CN110654380A - Method and device for controlling a vehicle - Google Patents
Method and device for controlling a vehicle Download PDFInfo
- Publication number
- CN110654380A CN110654380A CN201910953734.4A CN201910953734A CN110654380A CN 110654380 A CN110654380 A CN 110654380A CN 201910953734 A CN201910953734 A CN 201910953734A CN 110654380 A CN110654380 A CN 110654380A
- Authority
- CN
- China
- Prior art keywords
- obstacle
- vehicle
- axis
- speed
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 57
- 230000004044 response Effects 0.000 claims abstract description 33
- 230000008569 process Effects 0.000 claims abstract description 16
- 238000004590 computer program Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 abstract description 13
- 230000000694 effects Effects 0.000 abstract description 2
- 210000004556 brain Anatomy 0.000 description 10
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 4
- 241001465754 Metazoa Species 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000036544 posture Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000000835 fiber Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Traffic Control Systems (AREA)
Abstract
The disclosed embodiments disclose a method and apparatus for controlling a vehicle. One embodiment of the method comprises: identifying a point cloud of an obstacle from point clouds collected in the driving process of a vehicle; determining a central point of a point cloud aiming at an obstacle, which is acquired at the current acquisition time and at least one acquisition time before the current acquisition time; calculating the variance of the determined at least two center points on the X axis; determining whether the variance is smaller than a preset fluctuation threshold value; and in response to determining that the variance is smaller than the fluctuation threshold, controlling the vehicle to continue running at the current running speed, and correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition moment. This embodiment reduces the effect on the normal driving of the autonomous vehicle due to the change in the form of the obstacle.
Description
Technical Field
The disclosed embodiments relate to the field of computer technology, and in particular, to a method and apparatus for controlling a vehicle.
Background
In an application scenario of automatic driving, a laser radar may be used to sense obstacles on a road. When detecting and identifying an obstacle based on a point cloud acquired by a laser radar, a center point of the point cloud is generally used as a center point of the obstacle, so that a movement track and a movement speed of the obstacle are analyzed. In practical applications, since the postures of soft obstacles such as pedestrians and animals may change during the movement process, such as turning, and large-amplitude swinging of limbs, the central point of the point cloud corresponding to such soft obstacles may fluctuate with small amplitude along with the change of the postures. The fluctuation of the center point of the obstacle may cause deviation of the predicted movement locus and movement speed of the obstacle. These deviations sometimes affect the normal travel of the autonomous vehicle.
Disclosure of Invention
The disclosed embodiments provide a method and apparatus for controlling a vehicle.
In a first aspect, embodiments of the present disclosure provide a method for controlling a vehicle, the method comprising: identifying a point cloud of an obstacle from point clouds collected in the driving process of a vehicle; determining a central point of the point cloud aiming at the obstacle, which is acquired at the current acquisition time and at least one acquisition time before the current acquisition time; calculating the variance of the determined at least two center points on the X axis; determining whether the variance is smaller than a preset fluctuation threshold value; and controlling the vehicle to continue running at the current running speed in response to the fact that the variance is smaller than the fluctuation threshold, and correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition time.
In some embodiments, the above method further comprises: in response to determining that the variance is greater than or equal to the fluctuation threshold, determining a moving speed of the obstacle according to a center point of the point cloud of the obstacle acquired at a current acquisition time and a center point of the point cloud of the obstacle acquired at a previous acquisition time of the current acquisition time; predicting whether the vehicle will collide with the obstacle while continuing to travel at the current travel speed, based on the travel speed of the obstacle, the travel speed of the vehicle, and the distances between the obstacle and the vehicle in the X axis and the Y axis; and sending control information to the vehicle in response to the prediction that the vehicle will collide with the obstacle when continuing to run at the current speed, wherein the control information is used for controlling the vehicle to avoid the collision with the obstacle.
In some embodiments, the above method further comprises: and controlling the vehicle to continue to run at the current running speed in response to predicting that the vehicle continues to run at the current speed without colliding with the obstacle.
In some embodiments, the predicting whether the vehicle will collide with the obstacle when the vehicle continues to travel at the current travel speed based on the travel speed of the obstacle, the travel speed of the vehicle, and the distances between the obstacle and the vehicle in the X axis and the Y axis includes: determining a first relative speed of the obstacle and the vehicle on an X axis according to speed components of the moving speed of the obstacle and the running speed of the vehicle on the X axis respectively; determining a second relative speed of the obstacle and the vehicle on a Y axis according to speed components of the moving speed of the obstacle and the running speed of the vehicle on the Y axis respectively; taking the distance between the obstacle and the vehicle on the X axis as the X axis distance, and calculating the ratio of the X axis distance to the first relative speed to obtain first time; calculating a ratio of the distance between the Y-axis distance and the second relative speed to obtain a second time, wherein the distance between the obstacle and the vehicle on the Y-axis is taken as the Y-axis distance; predicting that the vehicle may collide with the obstacle while continuing to travel at the current travel speed in response to determining that a difference between the first time and the second time is less than a preset time interval.
In some embodiments, the correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle at the current acquisition time includes: and taking the coordinate value of the X axis of the central point of the point cloud of the obstacle acquired at the previous acquisition time of the current acquisition time as the coordinate value of the X axis of the central point of the obstacle acquired at the current acquisition time.
In a second aspect, an embodiment of the present disclosure provides an apparatus for controlling a vehicle, the apparatus including: an identification unit configured to identify a point cloud of an obstacle from point clouds collected during a vehicle traveling; a first determination unit configured to determine a center point of a point cloud for the obstacle, which is acquired at a current acquisition time and at least one acquisition time before the current acquisition time; a calculation unit configured to calculate a variance of the determined at least two center points on an X-axis; a second determination unit configured to determine whether the variance is smaller than a preset fluctuation threshold; a first control unit configured to control the vehicle to continue traveling at a current traveling speed and correct a coordinate value of an X axis of a center point of the point cloud of the obstacle acquired at a current acquisition time in response to a determination that the variance is smaller than the fluctuation threshold.
In some embodiments, the above apparatus further comprises: a speed determination unit configured to determine a moving speed of the obstacle according to a center point of the point cloud of the obstacle acquired at a current acquisition time and a center point of the point cloud of the obstacle acquired at a previous acquisition time to the current acquisition time in response to determining that the variance is greater than or equal to the fluctuation threshold; a prediction unit configured to predict whether the vehicle will collide with the obstacle while continuing to travel at a current travel speed, based on a travel speed of the obstacle, a travel speed of the vehicle, and distances between the obstacle and the vehicle in an X axis and a Y axis; a second control unit configured to transmit control information to the vehicle in response to prediction that the vehicle continues to travel at the current speed and collides with the obstacle, wherein the control information is used to control the vehicle to avoid the collision with the obstacle.
In some embodiments, the above apparatus further comprises: a third control unit configured to control the vehicle to continue traveling at the current traveling speed in response to prediction that the vehicle continues traveling at the current speed without colliding with the obstacle.
In some embodiments, the prediction unit is further configured to: determining a first relative speed of the obstacle and the vehicle on an X axis according to speed components of the moving speed of the obstacle and the running speed of the vehicle on the X axis respectively; determining a second relative speed of the obstacle and the vehicle on a Y axis according to speed components of the moving speed of the obstacle and the running speed of the vehicle on the Y axis respectively; taking the distance between the obstacle and the vehicle on the X axis as the X axis distance, and calculating the ratio of the X axis distance to the first relative speed to obtain first time; calculating a ratio of the distance between the Y-axis distance and the second relative speed to obtain a second time, wherein the distance between the obstacle and the vehicle on the Y-axis is taken as the Y-axis distance; predicting that the vehicle may collide with the obstacle while continuing to travel at the current travel speed in response to determining that a difference between the first time and the second time is less than a preset time interval.
In some embodiments, the first control unit is further configured to: and taking the coordinate value of the X axis of the central point of the point cloud of the obstacle acquired at the previous acquisition time of the current acquisition time as the coordinate value of the X axis of the central point of the obstacle acquired at the current acquisition time.
In a third aspect, an embodiment of the present disclosure provides an apparatus, including: one or more processors; a storage device, on which one or more programs are stored, which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any implementation manner of the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer-readable medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
According to the method and the device for controlling the vehicle, the point cloud of the obstacle is firstly identified from the point cloud collected in the driving process of the vehicle. Then, center points of the point clouds for the obstacle acquired at least two acquisition moments including the current acquisition moment are determined. Then, a variance of the determined at least two center points on the X-axis is calculated. And when the calculated variance is smaller than a preset fluctuation threshold value, controlling the vehicle to continuously run according to the current running speed, and correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition moment, so that the influence of the change of the obstacle shape on the normal running of the automatic driving vehicle is reduced, and the small-amplitude fluctuation of the obstacle center point on the X axis caused by the change of the obstacle shape is corrected.
Drawings
Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;
FIG. 2 is a flow chart of one embodiment of a method for controlling a vehicle according to the present disclosure;
FIG. 3 is a schematic diagram of one application scenario of a method for controlling a vehicle according to the present disclosure;
FIG. 4 is a flow chart of yet another embodiment of a method for controlling a vehicle according to the present disclosure;
FIG. 5 is a schematic block diagram of one embodiment of an apparatus for controlling a vehicle according to the present disclosure;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 for a method for controlling a vehicle or an apparatus for controlling a vehicle to which embodiments of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include vehicles 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the vehicles 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The vehicles 101, 102, 103 may interact with a server 105 over a network 104 to receive or send messages, etc. The vehicles 101, 102, 103 may have various information acquisition devices mounted thereon, such as image acquisition devices, binocular cameras, sensors, lidar, and the like. The information acquisition device can be used for acquiring the internal and external environment information of the vehicles 101, 102 and 103. The vehicles 101, 102, 103 may further be equipped with vehicle-mounted intelligent brains (not shown in the figure), and the vehicle-mounted intelligent brains may receive the information collected by the information collecting device, analyze the information, and perform processing, and then control the vehicles 101, 102, 103 to perform corresponding operations (e.g., continue driving, emergency stop, etc.) according to the processing result. The vehicles 101, 102, 103 may be vehicles including an autonomous driving mode, including vehicles that are fully autonomous, and vehicles that can be switched to an autonomous driving mode.
The vehicles 101, 102, 103 may be various types of vehicles including, but not limited to, large buses, tractors, city buses, medium buses, large trucks, minicars, and the like.
The server 105 may be a server that provides various services, such as a backend server that processes information sent by the vehicles 101, 102, 103. The backend server may perform various analysis processes on the received information and transmit control information to the vehicles 101, 102, 103 according to the processing result to control the vehicles 101, 102, 103.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be understood that the number of vehicles, networks, and servers in FIG. 1 is merely illustrative. There may be any number of vehicles, networks, and servers, as desired for implementation.
It should be noted that the method for controlling the vehicle provided in the embodiment of the present application may be executed by the onboard intelligent brains installed on the vehicles 101, 102, 103, or may be executed by the server 105. Accordingly, the device for controlling the vehicle may be provided in the in-vehicle intelligent brain mounted on the vehicles 101, 102, 103, or may be provided in the server 105.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for controlling a vehicle according to the present disclosure is shown. The method for controlling a vehicle includes the steps of:
In the present embodiment, an executing subject of the method for controlling the vehicle (for example, an on-board intelligent brain or 105 server of the vehicles 101, 102, 103 shown in fig. 1) may acquire point clouds collected during the driving of the vehicle by a wired connection manner or a wireless connection manner, and identify a point cloud of an obstacle from the collected point clouds. Here, the point cloud of the obstacle may refer to a point cloud composed of point data for describing the obstacle.
In practice, a lidar sensor may be mounted on the vehicle. Therefore, in the driving process of the vehicle, the laser radar sensor can acquire the point cloud of the object in the surrounding environment of the vehicle in real time. The point cloud includes a plurality of point data, each of which may include three-dimensional coordinates. In general, the three-dimensional coordinates of the point data may include information on the X-axis, Y-axis, and Z-axis. The execution body can receive the point clouds collected by the laser radar sensor in real time, and conduct obstacle identification and tracking on each received frame of point clouds to identify which point data in the point clouds are used for describing obstacles, which point data are used for describing non-obstacles (such as driving areas), and which point data in different frame of point cloud data are used for describing the same obstacle. Here, the obstacles include, but are not limited to, trees, warning signs, traffic signs, pedestrians, animals, vehicles, and the like.
In this embodiment, the executing subject may determine a center point of the point cloud of the obstacle in the frame of point cloud acquired at the current acquisition time. The executing subject may further determine a center point of the point cloud of the obstacle in each frame of point cloud acquired at least one acquisition time before the current acquisition time. Thereby obtaining at least two center points. As an example, the center point of the point cloud of the obstacle in a certain frame of point cloud may be determined by: and calculating the average value of the coordinates of a plurality of point data included in the point cloud of the obstacle, and taking the calculation result as the center point of the point cloud of the obstacle in the frame of point cloud. Here, the current acquisition time and at least one acquisition time before the current acquisition time may be consecutive acquisition times.
In step 203, the variance of the determined at least two center points on the X-axis is calculated.
In this embodiment, the executive subject may calculate the variance on the X-axis of the at least two center points determined in step 202. Specifically, the executing subject may first calculate the mean of the coordinates of the at least two central points on the X-axis. Then, for each of the at least two center points, the executive body may calculate a square of a difference between the X-axis coordinate of the center point and the mean. And finally, accumulating the squares corresponding to the at least two central points, and dividing the accumulated value by the number of the at least two central points to obtain the difference value between the at least two central points. The specific formula is as follows:
wherein N represents the number of center points,
and a mean value of the X-axis coordinates representing the at least two center points. XiAnd coordinate values of the ith central point on the X axis are shown.
In this embodiment, the execution subject may determine whether the variance calculated in step 203 is less than a preset fluctuation threshold. Here, the fluctuation threshold may be set by a technician according to actual needs. For example, a technician may acquire a point cloud collected for a soft obstacle such as a moving pedestrian or an animal, analyze fluctuation of a central point of the acquired point cloud, and determine a fluctuation threshold according to an analysis result.
And step 205, in response to the fact that the variance is smaller than the fluctuation threshold value, controlling the vehicle to continue running at the current running speed, and correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition time.
In the present implementation, if the variance calculated in step 203 is smaller than the fluctuation threshold, the execution subject may send a control instruction to the vehicle to control the vehicle to continue traveling at the current traveling speed. In practice, the variance of the center point of the point cloud of the obstacle in the point cloud acquired at a plurality of acquisition moments on the X axis is smaller than a preset fluctuation threshold, which indicates that the change of the center point of the obstacle on the X axis is a small-amplitude change caused by the change of the obstacle shape. At the moment, the automatically-driven vehicle can directly continue to drive at the current driving speed by ignoring the fluctuation of the central point of the obstacle point cloud on the X axis.
Meanwhile, the execution main body can correct the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition time. In practice, the executing subject may modify the coordinate value of the X axis of the center point of the point cloud of the obstacle in various ways. As an example, the executing subject may calculate a mean value of the at least two central points on the X axis, and use the obtained mean value as an X axis coordinate value of a central point of the obstacle point cloud acquired at the current acquisition time. Therefore, the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition moment is corrected, and the change of the center point of the obstacle on the X axis caused by the change of the obstacle state is reduced.
In some optional implementations of this embodiment, in the step 205, the correction of the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition time may be specifically performed as follows: and taking the coordinate value of the X axis of the central point of the point cloud of the obstacle acquired at the previous acquisition time of the current acquisition time as the coordinate value of the X axis of the central point of the obstacle acquired at the current acquisition time.
In this implementation, the executing body may use, as the coordinate value of the X axis of the center point of the obstacle acquired at the current acquisition time, the coordinate value of the X axis of the center point of the obstacle acquired at the previous acquisition time before the current acquisition time. Therefore, the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition time is corrected.
With continued reference to fig. 3, fig. 3 is a schematic view of an application scenario of the method for controlling a vehicle according to the present embodiment. In the application scenario of fig. 3, an onboard intelligence brain (not shown) in the vehicle 301 first identifies a point cloud of the obstacle 302 from point clouds collected during the driving of the vehicle 301. Second, the onboard intelligent brain determines the current acquisition time and a center point of the point cloud for the obstacle 302 acquired at least one acquisition time before the current acquisition time. The onboard intelligent brain then calculates the variance of the determined at least two central points on the X-axis. Then, the vehicle-mounted intelligent brain determines whether the calculated variance is smaller than a preset fluctuation threshold value. And if the calculated variance is smaller than the fluctuation threshold, controlling the vehicle 301 to continue running at the current running speed, and correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle 302 acquired at the current acquisition moment.
The method provided by the above embodiment of the present disclosure first determines a center point of a point cloud for an obstacle, which is acquired at a plurality of acquisition times including a current acquisition time. When the variance of the center points is smaller than a preset fluctuation threshold value, the vehicle is controlled to continue to run according to the current running speed, and the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition moment is corrected, so that the influence of obstacle shape change on the normal running of the automatic driving vehicle is reduced, and the small-amplitude fluctuation of the obstacle center point on the X axis caused by the obstacle shape change is corrected.
With further reference to FIG. 4, a flow 400 of yet another embodiment of a method for controlling a vehicle is shown. The process 400 of the method for controlling a vehicle includes the steps of:
In this embodiment, step 401 is similar to step 201 of the embodiment shown in fig. 2, and is not described here again.
In this embodiment, step 402 is similar to step 202 of the embodiment shown in fig. 2, and is not described herein again.
In step 403, the variance of the determined at least two center points on the X-axis is calculated.
In this embodiment, step 403 is similar to step 203 of the embodiment shown in fig. 2, and is not described herein again.
In step 404, it is determined whether the variance is less than a preset fluctuation threshold.
In this embodiment, step 404 is similar to step 204 of the embodiment shown in fig. 2, and is not described here again.
And step 405, in response to the fact that the variance is smaller than the fluctuation threshold value, controlling the vehicle to continue running at the current running speed, and correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition time.
In this embodiment, step 405 is similar to step 205 of the embodiment shown in fig. 2, and is not described herein again.
And step 406, in response to determining that the variance is greater than or equal to the fluctuation threshold, determining the moving speed of the obstacle according to the center point of the point cloud of the obstacle acquired at the current acquisition time and the center point of the point cloud of the obstacle acquired at the previous acquisition time of the current acquisition time.
In this embodiment, if the variance calculated in step 403 is greater than or equal to the fluctuation threshold, the execution subject may determine the moving speed of the obstacle according to the center point of the point cloud of the obstacle acquired at the current acquisition time and the center point of the point cloud of the obstacle acquired at the previous acquisition time of the current acquisition time. Here, the moving speed includes a speed and a direction.
Step 407 predicts whether the vehicle will collide with the obstacle while continuing to travel at the current travel speed, based on the travel speed of the obstacle, the travel speed of the vehicle, and the distances between the obstacle and the vehicle in the X-axis and Y-axis.
In this embodiment, the execution body may predict whether the vehicle will collide with the obstacle while continuing to travel at the current travel speed, based on the travel speed of the obstacle, the travel speed of the vehicle, and the distance between the obstacle and the vehicle in the X axis and the Y axis. Here, the traveling speed of the vehicle includes a speed and a direction. As an example, the execution body may predict whether the vehicle and the obstacle may collide in various ways. For example, the execution main body may first determine the movement trajectories of the obstacle and the vehicle according to the moving speed of the obstacle and the traveling speed of the vehicle. And then, judging the corresponding position of the intersection point of the motion tracks of the obstacle and the vehicle. Then, the times when the obstacle and the vehicle reach the position are calculated, respectively. And finally, predicting whether the vehicle and the obstacle collide or not according to the difference value of the two calculated time. In practice, a smaller difference between the two times indicates a greater possibility of collision of the obstacle with the vehicle.
In some optional implementations of this embodiment, the step 407 may specifically be performed as follows:
first, first relative speeds of the obstacle and the vehicle on the X axis are determined according to speed components of the moving speed of the obstacle and the running speed of the vehicle on the X axis respectively.
In this implementation, the execution main body may determine the first relative speed of the obstacle and the vehicle on the X axis according to a speed component of the moving speed of the obstacle on the X axis and a speed component of the current traveling speed of the vehicle on the X axis.
Then, according to the moving speed of the obstacle and the speed component of the running speed of the vehicle in the Y axis, a second relative speed of the obstacle and the vehicle in the Y axis is determined.
In this implementation, the execution subject may determine the second relative speed of the obstacle and the vehicle in the Y axis according to a speed component of the moving speed of the obstacle in the Y axis and a speed component of the current traveling speed of the vehicle in the Y axis.
And thirdly, taking the distance between the obstacle and the vehicle on the X axis as the X axis distance, and calculating the ratio of the X axis distance to the first relative speed to obtain the first time.
In this implementation, the execution body may take a distance of the obstacle from the vehicle on the X axis as the X axis distance. Thereafter, the executing body may calculate a ratio between the X-axis distance and the first relative speed, thereby obtaining a first time.
Then, the distance between the obstacle and the vehicle on the Y axis is used as the Y axis distance, and the ratio of the Y axis distance to the second relative speed is calculated to obtain the second time.
In this implementation, the execution body may take the distance of the obstacle from the vehicle on the Y axis as the Y axis distance. The executive may then calculate a ratio between the Y-axis distance and the second relative velocity, resulting in a second time.
Finally, in response to determining that the difference between the first time and the second time is less than the preset time interval, it is predicted that the vehicle will collide with the obstacle while continuing to travel at the current travel speed.
In this implementation, the execution main body may determine whether a difference between the first time and the second time is smaller than a preset time interval. If the current driving speed is less than the preset driving speed, the vehicle is predicted to collide with the obstacle when the vehicle continues to drive at the current driving speed. Here, the time interval may be set according to actual needs, and may be determined according to the length and width of the vehicle body and the current running speed, for example.
In the present embodiment, the execution subject may transmit the control information to the vehicle if it is predicted that the vehicle will collide with the above-described obstacle while continuing to travel at the current speed. Here, the above control information may be used to control the vehicle to avoid collision with an obstacle. For example, the control information may be used to control the vehicle to stop running, or to run around an obstacle.
In some optional implementations of the present embodiment, the method for controlling a vehicle described above may further include: and controlling the vehicle to continue to run at the current running speed in response to predicting that the vehicle continues to run at the current speed without colliding with the obstacle.
In the present implementation, the execution subject may control the vehicle to continue traveling at the current traveling speed if it is predicted that the vehicle will not collide with the obstacle while continuing traveling at the current speed.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the method for controlling a vehicle in the present embodiment highlights the step of predicting whether the vehicle will collide with the obstacle when the variance of at least two center points is greater than or equal to the preset fluctuation threshold, and controlling the vehicle according to the prediction result, whereby the scheme described in the present embodiment can ensure safe running of the vehicle while reducing the influence on normal running of the autonomous vehicle due to the change in the form of the obstacle.
With further reference to fig. 5, as an implementation of the methods shown in the above figures, the present disclosure provides one embodiment of an apparatus for controlling a vehicle, which corresponds to the method embodiment shown in fig. 2, and which may be particularly applied in various electronic devices.
As shown in fig. 5, the apparatus 500 for controlling a vehicle of the present embodiment includes: a recognition unit 501, a first determination unit 502, a calculation unit 503, a second determination unit 504, and a first control unit 505. The identifying unit 501 is configured to identify a point cloud of an obstacle from point clouds collected during the travel of the vehicle; the first determination unit 502 is configured to determine a center point of the point cloud for the obstacle acquired at the current acquisition time and at least one acquisition time before the current acquisition time; the calculation unit 503 is configured to calculate a variance of the determined at least two center points on the X-axis; the second determination unit 504 is configured to determine whether the variance is smaller than a preset fluctuation threshold; the first control unit 505 is configured to control the vehicle to continue traveling at a current traveling speed and correct the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition time in response to determining that the variance is smaller than the fluctuation threshold.
In this embodiment, specific processes of the identification unit 501, the first determination unit 502, the calculation unit 503, the second determination unit 504, and the first control unit 505 of the apparatus 500 for controlling a vehicle and technical effects brought by the specific processes may respectively refer to relevant descriptions of step 201, step 202, step 203, step 204, and step 205 in the corresponding embodiment of fig. 2, and are not repeated herein.
In some optional implementations of this embodiment, the apparatus 500 further includes: a speed determination unit (not shown in the drawings) configured to determine a moving speed of the obstacle from a center point of the point cloud of the obstacle acquired at a current acquisition time and a center point of the point cloud of the obstacle acquired at a previous acquisition time to the current acquisition time, in response to a determination that the variance is greater than or equal to the fluctuation threshold; a prediction unit (not shown) configured to predict whether the vehicle will collide with the obstacle while continuing to travel at a current travel speed, based on a travel speed of the obstacle, a travel speed of the vehicle, and distances of the obstacle from the vehicle in X and Y axes; and a second control unit (not shown) configured to transmit control information to the vehicle in response to prediction that the vehicle will collide with the obstacle while continuing to travel at the current speed, wherein the control information is used to control the vehicle to avoid the collision with the obstacle.
In some optional implementations of this embodiment, the apparatus 500 further includes: and a third control unit (not shown) configured to control the vehicle to continue traveling at the current traveling speed in response to a prediction that the vehicle continues traveling at the current speed without colliding with the obstacle.
In some optional implementations of the present embodiment, the prediction unit is further configured to: determining a first relative speed of the obstacle and the vehicle on an X axis according to speed components of the moving speed of the obstacle and the running speed of the vehicle on the X axis respectively; determining a second relative speed of the obstacle and the vehicle on a Y axis according to speed components of the moving speed of the obstacle and the running speed of the vehicle on the Y axis respectively; taking the distance between the obstacle and the vehicle on the X axis as the X axis distance, and calculating the ratio of the X axis distance to the first relative speed to obtain first time; calculating a ratio of the distance between the Y-axis distance and the second relative speed to obtain a second time, wherein the distance between the obstacle and the vehicle on the Y-axis is taken as the Y-axis distance; predicting that the vehicle may collide with the obstacle while continuing to travel at the current travel speed in response to determining that a difference between the first time and the second time is less than a preset time interval.
In some optional implementations of the present embodiment, the first control unit 505 is further configured to: and taking the coordinate value of the X axis of the central point of the point cloud of the obstacle acquired at the previous acquisition time of the current acquisition time as the coordinate value of the X axis of the central point of the obstacle acquired at the current acquisition time.
Referring now to fig. 6, a schematic diagram of an electronic device (e.g., the server in fig. 1 or an onboard intelligent brain installed in the vehicles 101, 102, 103) 600 suitable for implementing embodiments of the present disclosure is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 6 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of embodiments of the present disclosure.
It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: identifying a point cloud of an obstacle from point clouds collected in the driving process of a vehicle; determining a central point of the point cloud aiming at the obstacle, which is acquired at the current acquisition time and at least one acquisition time before the current acquisition time; calculating the variance of the determined at least two center points on the X axis; determining whether the variance is smaller than a preset fluctuation threshold value; and controlling the vehicle to continue running at the current running speed in response to the fact that the variance is smaller than the fluctuation threshold, and correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition time.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an identification unit, a first determination unit, a calculation unit, a second determination unit, and a first control unit. The names of these units do not in some cases form a limitation on the unit itself, and for example, the identification unit may also be described as a "unit that identifies a point cloud of an obstacle from point clouds collected during the travel of the vehicle".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (12)
1. A method for controlling a vehicle, comprising:
identifying a point cloud of an obstacle from point clouds collected in the driving process of a vehicle;
determining a central point of a point cloud for the obstacle, which is acquired at a current acquisition time and at least one acquisition time before the current acquisition time;
calculating the variance of the determined at least two center points on the X axis;
determining whether the variance is smaller than a preset fluctuation threshold value;
and in response to the fact that the variance is smaller than the fluctuation threshold value, controlling the vehicle to continue running at the current running speed, and correcting the coordinate value of the X axis of the center point of the point cloud of the obstacle acquired at the current acquisition moment.
2. The method of claim 1, wherein the method further comprises:
in response to determining that the variance is greater than or equal to the fluctuation threshold, determining a movement speed of the obstacle according to a center point of the point cloud of the obstacle acquired at a current acquisition time and a center point of the point cloud of the obstacle acquired at a previous acquisition time of the current acquisition time;
predicting whether the vehicle collides with the obstacle when the vehicle continues to run according to the current running speed according to the moving speed of the obstacle, the running speed of the vehicle and the distances between the obstacle and the vehicle on the X axis and the Y axis;
and in response to the prediction that the vehicle is collided with the obstacle when the vehicle continues to run at the current speed, sending control information to the vehicle, wherein the control information is used for controlling the vehicle to avoid the collision with the obstacle.
3. The method of claim 2, wherein the method further comprises:
and controlling the vehicle to continue to run at the current running speed in response to predicting that the vehicle continues to run at the current speed without colliding with the obstacle.
4. The method of claim 2, wherein the predicting whether the vehicle will collide with the obstacle while continuing to travel at the current travel speed based on the travel speed of the obstacle, the travel speed of the vehicle, and the distance of the obstacle from the vehicle in the X-axis and the Y-axis comprises:
determining a first relative speed of the obstacle and the vehicle on an X axis according to the speed components of the moving speed of the obstacle and the running speed of the vehicle on the X axis respectively;
determining a second relative speed of the obstacle and the vehicle on a Y axis according to the speed components of the moving speed of the obstacle and the running speed of the vehicle on the Y axis respectively;
taking the distance between the obstacle and the vehicle on the X axis as the X axis distance, and calculating the ratio of the X axis distance to the first relative speed to obtain first time;
taking the distance between the obstacle and the vehicle on the Y axis as the Y axis distance, and calculating the ratio of the Y axis distance to the second relative speed to obtain second time;
predicting that the vehicle will collide with the obstacle while continuing to travel at the current travel speed in response to determining that the difference between the first time and the second time is less than a preset time interval.
5. The method of claim 1, wherein the correcting the coordinate value of the X-axis of the center point of the point cloud of the obstacle acquired at the current acquisition time comprises:
and taking the coordinate value of the X axis of the central point of the point cloud of the obstacle acquired at the previous acquisition time of the current acquisition time as the coordinate value of the X axis of the central point of the obstacle acquired at the current acquisition time.
6. An apparatus for controlling a vehicle, comprising:
an identification unit configured to identify a point cloud of an obstacle from point clouds collected during a vehicle traveling;
a first determination unit configured to determine a center point of a point cloud for the obstacle acquired at a current acquisition time and at least one acquisition time before the current acquisition time;
a calculation unit configured to calculate a variance of the determined at least two center points on an X-axis;
a second determination unit configured to determine whether the variance is smaller than a preset fluctuation threshold;
a first control unit configured to control the vehicle to continue traveling at a current traveling speed and correct a coordinate value of an X-axis of a center point of a point cloud of the obstacle acquired at a current acquisition time in response to a determination that the variance is smaller than the fluctuation threshold.
7. The apparatus of claim 6, wherein the apparatus further comprises:
a speed determination unit configured to determine a moving speed of the obstacle according to a center point of the point cloud of the obstacle acquired at a current acquisition time and a center point of the point cloud of the obstacle acquired at a previous acquisition time to the current acquisition time in response to determining that the variance is greater than or equal to the fluctuation threshold;
a prediction unit configured to predict whether the vehicle will collide with the obstacle while continuing to travel at a current travel speed, based on a travel speed of the obstacle, a travel speed of the vehicle, and distances of the obstacle from the vehicle in an X axis and a Y axis;
a second control unit configured to transmit control information to the vehicle in response to prediction that the vehicle continues to travel at the current speed and collides with the obstacle, wherein the control information is used to control the vehicle to avoid the collision with the obstacle.
8. The apparatus of claim 7, wherein the apparatus further comprises:
a third control unit configured to control the vehicle to continue traveling at the current traveling speed in response to prediction that the vehicle continues traveling at the current speed without colliding with the obstacle.
9. The apparatus of claim 7, wherein the prediction unit is further configured to:
determining a first relative speed of the obstacle and the vehicle on an X axis according to the speed components of the moving speed of the obstacle and the running speed of the vehicle on the X axis respectively;
determining a second relative speed of the obstacle and the vehicle on a Y axis according to the speed components of the moving speed of the obstacle and the running speed of the vehicle on the Y axis respectively;
taking the distance between the obstacle and the vehicle on the X axis as the X axis distance, and calculating the ratio of the X axis distance to the first relative speed to obtain first time;
taking the distance between the obstacle and the vehicle on the Y axis as the Y axis distance, and calculating the ratio of the Y axis distance to the second relative speed to obtain second time;
predicting that the vehicle will collide with the obstacle while continuing to travel at the current travel speed in response to determining that the difference between the first time and the second time is less than a preset time interval.
10. The apparatus of claim 6, wherein the first control unit is further configured to:
and taking the coordinate value of the X axis of the central point of the point cloud of the obstacle acquired at the previous acquisition time of the current acquisition time as the coordinate value of the X axis of the central point of the obstacle acquired at the current acquisition time.
11. An apparatus, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910953734.4A CN110654380B (en) | 2019-10-09 | 2019-10-09 | Method and device for controlling a vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910953734.4A CN110654380B (en) | 2019-10-09 | 2019-10-09 | Method and device for controlling a vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110654380A true CN110654380A (en) | 2020-01-07 |
| CN110654380B CN110654380B (en) | 2023-12-15 |
Family
ID=69038687
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910953734.4A Active CN110654380B (en) | 2019-10-09 | 2019-10-09 | Method and device for controlling a vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110654380B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111301432A (en) * | 2020-03-03 | 2020-06-19 | 北京百度网讯科技有限公司 | Parking method and device for autonomous vehicle |
| CN111578894A (en) * | 2020-06-02 | 2020-08-25 | 北京经纬恒润科技有限公司 | Method and device for determining heading angle of obstacle |
| CN113728324A (en) * | 2020-03-26 | 2021-11-30 | 百度时代网络技术（北京）有限公司 | Low-height obstacle detection system based on point cloud |
| CN116125897A (en) * | 2023-03-02 | 2023-05-16 | 重庆长安汽车股份有限公司 | Automatic driving decision method, device, electronic equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108152831A (en) * | 2017-12-06 | 2018-06-12 | 中国农业大学 | A kind of laser radar obstacle recognition method and system |
| CN108226895A (en) * | 2017-12-27 | 2018-06-29 | 吉林大学 | Static-obstacle thing identifying system and recognition methods based on laser radar |
| CN109375228A (en) * | 2018-10-31 | 2019-02-22 | 歌尔股份有限公司 | Determination method, the processing method and processing device of the abnormal frame point cloud data of laser radar |
| CN109521756A (en) * | 2017-09-18 | 2019-03-26 | 百度在线网络技术（北京）有限公司 | Barrier motion information generation method and device for automatic driving vehicle |
| CN109927719A (en) * | 2017-12-15 | 2019-06-25 | 百度在线网络技术（北京）有限公司 | A kind of auxiliary driving method and system based on barrier trajectory predictions |
| CN110045729A (en) * | 2019-03-12 | 2019-07-23 | 广州小马智行科技有限公司 | A kind of Vehicular automatic driving method and device |
| CN110210389A (en) * | 2019-05-31 | 2019-09-06 | 东南大学 | A kind of multi-targets recognition tracking towards road traffic scene |
| FR3079034A1 (en) * | 2018-03-14 | 2019-09-20 | Renault S.A.S | ROBUST METHOD FOR DETECTING OBSTACLES, IN PARTICULAR FOR AUTONOMOUS VEHICLES |
-
2019
- 2019-10-09 CN CN201910953734.4A patent/CN110654380B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109521756A (en) * | 2017-09-18 | 2019-03-26 | 百度在线网络技术（北京）有限公司 | Barrier motion information generation method and device for automatic driving vehicle |
| CN108152831A (en) * | 2017-12-06 | 2018-06-12 | 中国农业大学 | A kind of laser radar obstacle recognition method and system |
| CN109927719A (en) * | 2017-12-15 | 2019-06-25 | 百度在线网络技术（北京）有限公司 | A kind of auxiliary driving method and system based on barrier trajectory predictions |
| CN108226895A (en) * | 2017-12-27 | 2018-06-29 | 吉林大学 | Static-obstacle thing identifying system and recognition methods based on laser radar |
| FR3079034A1 (en) * | 2018-03-14 | 2019-09-20 | Renault S.A.S | ROBUST METHOD FOR DETECTING OBSTACLES, IN PARTICULAR FOR AUTONOMOUS VEHICLES |
| CN109375228A (en) * | 2018-10-31 | 2019-02-22 | 歌尔股份有限公司 | Determination method, the processing method and processing device of the abnormal frame point cloud data of laser radar |
| CN110045729A (en) * | 2019-03-12 | 2019-07-23 | 广州小马智行科技有限公司 | A kind of Vehicular automatic driving method and device |
| CN110210389A (en) * | 2019-05-31 | 2019-09-06 | 东南大学 | A kind of multi-targets recognition tracking towards road traffic scene |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111301432A (en) * | 2020-03-03 | 2020-06-19 | 北京百度网讯科技有限公司 | Parking method and device for autonomous vehicle |
| CN113728324A (en) * | 2020-03-26 | 2021-11-30 | 百度时代网络技术（北京）有限公司 | Low-height obstacle detection system based on point cloud |
| CN111578894A (en) * | 2020-06-02 | 2020-08-25 | 北京经纬恒润科技有限公司 | Method and device for determining heading angle of obstacle |
| CN111578894B (en) * | 2020-06-02 | 2021-10-15 | 北京经纬恒润科技股份有限公司 | Method and device for determining heading angle of obstacle |
| CN116125897A (en) * | 2023-03-02 | 2023-05-16 | 重庆长安汽车股份有限公司 | Automatic driving decision method, device, electronic equipment and storage medium |
| CN116125897B (en) * | 2023-03-02 | 2024-06-28 | 重庆长安汽车股份有限公司 | Automatic driving decision method, device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110654380B (en) | 2023-12-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110654381B (en) | Method and device for controlling a vehicle | |
| US20210208597A1 (en) | Sensor aggregation framework for autonomous driving vehicles | |
| US10457294B1 (en) | Neural network based safety monitoring system for autonomous vehicles | |
| CN110654380B (en) | Method and device for controlling a vehicle | |
| US10807599B2 (en) | Driving scenario based lane guidelines for path planning of autonomous driving vehicles | |
| CN113715814B (en) | Collision detection method, device, electronic equipment, medium and automatic driving vehicle | |
| US20190318267A1 (en) | System and method for training a machine learning model deployed on a simulation platform | |
| US10054945B2 (en) | Method for determining command delays of autonomous vehicles | |
| US10272778B2 (en) | Method and system for determining unit gain of speed control for autonomous driving vehicles | |
| CN110696826B (en) | Method and device for controlling a vehicle | |
| US11453410B2 (en) | Reducing processing requirements for vehicle control | |
| US10909377B2 (en) | Tracking objects with multiple cues | |
| US11456890B2 (en) | Open and safe monitoring system for autonomous driving platform | |
| CN112622923B (en) | Method and device for controlling a vehicle | |
| CN114771533A (en) | Control method, device, equipment, vehicle and medium for automatic driving vehicle | |
| CN114771534A (en) | Control method, training method, vehicle, device, and medium for automatically driving vehicle | |
| CN114212108A (en) | Automatic driving method, device, vehicle, storage medium and product | |
| CN112558036B (en) | Method and device for outputting information | |
| CN112528711B (en) | Method and device for processing information | |
| CN114056337B (en) | Method, device and computer program product for predicting vehicle running behavior | |
| CN112526477B (en) | Method and device for processing information | |
| CN115565374A (en) | Logistics vehicle driving optimization method and device, electronic equipment and readable storage medium | |
| CN112668371A (en) | Method and apparatus for outputting information | |
| CN111832142A (en) | Method and apparatus for outputting information | |
| US20240211363A1 (en) | Gpu stress test with dynamic loads |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |