted to a driver of the vehicle.

According to a further embodiment of the invention, the vehicle further comprises a sensor and a communication module, wherein:

the sensor is configured to collect brake trace data of a road surface;

the positioning unit is configured to provide position data associated with the brake trace data; and is also provided with

The communication module is configured to report the brake trace data with associated location data to a server associated with the vehicle.

According to a further embodiment of the invention, the sensor is further configured to collect road surface information of the risk area in response to a risk area verification instruction received by the communication module from the server; and is also provided with

And the communication module reports the road pavement information to the server.

According to a further embodiment of the invention, the vehicle further comprises:

an autopilot unit configured to: and adjusting an automatic driving strategy according to the risk area contained in the planned path.

According to a further embodiment of the invention, adjusting the autopilot strategy comprises at least one of the following actions:

adjusting a speed of the vehicle based on a safe speed of the risk zone;

adjusting a lane of travel of the vehicle based on the risk zone; and

and adjusting the level of automatic driving based on the risk area. Compared with the prior art, the driving assistance system and the driving assistance method provided by the invention have at least the following advantages:

1. the traffic safety risk area can be effectively marked based on the brake trace;

2. can be combined with map or navigation application to prompt the driver in time when the vehicle is driving near the risk area;

3. the scheme does not depend on the real-time operation capability of the sensor and the processor, reduces the hardware cost, and can be widely applied to various vehicles.

These and other features and advantages will become apparent upon reading the following detailed description and upon reference to the associated drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed.

Drawings

So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only certain typical aspects of this invention and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects.

FIG. 1 is an exemplary architecture diagram of a system for providing brake trace based driving assistance in accordance with one embodiment of the present invention.

FIG. 2 illustrates an example flow chart of a method for identifying traffic safety risk areas according to one embodiment of this disclosure.

Fig. 3 shows an example of setting different risk levels for risk areas.

FIG. 4 illustrates an example block diagram of a system for identifying traffic safety risk areas according to one embodiment of this disclosure.

FIG. 5 illustrates a schematic diagram of a training process of a brake trace identification model according to one embodiment of the invention.

Fig. 6 shows an example flow chart of a method for driving assistance according to an embodiment of the invention.

FIG. 7 illustrates an example of a navigation interface according to one embodiment of the invention.

Fig. 8 shows an exemplary structural diagram of a vehicle according to an embodiment of the present invention.

Detailed Description

The features of the present invention will become more apparent from the detailed description set forth below when taken in conjunction with the drawings.

In a road surface environment, a vehicle brake trace may sometimes be observed on the road surface. Such brake marks are caused by the fact that the driver takes an emergency braking (so-called "sudden braking") during driving, causing a strong friction between the car tyre and the road surface. Therefore, in general, there is a clear correlation between the left brake trace on the road surface and various sudden conditions during driving, and some experienced drivers can recognize the existence of traffic safety risks by observing the brake trace. However, no inexperienced driver, nor current driving assistance or autopilot technology, effectively uses this information to address traffic safety risks.

To this end, the present invention provides a brake trace based driving assistance system and method. FIG. 1 is an exemplary architecture diagram of a system for providing brake trace based driving assistance in accordance with one embodiment of the present invention. As shown in fig. 1, there are a plurality of vehicles (101 a,101b, …,101 n) traveling on a road, some of which may be equipped with sensors for environmental detection capability, such as cameras capable of capturing road pictures. These cameras may be configured with the ability to identify brake marks on the road. When a brake trace is identified, the vehicle may report the relevant information directly or indirectly to an associated server (102). For example, the vehicle 101a has the ability to identify a brake trace, and upon a road segment in the road map, the sensor identifies a brake trace 103 left on the road surface, and then reports this information to the server 102, such as photographs, places, and opportunities related to the brake trace 103.

The owner or deployment party of the server (102) may be a map service provider, a navigation service provider, a whole car manufacturer, or any entity providing a brake trace data collection and analysis service. The server (102) may analyze collected braking trails reported by the vehicle, such as analyzing the density of braking trails present in different areas, thereby identifying traffic safety risk areas. The information about the risk area can be provided to the vehicle so that when the vehicle runs near the risk area, a prompt can be timely sent to the driver, thereby avoiding traffic safety accidents. It should be appreciated that information regarding the risk area may be provided to any vehicle and is not limited to vehicles having brake trace data acquisition and reporting capabilities. Additionally, information regarding the risk area may also be provided to the driver's mobile device, such as by a navigation application installed on the mobile device receiving and displaying relevant information and providing a reminder to the driver.

FIG. 2 illustrates an example flow chart of a method 200 for identifying traffic safety risk areas according to one embodiment of this disclosure. In one example, the method 200 may be performed by a server of a map/navigation service provider, a vehicle manufacturer, or other suitable entity. As shown in FIG. 1, the method 200 begins at step 202 by collecting brake trace data for a road surface. As mentioned previously, the brake trace data may originate primarily from the reporting of the vehicle. Additionally, the brake trace data may also be provided by a traffic administration, road maintenance, insurance company, or user, such as providing a photographed live photo and location information. In addition, the reported information generally also contains time information to indicate the time at which the brake trace is present.

At step 204, the brake trace density for the different areas is determined based on the collected brake trace data. The "brake mark density" may indicate the number of brake marks collected in each area. More specifically, the road may be divided into a plurality of areas according to a certain length or distance range, and the greater the number of braking marks collected in each area, the greater the braking mark density of that area. It will be appreciated that the same braking track may be reported to the server multiple times by different vehicles or different entities, and thus the number of different braking tracks in the area is counted when determining the braking track density.

In one example, the area may be statically partitioned, such as dividing the intersection into one area, and dividing each road into sections of area by length (e.g., 30 meters, 50 meters, 100 meters). The length of the specific adopted dividing regions can be uniform, and can be flexibly set according to different road grades, road speeds, road conditions and the lengths of the collected brake marks. For example, on a highway, an area may be divided every 200 meters, whereas on a general urban road, an area may be divided every 30 meters. Alternatively, the zones may be dynamically partitioned based on collected brake trace data. For example, an area is not required to be set for a road in which no braking trace is reported, and when new braking trace data in an undivided area is reported, an area can be immediately divided at the position of the braking trace, and the area can cover the whole braking trace and be widened in a certain length. The advantage of such dynamic zoning is that the server-side related effort can be significantly reduced, including taking into account zone zoning settings and adjustments, determining and updating the brake mark density for each zone, etc. After the areas are divided, the brake trace density of each area can be determined according to the number of the brake traces in the area.

In step 206, areas having a brake mark density that meets or exceeds a threshold are identified as risk areas. The threshold may be a predetermined value indicating the number of braking marks, which is generally effective to indicate the probability of emergency situations requiring sudden braking again at the same location. The lowest value of the threshold may be 1, meaning that whenever there is a reported brake trace, a location is identified as a risk area. Preferably, the threshold value may be 2 or more, i.e. it indicates that at least two or more braking marks are present at the same location, which is identified as a risk area. It will be appreciated that if there is only one braking trace somewhere, more is an occasional reflection, whereas if there are more than two braking traces somewhere, then the necessity is reflected to a certain extent that there are risk factors in the road environment that are prone to triggering traffic accidents, such as visual blind areas, in the vicinity of intersections, often pedestrians/non-motor vehicles/animals traversing the road, poor road conditions, falling rocks, sharp bends, etc. Further, it is understood that the lower the threshold value is set, the greater the number of risk areas. If the number of risk areas is excessive, which results in frequent reminding of the driver, the driver may be rather disliked and numbed, and the overall traffic efficiency of the traffic environment may be adversely affected. Based on the above consideration, the threshold value may be set to an appropriate value according to the actual situation, and different threshold values may also be set in different areas. When the brake mark density of a region meets or exceeds a threshold, then the region is identified as a risk region.

Optionally, a plurality of risk levels may be set for the risk area further based on the magnitude of the brake mark density. For example, different brake mark density thresholds may be set. Fig. 3 shows an example of setting different risk levels for risk areas. In the example of fig. 3, a fixed distance range of 100 meters is taken as the area division. In addition to the normal area where no brake marks are present, a two-level risk level and corresponding two thresholds are set, wherein the first threshold is 1 and the second threshold is 6. Accordingly, when the number of braking marks is 1 to 5, the area is identified as a general risk area or a risk area, and when the number of braking marks is 6 and above, the area is identified as a high risk area.

The brake trace may disappear naturally or be cleared manually after a period of time, but after reporting, the data record of the brake trace is stored in the system, so that even if the brake trace does not exist on the current road surface, an area can be identified as a risk area due to the historical brake trace data, and a reminder is given to the passing vehicle driver, which is also one of the purposes and effects of the invention. However, in some risk areas, after a certain time, the original risk factors may be eliminated. For example, in the case where traffic safety authorities have installed traffic lights in an area where a collision accident is likely to occur by crossing a road, or have installed a barrier in the middle of the road, the original risk potential is eliminated. In this case, the identification of the risk area is still preserved, which would affect the traffic efficiency and would lead to doubt about the accuracy of the identification of the risk area by the user. Thus, optionally, the method 200 may further include verifying the identified risk area and adjusting the identification of the risk area based on the verification result. In one example, the verification means may include sending instructions to vehicles passing through the area to collect and upload photographs or videos of the area's environment, such that a comparison with historical photographs or videos may be made to determine if any risk-eliminating measures have taken place. In addition, a time period length (for example, 1 year) can be set, and when no new brake trace data is reported within a period of time from the time when the brake trace of the area is reported last time, the risk area identification of the area can be canceled, namely, the area is restored to the common area. In examples where multiple risk levels are provided, adjusting the risk area identification also includes reducing, maintaining, or increasing the risk level.

In one embodiment, the information of the identified risk areas may be integrated into an existing digital map. In another embodiment, information of the identified risk areas may be provided to traffic management or road maintenance departments to take appropriate action, such as adding warning signs, adding traffic facilities (e.g., mirrors, barriers, etc.), road clearing maintenance, etc.

FIG. 4 illustrates an example block diagram of a system 400 for identifying traffic safety risk areas according to one embodiment of this disclosure. As previously mentioned, the system 400 may be a server of a map/navigation service provider, a whole car manufacturer, or other suitable entity, and configured to perform the method described above in connection with fig. 2.

As shown in fig. 3, the system 400 may include a brake trace data collection module 401, a brake trace density determination module 402, and a risk area identification module 403. The brake trace data collection module 401 may be configured to collect brake trace data of a road surface. The brake trace density determination module 402 may be configured to determine the brake trace density for different regions based on the collected brake trace data, wherein the brake trace density indicates the number of brake traces collected within each region. The risk region identification module 403 may be configured to identify a region having a brake trace density reaching a threshold as a risk region.

Optionally, the system 400 may further include a brake trace identification training module 404. The brake trace identification training module 404 may be configured to train a brake trace identification model that may be used to identify brake traces from road surface images acquired by the on-board sensors and to obtain corresponding brake trace data. Many sensors currently have the ability to identify different objects in the environment, such as vehicles, pedestrians, signal lights, etc. This is achieved by pre-training the object recognition model and loading the trained model into the sensor so that the sensor can recognize in real time. Thus, image recognition training may be similarly performed for brake marks, such as training based on neural networks. The training process includes collecting training data, which may be road surface images with actual brake marks captured by onboard sensors. The training data is then input into a neural network, such as convolutional neural network CNN. The training process may include image convolution, image pooling, fully connected network nodes, etc., as shown in fig. 5. The resulting brake trace identification model may be further optimized by back propagating BP. The trained brake trace identification model can be similarly applied to an on-board sensor, so that the brake trace identification model has the capability of identifying the brake trace in real time.

Fig. 6 shows an example flow chart of a method 600 for driving assistance according to an embodiment of the invention. In one example, the method 600 may be performed by a vehicle or a map or navigation application (hereinafter collectively referred to as a "navigation application") installed on a vehicle or mobile device. As shown in fig. 6, method 600 begins at step 602 with acquiring a real-time location of a vehicle. In one example, the real-time location of the vehicle may be obtained by an onboard positioning device (e.g., a satellite positioning device). Similarly, when the method is performed by a mobile device, the real-time position of the vehicle may be obtained by a positioning device of the mobile device.

Subsequently, in step 604, responsive to detecting that the vehicle is traveling proximate to the risk zone, the risk zone is prompted to a driver of the vehicle, wherein the risk zone is identified based on the brake trace density. As described previously, the cloud-located server has identified risk areas that present a security risk based on the collected brake trace data. Information about these risk areas may be provided to a vehicle or navigation application. The information may include at least location information of the risk area, and may also include, but is not limited to, a portion of the following: the attribute of the risk area (e.g., identified as an analysis area based on brake trace identification), the identifier of the risk area, the risk level of the risk area, the safe vehicle speed of the risk area, and so forth. Based on the location information of the risk zone and the real-time location of the current vehicle, it can be detected whether the vehicle has traveled close to the risk zone. The determination of "proximity" may be based on a pre-set criteria. For example, proximity may be determined when the real-time location of the vehicle is less than a certain value (e.g., 100 meters) from the center or edge location of the risk area. Alternatively, it may be determined whether the vehicle approaches the risk area further in conjunction with the speed of the vehicle, for example, when the current speed of the vehicle is high (for example, 120 km/h), it may be determined that the vehicle approaches at a distance of 200 meters from the center or edge position of the risk area. The purpose of this is that after the risk has been indicated to the user, the user can take appropriate measures, such as appropriate deceleration, observing the environment with a higher vigilance, moving the foot from the throttle up to the brake in preparation for braking, etc.

As one example, the driver of the vehicle may be prompted by various means when approaching the risk area. For example, a voice prompt may be provided to the driver via an audio system of the vehicle or mobile device, such as "200 meters ahead with a risk area, please drive carefully. Additionally or alternatively, a corresponding prompt may be displayed on the screen of the vehicle or mobile device. In an alternative example, such risk areas identified based on brake mark density may be named specifically, for example, as "brake high incidence area" or other names, similar to "accident ahead", "overspeed photo ahead", etc., so that the driver being alerted can intuitively understand the type of risk area and which corresponding measures should be performed.

In one embodiment, driving assistance may further include marking and prompting risk areas in the navigation application. For example, the method may further include obtaining a planned path for navigation. In a navigation scenario, a user may specify a departure point and a destination in a navigation application of an in-vehicle navigation system or a mobile device and request path planning. The planned path can be calculated by a cloud server or by a local application, so that the planned path is obtained.

Subsequently, it may be determined whether a risk area is included in the planned path, and when the risk area is present, the included risk area is marked in the navigation interface, as shown in fig. 7. In the example navigation interface of fig. 7, the current planned path is shown, along with candidate paths that may be selected and switched. The risk areas involved and their types are shown on each path, wherein the risk areas identified based on brake mark density according to the present invention, i.e. "brake high incidence area", are shown exaggerated on the right side of the navigation interface of fig. 7 for clarity of description. To distinguish from other types of risk areas, the example of fig. 7 is identified with a foot-operated brake pattern so that the user can intuitively understand that the risk area is a high brake area without additional text labeling. As another example of comparison and risk areas, a "truck alert area" is also shown in the example interface of fig. 7 that suggests that there are more trucks in the road segment.

In yet another embodiment, the driving assistance may further include a reminder of the safe vehicle speed. For this purpose, a safe vehicle speed in the risk zone can be obtained. The safe vehicle speed for each zone may be estimated based on the brake trace data in that zone. It will be appreciated that the length of the brake trace may generally reflect the distance required from the onset of emergency braking to the complete standstill of the vehicle, i.e., the braking distance, the higher the vehicle speed, the longer the braking distance. In addition, the stopping distance is also dependent on many factors such as vehicle type, load, tires, road surface, weather, etc. As a rough estimate, the average length of the brake marks in each zone can be counted and the average speed at which emergency braking is taken can be approximated. Since at this average vehicle speed, these vehicles have to take emergency braking at that time, it is considered that at this vehicle speed, it is not sufficient to safely cope with a possible emergency. Thus, the safe vehicle speed can be set to a value lower than this average vehicle speed, so that the vehicle can be braked in a more gentle braking posture with sufficient safe braking at this safe vehicle speed.

Then, whether the real-time vehicle speed of the vehicle when approaching the risk area is higher than the safe vehicle speed of the risk area is detected, and if so, a suggestion about the safe vehicle speed can be presented to the driver of the vehicle. For example, "200 meters ahead with a high braking zone, safe vehicle speed 80km/h, and slow down and carefully drive".

Fig. 8 shows an example structural diagram of a vehicle 800 according to one embodiment of the invention. As shown in fig. 8, the vehicle 800 may include a positioning unit 801 for determining a real-time position of the vehicle 800, and a navigation unit 802 for providing navigation or map services. The positioning unit 801 may be various commonly used vehicle-mounted positioning devices, such as devices supporting satellite positioning systems such as GPS, beidou, galileo, gnonas, etc. The navigation unit 802 may be an in-vehicle navigation system or a navigation application installed in an in-vehicle system. Navigation unit 802 may be configured to obtain a planned path for navigation; determining whether a risk area is included in the planned path, wherein the risk area is identified based on the brake trace density; and in response to determining that the planned path includes a risk region, marking the included risk region in the navigation interface.

Optionally, the navigation unit 802 may be further configured to: acquiring a safe vehicle speed of each risk area, wherein the safe vehicle speed is estimated based on brake trace data in each risk area; and in response to detecting that the vehicle 800 is traveling near a risk zone and the real-time vehicle speed is above the safe vehicle speed for the risk zone, prompting the driver of the vehicle for the risk zone and a corresponding safe vehicle speed.

Optionally, the vehicle 800 may also include one or more onboard sensors 803, thus providing environmental awareness capabilities. In-vehicle sensors 903 may include, but are not limited to: cameras, radars, lidar, and the like. The vehicle 800 may also include a communication module 804 for providing the vehicle with the ability to communicate with the outside world. The communication module 904 may support cellular data communication such as LTE, 5G, etc. to provide internet access capability to communicatively couple with an associated server.

In one example, the sensor 803 may be configured to collect brake trace data of the road surface, which may be randomly associated with real-time position data provided by the positioning unit 801 and reported to an associated server via the communication module 804.

In another example, the communication module 804 may receive a risk area verification instruction from a server. In response, the sensor 803 may collect road surface information for the risk area, such as taking pictures or videos of the surrounding environment or the road surface. Subsequently, the communication module 804 reports the road surface information to the server.

Optionally, the vehicle 800 may further comprise an autopilot unit 805 for providing autopilot capability to the vehicle 800. It should be noted that "autopilot" herein broadly refers to any level of autopilot that is capable of achieving, i.e., including all degrees of autopilot from the ability to achieve full autopilot of the vehicle to the ability to achieve only alert to the driver based on the received warning information. According to one example of the invention, the autopilot unit 805 may be configured to adjust the autopilot strategy according to risk areas contained in the planned path.

Adjusting the autopilot strategy may include, but is not limited to, the following adjustment measures:

1. adjusting the speed of the vehicle based on the safe speed of the risk zone, for example, when the current speed of the vehicle is higher than the safe speed of the front risk zone, the automatic driving unit can automatically reduce the speed below the safe speed;

2. adjusting a driving lane of the vehicle based on the risk area, for example, in a case where a granularity of position information of the risk area may be specific to the lane, the automatic driving unit may control the vehicle to change lanes to avoid the high-risk lane;

3. the level of autopilot is adjusted based on the risk area, e.g., the autopilot level may be reduced from a full autopilot mode to a mode requiring partial user intervention.

In addition, in the automatic driving mode, the in-vehicle system may also be configured to alert the driver and passengers for such areas of high brake development, thereby enabling the driver and passengers to prepare for possible emergency braking.

What has been described above includes examples of aspects of the claimed subject matter. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the claimed subject matter, but one of ordinary skill in the art may recognize that many further combinations and permutations of the claimed subject matter are possible. Accordingly, the disclosed subject matter is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims.

Claims

1. A method for identifying traffic safety risk areas, comprising:

collecting brake trace data of a road surface;

2. The method of claim 1, wherein the regions are dynamically partitioned based on collected brake trace data.

3. The method of claim 1, wherein the method further comprises:

verifying the identified risk area; and

4. The method of claim 1, wherein the method further comprises:

5. A system for identifying traffic safety risk areas, comprising:

6. The system of claim 5, wherein the system further comprises:

7. A method for driving assistance, comprising:

acquiring a real-time position of a vehicle; and

8. The method of claim 7, wherein the method further comprises:

acquiring a planning path for navigation;

determining whether a risk area is included in the planned path; and

9. The method of claim 7, wherein the method further comprises:

10. A vehicle, characterized by comprising:

a navigation unit configured to:

acquiring a planning path for navigation;

11. The vehicle of claim 10, wherein the navigation unit is further configured to:

12. The vehicle of claim 10, further comprising a sensor and a communication module, wherein:

the sensor is configured to collect brake trace data of a road surface;

13. The vehicle of claim 12, wherein the sensor is further configured to collect road surface information for the risk area in response to a risk area verification instruction received by the communication module from the server; and is also provided with

The communication module is further configured to report the road surface information to the server.

14. The vehicle of claim 10, characterized in that the vehicle further comprises:

15. The vehicle of claim 14, wherein adjusting the autopilot strategy comprises at least one of:

adjusting a speed of the vehicle based on a safe speed of the risk zone;

adjusting a lane of travel of the vehicle based on the risk zone; and

and adjusting the level of automatic driving based on the risk area.