CN109739230B - Driving track generation method and device and storage medium - Google Patents
Driving track generation method and device and storage medium Download PDFInfo
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- CN109739230B CN109739230B CN201811631342.8A CN201811631342A CN109739230B CN 109739230 B CN109739230 B CN 109739230B CN 201811631342 A CN201811631342 A CN 201811631342A CN 109739230 B CN109739230 B CN 109739230B
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Abstract
The application provides a driving track generation method, a device and a storage medium, wherein the method is suitable for a hot standby module in a vehicle-mounted control system, and the vehicle-mounted control system comprises the following steps: the system comprises a main control module and a hot standby module, wherein the method comprises the following steps: when the master control module is determined to work abnormally, a reference information set is obtained, wherein the reference information set comprises: and generating a driving track of the automatic driving vehicle according to effective reference information in the reference information set, wherein the effective reference information is reference information with the confidence coefficient higher than a preset threshold value. According to the technical scheme, the hot standby module generates a driving track according to the acquired reference information set, the driving track can be used for controlling the automatic driving vehicle to safely drive, and the potential safety hazard problem which may occur when a vehicle-mounted control system of the automatic driving vehicle breaks down is solved.
Description
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a driving trajectory generation method, device, and storage medium.
Background
The automatic driving vehicle is also called as unmanned vehicle, and is an intelligent vehicle which realizes unmanned driving through a computer system, and the computer system of the vehicle can automatically and safely operate the vehicle under the unmanned operation condition by means of the cooperative cooperation of artificial intelligence, visual calculation, radar, a monitoring device and a global positioning system.
In the prior art, when an automatic driving vehicle runs, a vehicle-mounted control system may have a fault problem, so that driving behaviors of the automatic driving vehicle are abnormal, and hidden dangers of traffic accidents exist. Therefore, it is desirable to provide a method for solving the potential safety hazard that may occur when the vehicle-mounted control system of the autonomous vehicle fails.
Disclosure of Invention
The application provides a driving track generation method, a driving track generation device and a storage medium, which are used for solving the problem that potential safety hazards can occur when a vehicle-mounted control system of an automatic driving vehicle breaks down.
A driving track generating method provided in a first aspect of the present application is applicable to a hot standby module in a vehicle-mounted control system, where the vehicle-mounted control system includes: the method comprises the following steps:
when the master control module is determined to work abnormally, a reference information set is obtained, wherein the reference information set comprises: lane line information, obstacle information, and vehicle state information of at least one vehicle;
and generating a driving track of the automatic driving vehicle according to effective reference information in the reference information set, wherein the effective reference information is the reference information with the confidence coefficient higher than a preset threshold value.
In one possible design of the first aspect, before the generating the driving trajectory of the autonomous vehicle according to the valid reference information in the reference information set, the method further includes:
determining a confidence level of each reference information in the reference information set;
and eliminating invalid reference information with the confidence coefficient lower than the preset threshold value in the reference information set, and reserving the valid reference information with the confidence coefficient higher than the preset threshold value in the reference information set.
In another possible design of the first aspect, the generating a driving track of an autonomous vehicle according to the valid reference information in the reference information set includes:
determining a target path track of the automatic driving vehicle according to effective lane line information in the effective reference information and effective vehicle state information of the automatic driving vehicle;
determining target speed information of the autonomous vehicle according to effective obstacle information in the effective reference information and effective vehicle state information of the at least one vehicle;
generating the driving trajectory based on the target path trajectory and the target speed information.
In the foregoing possible design of the first aspect, the determining the target path trajectory of the autonomous vehicle according to the effective lane line information in the effective reference information and the effective vehicle state information of the autonomous vehicle includes:
determining a lane central line of a lane on which the automatic driving vehicle runs and a feasible interval range of the lane according to the effective lane line information;
determining a preset number of reference points in the lane by taking the lane central line as a reference according to the effective vehicle state information of the automatic driving vehicle;
and generating a target path track of the automatic driving vehicle within the feasible interval range of the lane based on the smooth characteristic of the driving track and the positions of the preset number of reference points.
In still another possible design of the first aspect, the at least one vehicle includes: the autonomous vehicle, a first vehicle obstacle in a lane where the autonomous vehicle is located and/or a second vehicle obstacle in an adjacent lane and having a collision risk.
In the above possible design of the first aspect, the determining target speed information of the autonomous vehicle according to the effective obstacle information in the effective reference information and the effective vehicle state information of the at least one vehicle includes:
determining a first acceleration of the autonomous vehicle from uniform deceleration of the current speed to stop according to the current speed of the autonomous vehicle and a preset brake-stop time of the autonomous vehicle in the effective vehicle state information;
determining a second acceleration at which the autonomous vehicle decelerates from the current speed to a target vehicle speed that is a minimum speed in the first vehicle obstacle and/or the second vehicle obstacle according to effective vehicle state information of the first vehicle obstacle and/or effective vehicle state information of the second vehicle obstacle;
determining a third minimum acceleration of the autonomous vehicle when the autonomous vehicle stops before colliding with the fixed obstacle according to distance information of the autonomous vehicle and the fixed obstacle in the effective obstacle information;
and determining the target speed information based on the first acceleration, the second acceleration and the third acceleration, wherein the speed track corresponding to the target speed information is a track formed by uniform deceleration motion with the maximum value of the first acceleration, the second acceleration and the third acceleration as deceleration.
A second aspect of the present application provides a driving trajectory generation device, which is suitable for a hot standby module in a vehicle-mounted control system, where the vehicle-mounted control system includes: the master control module with the hot spare module, the device includes: the device comprises an acquisition module and a generation module;
the acquiring module is configured to acquire a reference information set when it is determined that the master control module is abnormal in operation, where the reference information set includes: lane line information, obstacle information, and vehicle state information of at least one vehicle;
the generating module is used for generating a driving track of the automatic driving vehicle according to effective reference information in the reference information set, wherein the effective reference information is reference information with confidence coefficient higher than a preset threshold value.
In one possible design of the second aspect, the apparatus further includes: a preprocessing module;
the preprocessing module is used for determining the confidence level of each reference information in the reference information set before the generating module generates the driving track of the automatic driving vehicle according to the effective reference information in the reference information set, eliminating invalid reference information of which the confidence level is lower than the preset threshold value in the reference information set, and reserving the effective reference information of which the confidence level is higher than the preset threshold value in the reference information set.
In another possible design of the second aspect, the generating module includes: a first determining unit, a second determining unit and a generating unit;
the first determination unit is used for determining a target path track of the automatic driving vehicle according to effective lane line information in the effective reference information and effective vehicle state information of the automatic driving vehicle;
the second determination unit is used for determining the target speed information of the automatic driving vehicle according to the effective obstacle information in the effective reference information and the effective vehicle state information of the at least one vehicle;
the generating unit is used for generating the driving track based on the target path track and the target speed information.
In the foregoing possible design of the second aspect, the first determining unit is specifically configured to determine a lane center line of a lane on which the autonomous vehicle travels and a feasible section range of the lane according to the valid lane line information, determine a preset number of reference points in the lane with the lane center line as a reference according to the valid vehicle state information of the autonomous vehicle, and generate the target path trajectory of the autonomous vehicle in the feasible section range of the lane based on a characteristic of driving trajectory smoothing and positions of the preset number of reference points.
In yet another possible design of the second aspect, the at least one vehicle includes: the autonomous vehicle, a first vehicle obstacle in a lane where the autonomous vehicle is located and/or a second vehicle obstacle in an adjacent lane and having a collision risk.
In the above possible design of the second aspect, the second determining unit is specifically configured to determine a first acceleration at which the autonomous vehicle decelerates from the current speed to a stop according to the current speed of the autonomous vehicle in the valid vehicle state information, a preset brake-off time of the autonomous vehicle, determine a second acceleration at which the autonomous vehicle decelerates from the current speed to a target vehicle speed according to the valid vehicle state information of the first vehicle obstacle and/or the valid vehicle state information of the second vehicle obstacle, the target vehicle speed being a minimum speed in the first vehicle obstacle and/or the second vehicle obstacle, determine a third acceleration at which the autonomous vehicle stops before colliding with a fixed obstacle in the valid obstacle information according to the distance information of the autonomous vehicle to the fixed obstacle, and determining the target speed information based on the first acceleration, the second acceleration and the third acceleration, wherein the speed track corresponding to the target speed information is a track formed by uniform deceleration motion with the maximum value of the first acceleration, the second acceleration and the third acceleration as deceleration.
A third aspect of the present application provides a driving trajectory generation device, comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method according to the first aspect as well as various possible designs of the first aspect.
A fourth aspect of the present application provides a storage medium having stored therein instructions that, when executed on a computer, cause the computer to perform the method as set forth in the first aspect and various possible designs of the first aspect.
A fifth aspect of the present application provides a chip for executing instructions, the chip being configured to perform the method according to the first aspect and various possible designs of the first aspect.
According to the driving track generation method, the driving track generation device and the storage medium, when the hot standby module determines that the main control module works abnormally, the hot standby module acquires a reference information set, wherein the reference information set comprises: and generating a driving track of the automatic driving vehicle according to effective reference information in the reference information set, wherein the effective reference information is reference information with the confidence coefficient higher than a preset threshold value. According to the technical scheme, the hot standby module generates a driving track according to the acquired reference information set, the driving track can be used for controlling the automatic driving vehicle to safely drive, the traffic accidents are avoided to a certain extent, and the potential safety hazard problem which possibly occurs when the vehicle-mounted control system of the automatic driving vehicle breaks down is solved.
Drawings
FIG. 1 is a schematic structural diagram of an on-board control system provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a first embodiment of a driving track generation method provided in the embodiment of the present application;
fig. 3 is a schematic flow chart of a second driving trajectory generation method provided in the embodiment of the present application;
fig. 4 is a schematic flow chart of a third embodiment of a driving trajectory generation method provided in the embodiment of the present application;
fig. 5 is a schematic flow chart of a fourth driving trajectory generation method provided in the embodiment of the present application;
FIG. 6 is a schematic view of a scene in which an autonomous vehicle is traveling;
fig. 7 is a schematic flow chart of a fifth embodiment of a driving trajectory generation method provided in the embodiment of the present application;
fig. 8 is a schematic structural diagram of a first driving trajectory generation device according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a second driving trajectory generation device according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of a third driving trajectory generation device according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of a fourth driving trajectory generation device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The driving track generation method provided by the embodiment of the application is applied to a vehicle-mounted control system of an automatic driving vehicle, and fig. 1 is a schematic structural diagram of the vehicle-mounted control system provided by the embodiment of the application. As shown in fig. 1, the in-vehicle control system includes: a main control module 11 and a hot standby module 12. When the main control module 11 fails, the running state of the autonomous vehicle is temporarily controlled by the hot standby module 12.
Optionally, the main control module 11 and the hot standby module 12 may be connected to each other, in a normal case, when the main control module 11 works normally, the main control module 11 controls the automatic driving of the automatic driving vehicle, the hot standby module 12 may assist the main control module 11 to control the driving state of the automatic driving vehicle, and when the main control module 11 works abnormally, the hot standby module 12 may replace the main control module 11 to control the driving state of the automatic driving vehicle, so as to ensure the safe driving of the automatic driving vehicle.
For example, in this embodiment, the main control module 11 is a system with higher performance, in a normal case, the vehicle-mounted control system only uses the main control module 11 to control the driving state of the autonomous vehicle according to the acquired vehicle state information and the real-time traffic information (including traffic congestion information and route restriction information) of the route traveled by the autonomous vehicle, and when detecting that the working state of the main control module 11 is abnormal, uses the hot standby module 12 to acquire a reference information set of a fault scene where the autonomous vehicle is located, and controls the driving state of the autonomous vehicle according to the driving track determined by the reference information set.
It should be noted that, in the embodiment of the present application, the hot standby module 12 may be a hardware platform with low cost and limited system resources, and the limited resources can replace the main control module 11 to temporarily work for a period of time when the main control module 11 fails, so as to avoid a traffic accident.
For example, in this embodiment, the vehicle-mounted control system may further include: a detection module 13 and an alarm module 14 connected with the main control module 11 and the hot standby module 12. The detecting module 13 may be configured to detect working states of the main control module 11 and the hot standby module 12, and the alarming module 14 may be configured to alarm when the working states of the main control module 11 and/or the hot standby module 12 are abnormal.
Optionally, the embodiment shown in fig. 1 is described by taking an example that the vehicle-mounted control system includes a main control module 11, a hot standby module 12, a detection module 13, and an alarm module 14. It should be noted that the onboard control system may also include other types of devices, such as a detection device, a control device, and a power supply device. The embodiment of the application does not limit the specific composition of the vehicle-mounted control system, and the specific composition can be determined according to actual conditions.
The embodiment of the application provides a driving track generation method, a driving track generation device and a storage medium, wherein when the master control module is determined to work abnormally, a hot standby module acquires a reference information set, and the reference information set comprises: and generating a driving track of the automatic driving vehicle according to effective reference information in the reference information set, wherein the effective reference information is reference information with the confidence coefficient higher than a preset threshold value. According to the technical scheme, the hot standby module generates a driving track according to the acquired reference information set, and the driving track can be used for controlling the automatic driving vehicle to safely drive, so that traffic accidents are avoided to a certain extent.
The technical solution of the present application will be described in detail below with reference to specific examples. It should be noted that the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
Fig. 2 is a schematic flow chart of a first embodiment of a driving trajectory generation method provided in the embodiment of the present application. The method is suitable for the hot standby module in the vehicle-mounted control system shown in FIG. 1. As shown in fig. 2, the driving trajectory generation method may include the steps of:
step 21: when the work abnormality of the main control module is determined, a reference information set is obtained, wherein the reference information set comprises: lane line information, obstacle information, and vehicle state information of at least one vehicle.
For example, in this embodiment, when the main control module of the on-board control system fails, in order to ensure that the autonomous vehicle can still normally run for a period of time, the hot standby module may detect, in real time, the surroundings of the autonomous vehicle, for example, lane line information of a lane on which the autonomous vehicle runs, obstacle information within a preset distance range from the autonomous vehicle, and vehicle state information of at least one vehicle, using sensors mounted on the vehicle. Optionally, at least one vehicle in this embodiment includes: autonomous vehicles themselves and other vehicles.
In this embodiment, the hot standby module of the vehicle-mounted control system may comprehensively determine scene information where the automatically-driven vehicle is located according to the acquired lane line information, the acquired obstacle information, and the vehicle state information of at least one vehicle, and generate a corresponding driving track.
For example, in this embodiment, the obstacle information may include: fixed obstacles located within the lane in which the autonomous vehicle is traveling, vehicles that are traveling but are at risk of colliding with the autonomous vehicle, etc. The embodiment does not limit the specific expression form of the obstacle information, and can be determined according to the actual situation.
For example, in one possible design, the hot standby module may acquire the reference information set through visual perception or obstacle perception by using a detection device disposed on the autonomous vehicle. Wherein, this detection equipment includes: laser equipment, camera equipment, radar equipment, infrared equipment.
By way of example, the detection device may include, but is not limited to, any of the following devices: radar equipment, laser equipment, camera equipment, radar equipment, infrared equipment. For example, the detection device may also be an ultrasonic detection device, a ranging detection device, a visual detection device, and the like, as well as combinations thereof.
In the embodiment, during the running process of the automatic driving vehicle, the vehicle-mounted control system can send detection signals to the surroundings through a detection device arranged on the automatic driving vehicle so as to acquire the relevant information of the object within the preset distance range of the automatic driving vehicle. For example, a detection device is used to scan surrounding objects, and lane line information, obstacle information, vehicle state information such as the position, speed, acceleration, and the like of each vehicle, and roadside traffic lights and road condition information are acquired by a visual perception or obstacle perception method, and are used as a reference information set for generating a driving track of an autonomous vehicle.
Step 22: and generating a driving track of the autonomous vehicle according to the effective reference information in the reference information set.
Wherein the valid reference information is the reference information with the confidence higher than a preset threshold.
In this embodiment, in order to enable an accurate driving track, the hot standby module may use reference information with a confidence level higher than a preset threshold as a reference to generate the driving track of the autonomous vehicle. For example, in this embodiment, the reference information in the reference information set whose confidence is higher than the preset threshold is referred to as valid reference information.
For example, the hot standby module may generate a target path trajectory based on the acquired effective lane line information and the effective vehicle state information of the at least one vehicle, and generate target speed information based on the acquired obstacle information and the effective vehicle state information of the at least one vehicle. The target path trajectory and the target speed information may be integrated into a driving trajectory of the autonomous vehicle.
It should be noted that, in this embodiment, the requirement of the hot standby module on hardware resources is low, and only the main control module needs to be ensured not to have a collision accident during the period from the time when the main control module abnormally operates to the time when the user takes over the autonomous vehicle, and to continue driving or stop along the original lane. Therefore, in the embodiment, the performance requirement on the hot standby module is not high, and the cost of the automatic driving vehicle is reduced.
Specifically, the driving track generation method of the embodiment is particularly suitable for high-speed automatic driving, and is switched to a scenario controlled by the hot standby module when the main control module fails. Because the hot standby module can generate safe and reasonable driving tracks based on the acquired reference information set, the resource overhead and the module complexity caused by iteration are reduced, the hardware resources required by the hot standby module for generating the driving tracks are less, the chip processing capacity is low, and the cost is reduced.
According to the driving track generation method provided by the embodiment of the application, the hot standby module acquires a reference information set when the master control module is determined to work abnormally, and the reference information set comprises: and generating a driving track of the automatic driving vehicle according to the effective reference information of the reference information of which the confidence coefficient is higher than a preset threshold value in the reference information set, the lane line information, the obstacle information and the vehicle state information of at least one vehicle. According to the technical scheme, the automatic driving vehicle continues to run according to the driving track generated by the hot standby module, so that traffic accidents can be avoided to a certain extent, and the safe running of the automatic driving vehicle is ensured.
Exemplarily, on the basis of the above embodiments, fig. 3 is a schematic flow diagram of a second driving trajectory generation method provided in the embodiment of the present application. As shown in fig. 3, in the present embodiment, before the step 22 (generating the driving track of the autonomous vehicle according to the effective reference information in the reference information set), the method may further include the steps of:
step 31: a confidence level is determined for each reference information in the set of reference information.
For example, in this embodiment, after the hot standby module obtains the reference information set of the autonomous vehicle through the detection device, the hot standby module analyzes lane line information, obstacle information, and vehicle state information of at least one vehicle, which are output by different detection devices at different times, and calculates a confidence of each reference information in the reference information set.
For example, for an obstacle on a lane on which the autonomous vehicle travels, the hot standby module may acquire a plurality of pieces of description information, which is output by the laser radar, the camera device, and the infrared device at a plurality of consecutive times, for the obstacle, determine correct information and error information in the plurality of pieces of description information by comprehensively analyzing the plurality of pieces of description information, and determine the confidence of the acquired obstacle information according to the proportion of the correct information and the error information in the plurality of pieces of description information.
Similarly, the confidence determining method for the lane line information in the reference information set and the vehicle state information of at least one vehicle is similar to the method for determining the confidence of the obstacle information, and is not repeated here.
Step 32: and eliminating invalid reference information with the confidence coefficient lower than a preset threshold value in the reference information set, and keeping the valid reference information with the confidence coefficient higher than the preset threshold value in the reference information set.
For example, in this embodiment, the hot standby module may set a threshold of the confidence level in advance, for example, a preset threshold is used as a boundary for determining the invalid reference information and the valid reference information, the reference information with the confidence level lower than the preset threshold is referred to as the invalid reference information, and the reference information with the confidence level higher than the preset threshold is referred to as the valid reference information.
In this embodiment, in order to improve the rationality and safety of the subsequently generated driving track, invalid reference information in the reference information set may be removed, and valid reference information in the reference information set may be retained, so that the hot standby module may generate the driving track of the autonomous vehicle only according to the valid reference information in the reference information set.
According to the driving track generation method provided by the embodiment of the application, the hot backup module determines the confidence level of each piece of reference information in the reference information set, eliminates invalid reference information of which the confidence level is lower than a preset threshold value in the reference information set, and retains the valid reference information of which the confidence level is higher than the preset threshold value in the reference information set, so that the hot backup module can generate the driving track only according to the valid reference information in the reference information set, the reasonability and the safety of the driving track are improved, and a foundation is laid for the safe driving of an automatic driving vehicle.
Exemplarily, on the basis of the above embodiments, fig. 4 is a schematic flow chart of a third embodiment of the driving trajectory generation method provided in the embodiment of the present application. As shown in fig. 4, in the present embodiment, the step 22 (generating the driving track of the autonomous vehicle according to the effective reference information in the reference information set) can be implemented by the following steps:
step 41: and determining the target path track of the automatic driving vehicle according to the effective lane line information in the effective reference information and the effective vehicle state information of the automatic driving vehicle.
In this embodiment, the hot standby module may first determine a reference line function of the autonomous vehicle on the driving lane and an interval range of the autonomous vehicle on the driving lane by using effective lane line information in the effective reference information, and then determine a preset driving distance within a preset time period by combining effective vehicle state information of the autonomous vehicle, so as to determine a target path track of the autonomous vehicle within the preset driving distance range.
For a specific implementation of this step 41, reference may be made to the following description of the embodiment shown in fig. 5, which is not described herein again.
Step 42: and determining target speed information of the automatic driving vehicle according to the effective obstacle information in the effective reference information and the effective vehicle state information of at least one vehicle.
In this embodiment, the hot standby module first divides the obstacle information on the driving lane or the adjacent lane where the autonomous vehicle is located into a plurality of cases, for example, a fixed obstacle and/or a vehicle obstacle having a collision risk exists in the driving lane of the autonomous vehicle, or a vehicle obstacle having a collision risk exists in the adjacent lane, by using the valid obstacle information in the valid reference information. Secondly, the running acceleration of the automatic driving vehicle under different conditions is respectively calculated according to the effective vehicle state information of at least one vehicle, and finally, the target speed information of the automatic driving vehicle is determined.
For a specific implementation of this step 42, reference may be made to the following description of the embodiment shown in fig. 7, which is not described herein again.
It should be noted that, in the embodiment of the present application, the execution sequence of step 42 and step 41 is not limited, the hot standby module may first execute step 42 to generate target speed information, and then execute step 41 to generate target track information, or may simultaneously execute step 41 and step 42 to obtain a target path track and target speed information, which may be determined according to an actual situation.
Step 43: based on the target path trajectory and the target speed information, a driving trajectory is generated.
In this embodiment, after the hot backup module determines the target path track and the target speed information of the autonomous vehicle, the target speed information is integrated into the target path track at the corresponding time, a driving track point set at the corresponding time is generated, and a curve formed by all the driving track point sets is the driving track of the autonomous vehicle.
According to the driving track generation method provided by the embodiment of the application, the target path track of the automatic driving vehicle is determined according to the effective lane line information in the effective reference information and the effective vehicle state information of the automatic driving vehicle, the target speed information of the automatic driving vehicle is determined according to the effective obstacle information in the effective reference information and the effective vehicle state information of at least one vehicle, and finally the driving track is generated based on the target path track and the target speed information. According to the technical scheme, the hot standby module respectively determines the target path track and the target speed information according to the obtained effective reference information, and track information is regenerated, so that the reasonability and the safety of the driving track are improved.
For example, on the basis of the foregoing embodiment, fig. 5 is a schematic flow chart of a fourth embodiment of the driving trajectory generation method provided in the embodiment of the present application. As shown in fig. 5, in the present embodiment, the step 41 (determining the target path trajectory of the autonomous vehicle according to the effective lane line information in the effective reference information and the effective vehicle state information of the autonomous vehicle) can be implemented by:
step 51: and determining the lane central line of the lane driven by the automatic driving vehicle and the feasible interval range of the lane according to the effective lane line information.
Optionally, in this embodiment, the hot standby module determines, according to the effective lane line information, lane line parameters on two sides of a lane on which the autonomous vehicle travels, and according to relative positions of the lane lines on the two sides, uses center lines of the lane lines on the two sides as lane center lines, and uses a vertical distance range between the lane lines on the two sides as a feasible interval range of the lane.
For example, fig. 6 is a schematic view of a scene of a lane in which an autonomous vehicle is traveling. As shown in fig. 6, the autonomous vehicle is traveling on a lane 60, which is a lane line 601 and a lane line 602 on both sides of the lane 60, and accordingly, the center line of the lane is referred to as a lane center line 61, and the range between the lane line 601 and the lane line 602 is the feasible section range of the lane.
Step 52: and determining a preset number of reference points in the lane by taking the center line of the lane as a reference according to the effective vehicle state information of the automatic driving vehicle.
Optionally, in this embodiment, the hot standby module determines a current driving speed from the acquired effective state information of the autonomous vehicle, determines a preset number of reference points on the lane according to the current driving speed, and a distance between two adjacent reference points is related to the current driving speed and a sampling time interval.
The number of reference points and the sampling time interval may be determined according to actual situations, and the embodiment does not limit the number.
Step 53: and generating a target path track of the automatic driving vehicle within the feasible interval range of the lane based on the smooth characteristic of the driving track and the positions of the preset number of reference points.
In the present embodiment, based on the smooth characteristic of the actual driving trajectory, the generated target path trajectory should be a curve which is smooth, has small curvature and small curvature transformation ratio, and approaches to the reference line function in the lane.
For example, in the present embodiment, the driving trajectory of the autonomous vehicle may be mapped onto a two-dimensional plane. Assuming that the target path trajectory of the autonomous vehicle is a function y (x) ax with the vehicle as the origin3+bx2+ cx, which is an over-origin and trivalent derivative function, the reference line function of the target path trajectory being z (x) dx3+ex2+ fx + g. Where x represents the coordinate of the autonomous vehicle in the direction of travel, and y (x) represents the coordinate of the autonomous vehicle in the x-vertical direction when it is on the lane line. a. b and c are coefficients of the path track to be solved; d. e, f, g are known constants that can be solved from the lane line parameters on both sides and the lane center line.
Optionally, in order to ensure safe driving of the autonomous vehicle, the target path trajectory function of the autonomous vehicle is preferably the smallest absolute value of the difference between the target path trajectory function and the reference line function at the corresponding coordinates, that is, the target is:
wherein the content of the first and second substances,
represents the function after the third derivative of the function y (x),
represents the function after the second derivative of the function y (x).
In this embodiment, in order to ensure that the driving trajectory of the autonomous vehicle is within the own lane, the function y (x) needs to satisfy the following constraint condition: left (x) is less than or equal to ax3+bx2+ cx ≦ right (x), where left (x) represents the lane line function for the autonomous vehicle to the left of the vehicle with the vehicle driving direction as the front, and right (x) represents the lane line function for the autonomous vehicle to the right of the vehicle with the vehicle driving direction as the front.
That is, the purpose of this embodiment is to
Optimally, the problem of coefficients a, b, c in function y (x) is solved. Illustratively, the problem of solving the coefficients a, b, and c in the function y (x) may be solved by using a convex optimization method, and the scheme of the convex optimization method belongs to the prior art and is not described herein again.
According to the driving track generation method provided by the embodiment of the application, the hot standby module determines a lane center line of a lane where the automatic driving vehicle runs and a feasible section range of the lane according to the effective lane line information, determines a preset number of reference points in the lane by taking the lane center line as a reference according to the effective vehicle state information of the automatic driving vehicle, and generates a target path track of the automatic driving vehicle in the feasible section range of the lane based on the smooth characteristic of the driving track and the positions of the preset number of reference points. According to the technical scheme, the target path track generated by the hot standby module is smooth and has small curvature and curvature change rate, the trend is a curve running along the center line of the lane, the reasonability is high, the safety is good, and the running safety is improved.
For example, in a possible design of this embodiment, the at least one vehicle includes: the autonomous vehicle, a first vehicle obstacle at risk of collision in a lane in which the autonomous vehicle is located, and/or a second vehicle obstacle at risk of collision in an adjacent lane.
Correspondingly, on the basis of the above embodiment, fig. 7 is a schematic flow chart of a fifth embodiment of the driving trajectory generation method provided in the embodiment of the present application. As shown in fig. 7, in the present embodiment, the step 42 (determining the target speed information of the autonomous vehicle according to the effective obstacle information in the effective reference information and the effective vehicle state information of at least one vehicle) can be implemented by the following steps:
step 71: and determining a first acceleration of the automatic driving vehicle from the current speed uniform deceleration to the stop according to the current speed of the automatic driving vehicle and the preset brake-stop time of the automatic driving vehicle in the effective vehicle state information.
In the present embodiment, in order to ensure that the autonomous vehicle can simultaneously guarantee the speed and the somatosensory acceleration in the high-speed autonomous driving working scene. Thus, the most reasonable target speed information may be determined based on the obstacle information and the valid vehicle state information of the at least one vehicle.
As an example, assuming that there is no obstacle in the lane where the autonomous vehicle is traveling, the hot standby module may calculate a first acceleration, which is an acceleration of the autonomous vehicle when decelerating from a current speed level to a stop, with minimal impact on comfort of a user, according to a current speed of the autonomous vehicle and a preset brake-off time of the autonomous vehicle.
Step 72: and determining a second minimum acceleration of the autonomous vehicle when the autonomous vehicle decelerates from the current speed level to the target vehicle speed according to the effective vehicle state information of the first vehicle obstacle and/or the effective vehicle state information of the second vehicle obstacle.
Wherein the target vehicle speed is a minimum speed in the first vehicle obstacle and/or the second vehicle obstacle.
Alternatively, as another example, assuming that there is a first vehicle obstacle at risk of collision in the lane in which the autonomous vehicle is traveling and/or a second vehicle obstacle at risk of collision in an adjacent lane, in order to avoid the risk of collision of the autonomous vehicle with the first vehicle obstacle or the second vehicle obstacle, the target speed of the autonomous vehicle should be less than the minimum speed among the first vehicle obstacle and the second vehicle obstacle.
Therefore, in this embodiment, the hot standby module calculates a second acceleration, which is the minimum second acceleration when the autonomous vehicle uniformly decelerates from the current speed to the target vehicle speed, based on the effective vehicle state information of the first vehicle obstacle and/or the effective vehicle state information of the second vehicle obstacle, in consideration of the comfort of the user in the vehicle.
Step 73: and determining a third acceleration which is minimum when the automatic driving vehicle stops before colliding with the fixed obstacle according to the distance information of the automatic driving vehicle and the fixed obstacle in the effective obstacle information.
Alternatively, as another example, assuming that a fixed obstacle exists in a lane where the autonomous vehicle travels, in order to avoid a collision risk, the autonomous vehicle needs to stop before traveling to a position where the fixed obstacle exists, and thus, the hot standby module needs to calculate a third acceleration that is the minimum when the autonomous vehicle stops before colliding with the fixed obstacle.
Step 74: the target speed information is determined based on the first acceleration, the second acceleration, and the third acceleration, and the speed track corresponding to the target speed information is a track formed by uniform deceleration motion with the maximum value of the first acceleration, the second acceleration, and the third acceleration as deceleration.
For example, in this embodiment, the first acceleration, the second acceleration, and the third acceleration calculated by the hot standby module according to the scene where the autonomous vehicle is located may be updated in real time during the driving process of the autonomous vehicle, and thus, in this embodiment, the autonomous vehicle may select the corresponding acceleration according to the scene where the autonomous vehicle is actually located to form the actual target speed information.
Optionally, the speed track corresponding to the target speed information is a track formed by uniform deceleration motion in which the maximum value of the first acceleration, the second acceleration, and the third acceleration is the deceleration.
According to the driving track generation method provided by the embodiment of the application, the hot standby module is combined with the current speed of the automatic driving vehicle, the preset brake-stop time of the automatic driving vehicle, the effective vehicle state information of the first vehicle barrier and/or the effective vehicle state information of the second vehicle barrier in the effective vehicle state information, and then the distance information of the automatic driving vehicle and the fixed barrier in the effective barrier information is combined to determine the target speed information. According to the technical scheme, the target speed information determined by the hot standby module is more fit with an actual scene, and the safe running of the automatic driving vehicle when the main control module breaks down can be guaranteed.
It should be noted that the driving track generation method of the present embodiment is particularly important for a high-speed automatic driving scenario, because safety must be guaranteed at one hundred percent, which is the root of an automatic driving vehicle and is the core competitiveness compared with a competitive product. In this embodiment, the hot standby module is the best choice for the low-cost safety framework, wherein the driving trajectory generation method is the core function of the hot standby module to ensure the normal driving of the autonomous vehicle, and the hot standby module can ensure the safe driving of the autonomous vehicle on the premise of limited hardware resources and effective processing capability.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Fig. 8 is a schematic structural diagram of a first driving trajectory generation device according to an embodiment of the present application. The device can be integrated in a hot standby module in a vehicle-mounted control system, and can also be realized by the hot standby module, wherein the vehicle-mounted control system comprises: the system comprises a main control module and a hot standby module. As shown in fig. 8, the apparatus may include: an acquisition module 81 and a generation module 82.
The obtaining module 81 is configured to obtain a reference information set when it is determined that the main control module works abnormally, where the reference information set includes: lane line information, obstacle information, and vehicle state information of at least one vehicle;
the generating module 82 is configured to generate a driving track of the autonomous vehicle according to effective reference information in the reference information set, where the effective reference information is reference information whose confidence is higher than a preset threshold.
Fig. 9 is a schematic structural diagram of a second driving trajectory generation device according to an embodiment of the present application. As shown in fig. 9, in this embodiment, the apparatus may further include: a pre-processing module 91.
The preprocessing module 91 is configured to determine a confidence level of each reference information in the reference information set before the generating module 82 generates a driving track of the autonomous vehicle according to the valid reference information in the reference information set, remove invalid reference information in the reference information set, where the confidence level is lower than the preset threshold, and retain the valid reference information in the reference information set, where the confidence level is higher than the preset threshold.
Fig. 10 is a schematic structural diagram of a third embodiment of the driving trajectory generation device according to the embodiment of the present application. As shown in fig. 10, in this embodiment, the generating module 82 includes: a first determining unit 101, a second determining unit 102 and a generating unit 103.
The first determining unit 101 is configured to determine a target path trajectory of the autonomous vehicle according to effective lane line information in the effective reference information and effective vehicle state information of the autonomous vehicle;
the second determining unit 102 is configured to determine target speed information of the autonomous vehicle according to effective obstacle information in the effective reference information and effective vehicle state information of the at least one vehicle;
the generating unit 103 is configured to generate the driving trajectory based on the target path trajectory and the target speed information.
For example, in this embodiment, the first determining unit 101 is specifically configured to determine a lane center line of a lane on which the autonomous vehicle travels and a feasible section range of the lane according to the valid lane line information, determine a preset number of reference points in the lane with reference to the lane center line according to the valid vehicle state information of the autonomous vehicle, and generate the target path trajectory of the autonomous vehicle in the feasible section range of the lane based on a driving trajectory smoothing characteristic and positions of the preset number of reference points.
Illustratively, in this embodiment, the at least one vehicle includes: the autonomous vehicle, a first vehicle obstacle in a lane where the autonomous vehicle is located and/or a second vehicle obstacle in an adjacent lane and having a collision risk.
Accordingly, in this embodiment, the second determining unit 102 is specifically configured to determine a first acceleration at which the autonomous vehicle decelerates from the current speed to a stop according to the current speed of the autonomous vehicle in the valid vehicle state information and the preset brake-off time of the autonomous vehicle, determine a second acceleration at which the autonomous vehicle decelerates from the current speed to a target vehicle speed according to the valid vehicle state information of the first vehicle obstacle and/or the valid vehicle state information of the second vehicle obstacle, the target vehicle speed being a minimum speed in the first vehicle obstacle and/or the second vehicle obstacle, determine a third acceleration at which the autonomous vehicle stops before colliding with the fixed obstacle according to the distance information between the autonomous vehicle and the fixed obstacle in the valid obstacle information, and determining the target speed information based on the first acceleration, the second acceleration and the third acceleration, wherein the speed track corresponding to the target speed information is a track formed by uniform deceleration motion with the maximum value of the first acceleration, the second acceleration and the third acceleration as deceleration.
The apparatus provided in the embodiment of the present application may be used to execute the method in the embodiments shown in fig. 2 to fig. 7, and the implementation principle and the technical effect are similar, which are not described herein again.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the determining module may be a processing element separately set up, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the function of the determining module is called and executed by a processing element of the apparatus. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Fig. 11 is a schematic structural diagram of a fourth driving trajectory generation device according to an embodiment of the present application. As shown in fig. 11, the apparatus may include: the system comprises a processor 111, a memory 112, a communication interface 113 and a system bus 114, wherein the memory 112 and the communication interface 113 are connected with the processor 111 through the system bus 114 and complete mutual communication, the memory 112 is used for storing computer execution instructions, the communication interface 113 is used for communicating with other devices, and the processor 111 implements the scheme in the embodiments shown in fig. 2 to fig. 7 when executing the computer program.
The system bus mentioned in fig. 11 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The memory may comprise Random Access Memory (RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor may be a general-purpose processor, including a central processing unit CPU, a Network Processor (NP), and the like; but also a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
Optionally, an embodiment of the present application further provides a storage medium, where instructions are stored in the storage medium, and when the storage medium is run on a computer, the storage medium causes the computer to perform the method according to the embodiment shown in fig. 2 to 7.
Optionally, an embodiment of the present application further provides a chip for executing the instruction, where the chip is configured to execute the method in the embodiment shown in fig. 2 to 7.
The embodiment of the present application further provides a program product, where the program product includes a computer program, where the computer program is stored in a storage medium, and the computer program can be read from the storage medium by at least one processor, and when the computer program is executed by the at least one processor, the method of the embodiment shown in fig. 2 to 7 can be implemented.
In the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship; in the formula, the character "/" indicates that the preceding and following related objects are in a relationship of "division". "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items.
It is to be understood that the various numerical references referred to in the embodiments of the present application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of the present application.
It should be understood that, in the embodiment of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiment of the present application.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (9)
1. A driving track generation method is suitable for a hot standby module in a vehicle-mounted control system, and the vehicle-mounted control system comprises the following steps: the main control module and the hot standby module are characterized in that the method comprises the following steps:
when the master control module is determined to work abnormally, a reference information set is obtained, wherein the reference information set comprises: lane line information, obstacle information, and vehicle state information of at least one vehicle;
generating a driving track of the automatic driving vehicle according to effective reference information in the reference information set, wherein the effective reference information is reference information with confidence coefficient higher than a preset threshold value;
before the generating a driving trajectory of an autonomous vehicle according to valid reference information in the reference information set, the method further comprises:
determining a confidence level of each reference information in the reference information set;
and eliminating invalid reference information with the confidence coefficient lower than the preset threshold value in the reference information set, and reserving the valid reference information with the confidence coefficient higher than the preset threshold value in the reference information set.
2. The method of claim 1, wherein generating a driving trajectory for an autonomous vehicle based on valid reference information in the set of reference information comprises:
determining a target path track of the automatic driving vehicle according to effective lane line information in the effective reference information and effective vehicle state information of the automatic driving vehicle;
determining target speed information of the autonomous vehicle according to effective obstacle information in the effective reference information and effective vehicle state information of the at least one vehicle;
generating the driving trajectory based on the target path trajectory and the target speed information.
3. The method of claim 2, wherein determining the target path trajectory of the autonomous vehicle based on the valid lane line information in the valid reference information and valid vehicle state information of the autonomous vehicle comprises:
determining a lane central line of a lane on which the automatic driving vehicle runs and a feasible interval range of the lane according to the effective lane line information;
determining a preset number of reference points in the lane by taking the lane central line as a reference according to the effective vehicle state information of the automatic driving vehicle;
and generating a target path track of the automatic driving vehicle within the feasible interval range of the lane based on the smooth characteristic of the driving track and the positions of the preset number of reference points.
4. The method of claim 2, wherein the at least one vehicle comprises: the autonomous vehicle, a first vehicle obstacle in a lane where the autonomous vehicle is located and/or a second vehicle obstacle in an adjacent lane and having a collision risk.
5. The method of claim 4, wherein determining the target speed information of the autonomous vehicle from the valid obstacle information in the valid reference information and the valid vehicle state information of the at least one vehicle comprises:
determining a first acceleration of the autonomous vehicle from uniform deceleration of the current speed to stop according to the current speed of the autonomous vehicle and a preset brake-stop time of the autonomous vehicle in the effective vehicle state information;
determining a second acceleration at which the autonomous vehicle decelerates from the current speed to a target vehicle speed that is a minimum speed in the first vehicle obstacle and/or the second vehicle obstacle according to effective vehicle state information of the first vehicle obstacle and/or effective vehicle state information of the second vehicle obstacle;
determining a third minimum acceleration of the autonomous vehicle when the autonomous vehicle stops before colliding with the fixed obstacle according to distance information of the autonomous vehicle and the fixed obstacle in the effective obstacle information;
and determining the target speed information based on the first acceleration, the second acceleration and the third acceleration, wherein the speed track corresponding to the target speed information is a track formed by uniform deceleration motion with the maximum value of the first acceleration, the second acceleration and the third acceleration as deceleration.
6. A driving track generation device is suitable for a hot standby module in an on-vehicle control system, and the on-vehicle control system comprises: the master control module with the hot spare module, its characterized in that, the device includes: the device comprises an acquisition module, a generation module and a preprocessing module;
the acquiring module is configured to acquire a reference information set when it is determined that the master control module is abnormal in operation, where the reference information set includes: lane line information, obstacle information, and vehicle state information of at least one vehicle;
the generating module is used for generating a driving track of the automatic driving vehicle according to effective reference information in the reference information set, wherein the effective reference information is reference information with confidence coefficient higher than a preset threshold value;
the preprocessing module is used for determining the confidence level of each reference information in the reference information set before the generating module generates the driving track of the automatic driving vehicle according to the effective reference information in the reference information set, eliminating invalid reference information of which the confidence level is lower than the preset threshold value in the reference information set, and reserving the effective reference information of which the confidence level is higher than the preset threshold value in the reference information set.
7. The apparatus of claim 6, wherein the generating module comprises: a first determining unit, a second determining unit and a generating unit;
the first determination unit is used for determining a target path track of the automatic driving vehicle according to effective lane line information in the effective reference information and effective vehicle state information of the automatic driving vehicle;
the second determination unit is used for determining the target speed information of the automatic driving vehicle according to the effective obstacle information in the effective reference information and the effective vehicle state information of the at least one vehicle;
the generating unit is used for generating the driving track based on the target path track and the target speed information.
8. A driving trajectory generation device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-5 when executing the program.
9. A storage medium having stored therein instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1-5.
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Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10928820B1 (en) * | 2019-11-12 | 2021-02-23 | Baidu Usa Llc | Confidence levels along the same predicted trajectory of an obstacle |
| CN111368641A (en) * | 2020-02-11 | 2020-07-03 | 北京百度网讯科技有限公司 | Obstacle screening method, obstacle screening device, electronic device and storage medium |
| CN112380448B (en) * | 2020-12-03 | 2022-02-22 | 腾讯科技（深圳）有限公司 | Vehicle data processing method and device, computer equipment and storage medium |
| CN113011323B (en) * | 2021-03-18 | 2022-11-29 | 阿波罗智联(北京)科技有限公司 | Method for acquiring traffic state, related device, road side equipment and cloud control platform |
| CN113085809B (en) * | 2021-04-28 | 2022-10-04 | 北京易控智驾科技有限公司 | Brake control method and control device for intelligent driving mine car |
| WO2022267035A1 (en) * | 2021-06-25 | 2022-12-29 | 华为技术有限公司 | Control method for vehicle, and device and storage medium |
| CN113619576B (en) * | 2021-09-07 | 2022-08-12 | 阿波罗智能技术(北京)有限公司 | Vehicle control method, device, equipment, storage medium and automatic driving vehicle |
| CN113635912B (en) * | 2021-09-10 | 2023-02-24 | 阿波罗智能技术(北京)有限公司 | Vehicle control method, device, equipment, storage medium and automatic driving vehicle |
| CN115257812A (en) * | 2022-08-19 | 2022-11-01 | 智己汽车科技有限公司 | Automatic driving confidence obtaining and reminding method and equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106527452A (en) * | 2016-12-30 | 2017-03-22 | 广州汽车集团股份有限公司 | Motion path planning method of pilotless automobile when encountering obstacle and system thereof |
| CN106740841A (en) * | 2017-02-14 | 2017-05-31 | 驭势科技（北京）有限公司 | Method for detecting lane lines, device and mobile unit based on dynamic control |
| CN107624155A (en) * | 2014-12-05 | 2018-01-23 | 苹果公司 | Autonomous navigation system |
| CN107644197A (en) * | 2016-07-20 | 2018-01-30 | 福特全球技术公司 | Rear portion video camera lane detection |
| CN108136977A (en) * | 2015-10-16 | 2018-06-08 | 日立汽车系统株式会社 | Automated driving system, automatic Pilot control method, data ECU and automatic Pilot ECU |
| CN108196547A (en) * | 2018-01-08 | 2018-06-22 | 北京图森未来科技有限公司 | A kind of automated driving system |
-
2018
- 2018-12-29 CN CN201811631342.8A patent/CN109739230B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107624155A (en) * | 2014-12-05 | 2018-01-23 | 苹果公司 | Autonomous navigation system |
| CN108136977A (en) * | 2015-10-16 | 2018-06-08 | 日立汽车系统株式会社 | Automated driving system, automatic Pilot control method, data ECU and automatic Pilot ECU |
| CN107644197A (en) * | 2016-07-20 | 2018-01-30 | 福特全球技术公司 | Rear portion video camera lane detection |
| CN106527452A (en) * | 2016-12-30 | 2017-03-22 | 广州汽车集团股份有限公司 | Motion path planning method of pilotless automobile when encountering obstacle and system thereof |
| CN106740841A (en) * | 2017-02-14 | 2017-05-31 | 驭势科技（北京）有限公司 | Method for detecting lane lines, device and mobile unit based on dynamic control |
| CN108196547A (en) * | 2018-01-08 | 2018-06-22 | 北京图森未来科技有限公司 | A kind of automated driving system |
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| Publication number | Publication date |
|---|---|
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