Traveling control method, traveling control device, and intelligent driving vehicle

Abstract

The present disclosure relates to the field of computers, in particular to the field of intelligent driving, and provides a traveling control method, a traveling control apparatus, and an intelligent driving vehicle. The method comprises: acquiring the position, speed and acceleration of a target in a coordinate system of an intelligent driving vehicle, and the speed and braking attributes of the intelligent driving vehicle; determining, on the basis the position, speed and acceleration of the target in the coordinate system of the intelligent driving vehicle, and the speed and the braking attributes of the intelligent driving vehicle, whether there is a risk of collision between the target and the intelligent driving vehicle; and when it is determined that there is a risk of collision, determining that emergency braking needs to be performed. On the basis of rich information such as the sensed speed and acceleration of the target, whether there is a risk of collision between the target and the intelligent driving vehicle is more accurately determined, thereby accurately determining whether emergency braking is needed.

Description

Driving control methods, driving control devices, and intelligent driving vehicles

[0001] Cross-reference of related applications

[0002] This application is based on and claims priority to CN application No. 202411959824.1, filed on December 27, 2024, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] This disclosure relates to the field of computers, and more particularly to the field of intelligent driving, and especially to driving control methods, driving control devices and intelligent driving vehicles. Background Technology

[0004] Intelligent driving vehicles are used to automatically transport people or goods from one location to another. They collect environmental information through sensors on the vehicle and complete the automated transport. Intelligent driving vehicles controlled by autonomous driving technology greatly improve the convenience of production and daily life, and save labor costs.

[0005] The active safety module is an important component of the autonomous driving system. It perceives and makes decisions about the driving environment and issues emergency braking commands to the vehicle chassis in the event of a dangerous situation to ensure vehicle safety.

[0006] Some intelligent driving vehicles can only sense the speed information of the target and decide whether emergency braking is needed based on the sensed speed information of the target. Summary of the Invention

[0007] Some embodiments of this disclosure propose a driving control method, including: acquiring the position, velocity, and acceleration of a target in the coordinate system of an intelligent driving vehicle, as well as the speed and braking attributes of the intelligent driving vehicle; determining whether there is a risk of conflict between the target and the intelligent driving vehicle based on the position, velocity, and acceleration of the target in the coordinate system of the intelligent driving vehicle, as well as the speed and braking attributes of the intelligent driving vehicle; and determining that emergency braking needs to be performed if a risk of conflict is determined.

[0008] In some embodiments, determining whether there is a risk of conflict between a target and an autonomous vehicle includes: determining the relative speed of the target relative to the autonomous vehicle, and determining whether the target is a candidate target for triggering emergency braking based on whether the ray of the target in the relative speed direction crosses the safe driving range of the autonomous vehicle; if the target is a candidate target for triggering emergency braking, determining whether there is a risk of conflict between the target and the autonomous vehicle based on the target's position, speed and acceleration in the autonomous vehicle's coordinate system and the speed and braking attributes of the autonomous vehicle.

[0009] In some embodiments, determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: determining the braking distance of the target and the braking distance of the intelligent driving vehicle based on the radial components of the target's velocity and acceleration, as well as the speed and braking attributes of the intelligent driving vehicle; and determining whether there is a risk of conflict between the target and the intelligent driving vehicle in the radial direction by comparing the difference between the braking distance of the intelligent driving vehicle and the braking distance of the target with the radial distance between the target and the intelligent driving vehicle, wherein the radial distance between the target and the intelligent driving vehicle is determined based on the radial component of the target's position in the coordinate system of the intelligent driving vehicle.

[0010] In some embodiments, determining the braking distance of the target and the braking distance of the autonomous vehicle includes: if the radial components of the speeds of the target and the autonomous vehicle are in the same direction, and if the speeds of the autonomous vehicle and the target will not reach the same at any time during the deceleration of the autonomous vehicle at the maximum braking deceleration, the distance traveled by the autonomous vehicle from its current speed to zero at the maximum braking deceleration is determined as the braking distance of the autonomous vehicle, and the distance traveled by the target from its current speed radial component to zero at the acceleration radial component is determined as the braking distance of the target.

[0011] In some embodiments, determining the braking distance of the target and the braking distance of the autonomous vehicle includes: when the radial components of the speeds of the target and the autonomous vehicle are in the same direction, if the speeds of the autonomous vehicle and the target reach the same point at a certain moment during the deceleration process of the autonomous vehicle at its maximum braking deceleration, the distance traveled by the autonomous vehicle from its current speed to the moment when the speeds are the same is determined as the braking distance of the autonomous vehicle, and the distance traveled by the target from its current speed radial component to the moment when the speeds are the same is determined as the braking distance of the target.

[0012] In some embodiments, determining the braking distance of the target and the braking distance of the intelligent driving vehicle includes: when the radial component of the speed of the target and the intelligent driving vehicle are opposite in direction, determining the distance traveled by the intelligent driving vehicle during the process of decelerating from the current speed to zero at the maximum braking deceleration as the braking distance of the intelligent driving vehicle, and determining the distance traveled by the target during the process of decelerating from the current speed radial component to zero at the preset maximum deceleration as the braking distance of the target.

[0013] In some embodiments, determining whether there is a risk of conflict between the target and the intelligent driving vehicle in the radial direction includes: determining that there is a risk of conflict between the target and the intelligent driving vehicle in the radial direction if the difference between the braking distance of the intelligent driving vehicle and the braking distance of the target is greater than the difference between the radial distance between the target and the intelligent driving vehicle and the safety buffer distance, wherein the radial distance between the target and the intelligent driving vehicle is determined based on the radial component of the target's position in the intelligent driving vehicle's coordinate system.

[0014] In some embodiments, determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: determining whether the target will move into the width range of the intelligent driving vehicle in the lateral direction based on the target's position, velocity, and lateral acceleration components in the intelligent driving vehicle's coordinate system and the speed of the intelligent driving vehicle; and determining whether there is a risk of conflict between the target and the intelligent driving vehicle in the lateral direction based on whether the target will move into the width range of the intelligent driving vehicle in the lateral direction.

[0015] In some embodiments, determining whether a target will move into the width range of the autonomous vehicle in the lateral direction includes: determining the time when the target and the autonomous vehicle will collide in the radial direction based on the radial distance between the target and the autonomous vehicle, and the difference between the radial components of the speed of the autonomous vehicle and the speed of the target, wherein the radial distance between the target and the autonomous vehicle is determined based on the radial component of the target's position in the autonomous vehicle's coordinate system; determining the lateral component of the target's position at the time of the collision based on the lateral component of the target's current position, the lateral component of its speed, and the lateral component of its acceleration in the autonomous vehicle's coordinate system, and the time of the collision; and determining whether the target will move into the width range of the autonomous vehicle in the lateral direction based on the lateral component of the target's position at the time of the collision.

[0016] In some embodiments, determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: determining whether there is a potential risk of conflict between the target and the intelligent driving vehicle based on at least one of the target's position in the intelligent driving vehicle's coordinate system, the target's velocity, and the intelligent driving vehicle's velocity; and if a potential risk of conflict is determined, determining whether there is a risk of conflict between the target and the intelligent driving vehicle based on the target's position, velocity, and acceleration in the intelligent driving vehicle's coordinate system, as well as the intelligent driving vehicle's velocity and braking attributes.

[0017] In some embodiments, determining whether there is a potential conflict risk between the target and the intelligent driving vehicle includes: determining whether the target is behind or in front of the intelligent driving vehicle based on the radial component of the target's position in the intelligent driving vehicle's coordinate system; comparing the radial component of the target's velocity with the velocity of the intelligent driving vehicle; determining that there is no potential conflict risk between the target and the intelligent driving vehicle if the target is behind the intelligent driving vehicle and the radial component of the target's velocity is less than the velocity of the intelligent driving vehicle; and determining that there is no potential conflict risk between the target and the intelligent driving vehicle if the target is in front of the intelligent driving vehicle and the radial component of the target's velocity is greater than the velocity of the intelligent driving vehicle.

[0018] In some embodiments, determining whether there is a potential conflict risk between the target and the intelligent driving vehicle includes: determining whether the target is located to the left or right of the intelligent driving vehicle based on the lateral component of the target's position in the intelligent driving vehicle's coordinate system; determining that there is no potential conflict risk between the target and the intelligent driving vehicle if the target is located to the left of the intelligent driving vehicle and the lateral component of the target's velocity is to the left; and determining that there is no potential conflict risk between the target and the intelligent driving vehicle if the target is located to the right of the intelligent driving vehicle and the lateral component of the target's velocity is to the right.

[0019] In some embodiments, determining whether there is a potential conflict risk between the target and the intelligent driving vehicle includes: determining whether the target is located behind the center point of the intelligent driving vehicle based on the radial component of the target's position in the coordinate system of the intelligent driving vehicle; and determining that there is no potential conflict risk between the target and the intelligent driving vehicle if the target is located behind the center point of the intelligent driving vehicle.

[0020] Some embodiments of this disclosure provide a driving control device, including: a memory; and a processor coupled to the memory, the processor being configured to execute a driving control method based on instructions stored in the memory.

[0021] Some embodiments of this disclosure provide an intelligent driving vehicle, including: a driving control device configured to perform a driving control method.

[0022] Some embodiments of this disclosure provide a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of a driving control method.

[0023] Some embodiments of this disclosure provide a computer program product including computer instructions that, when executed by a processor, implement the steps of a driving control method. Attached Figure Description

[0024] The accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. This disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings.

[0025] Obviously, the accompanying drawings described below are merely some embodiments of this disclosure. Those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0026] Figure 1 shows a schematic diagram of the electrical architecture of an intelligent (autonomous) driving vehicle according to some embodiments of the present disclosure.

[0027] Figure 2 shows a schematic diagram of the external structure of an intelligent (autonomous) driving vehicle according to some embodiments of the present disclosure.

[0028] Figure 3 shows a schematic diagram of a neural network for processing point clouds according to some embodiments of the present disclosure.

[0029] Figure 4 shows a schematic diagram of a driving control method according to some embodiments of the present disclosure.

[0030] Figure 5 shows a schematic diagram illustrating the determination of whether a target is a candidate target for triggering emergency braking, according to some embodiments of this disclosure.

[0031] Figures 6, 7, and 8 illustrate schematic diagrams of determining the braking distance of a target and the braking distance of an intelligent driving vehicle under different conditions according to some embodiments of the present disclosure.

[0032] Figure 9 shows a schematic diagram of a driving control method according to some embodiments of the present disclosure.

[0033] Figure 10 shows a schematic diagram of the structure of a driving control device according to some embodiments of the present disclosure. Detailed Implementation

[0034] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0035] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0036] It should also be understood that in the embodiments disclosed herein, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more.

[0037] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0038] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0039] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0040] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0041] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0042] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0043] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0044] Furthermore, to avoid obscuring this disclosure with unnecessary detail, only processing steps and / or apparatus structures closely related to at least the solutions according to this disclosure are shown in the accompanying drawings, while other details not closely related to this disclosure are omitted. It should also be noted that similar reference numerals and letters in the drawings indicate similar items, and therefore once an item is defined in one drawing, it need not be discussed again in subsequent drawings.

[0045] According to the embodiments of this disclosure, based on rich information such as the speed and acceleration of the perceived target, it can more accurately determine whether there is a risk of conflict between the target and the intelligent driving vehicle, and thus make an accurate decision on whether emergency braking is required.

[0046] Figure 1 shows a schematic diagram of the electrical architecture of an intelligent (autonomous) driving vehicle according to some embodiments of the present disclosure.

[0047] As shown in Figure 1, the intelligent (autonomous) driving vehicle 100 of this embodiment includes, for example, an autonomous driving module 110 and a chassis module 120. Depending on the needs, it may also include a remote monitoring and streaming module 130 and a cargo box module 140. For example, vehicles with remote monitoring requirements include the remote monitoring and streaming module 130; vehicles without remote monitoring requirements may omit it. Similarly, vehicles with cargo-carrying requirements (such as trucks) include the cargo box module 140; vehicles without cargo-carrying requirements (such as passenger cars) may omit it. The intelligent (autonomous) driving vehicle may be, for example, a driverless car, an unmanned delivery vehicle, or an unmanned vending vehicle.

[0048] The autonomous driving module 110 may include, as needed, one or more of the following: a central processing unit (Orin or Xavier module) 111, a traffic light recognition camera 112, a front camera 1131, a rear camera 1132, a left camera 1133, a right camera 1134, a LiDAR 114, a front blind spot radar 1151, a rear blind spot radar 1152, a left blind spot radar 1153, and a right blind spot radar 1154, a positioning module (such as BeiDou, GPS, etc.) 116, an inertial navigation unit 117, and a switch 118. Each camera can communicate with the autonomous driving module. To improve transmission speed and reduce wiring, GMSL (Gigabit Multimedia Serial Links) links can be used for communication. The central processing unit 111 can be implemented using a general-purpose central processing unit, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates, or transistors, etc. The central processing unit 111 can be configured for autonomous driving control.

[0049] The chassis module 120 may include, as needed, one or more of the following: battery 121, power management device 122, chassis controller 123, motor driver 124, drive motor 125, and communication module 126. Battery 121 provides power to the entire autonomous vehicle system. Battery 121 includes a main battery 1211 and a standby battery 1212. When the autonomous vehicle is running, the main battery 1211 powers each module of the autonomous vehicle. When the autonomous vehicle is in standby mode, the standby battery 1212 powers the central processing unit 111 and the communication module 126. The power management device 122 converts the output of battery 121 into different voltage levels usable by each module and controls power-on and power-off. The chassis controller 123 receives motion commands from the autonomous driving module 110 and controls the autonomous vehicle's steering, forward, reverse, and braking. The communication module 126 communicates with a backend server, enabling remote control of the autonomous vehicle by backend operators. The communication module 126 includes a cellular wireless communication device 1261 and a radio frequency communication device 1262. Cellular wireless communication device 1261 communicates using cellular wireless communication technology, such as 2G (second generation), 3G (third generation), 4G (fourth generation), or 5G (fifth generation) cellular wireless communication technology. Radio frequency communication device 1262 communicates using radio frequency communication technology.

[0050] The remote monitoring streaming module 130 may include, as needed, one or more of the following: a front monitoring camera 1311, a rear monitoring camera 1312, a left monitoring camera 1313, a right monitoring camera 1314, and a streaming module 132. The streaming module 132 transmits the video data captured by the monitoring cameras 1311-1314 to the backend server for viewing by backend operators.

[0051] The cargo box module 140 may include a cargo box 141 as needed, which is a cargo-carrying device for the autonomous vehicle. The cargo box module 140 also includes a display and interaction module 142 for interaction between the autonomous vehicle and the user. Users can perform operations such as picking up items, storing goods, and purchasing goods through the display and interaction module 142. The type of cargo box 141 can be changed according to actual needs. For example, in a logistics scenario, the cargo box may include multiple sub-boxes of different sizes, which can be used to load goods for delivery. In a retail scenario, the cargo box can be set as a transparent box so that users can see the products for sale.

[0052] Figure 2 shows a schematic diagram of the external structure of an intelligent (autonomous) driving vehicle according to some embodiments of the present disclosure. Figure 2 shows a schematic diagram of the external structure of an autonomous driving vehicle capable of carrying cargo. Autonomous driving vehicles with different functions can have different external structures. For example, the external structure of an autonomous driving vehicle carrying passengers can be referenced from that of a car. As shown in Figure 2, from the current perspective, the autonomous driving vehicle 200 includes a chassis 210, a cargo box 141, a display and interaction module 142, a right-side camera 1134, a lidar 114, a rear blind spot radar 1152, a left-side blind spot radar 1153, and a right-side blind spot radar 1154.

[0053] The active safety module of the intelligent driving vehicle in this disclosure uses lidar signals as input to perceive surrounding targets and their motion states. LiDAR is denser than millimeter-wave radar, can effectively perceive small targets, and has higher vertical resolution, enabling the separation of static obstacles from the background. Based on the scheme proposed in this disclosure, as described below with reference to Figure 3, the temporal lidar signals can be used to perceive the target's speed, acceleration, and category information. Furthermore, the speed signal predicted by the two-dimensional speed prediction map has high accuracy in both the radial and lateral directions, reflecting the target's true motion state and providing rich and high-quality upstream information for the active safety decisions of the autonomous driving system.

[0054] Figure 3 shows a schematic diagram of a neural network for processing point clouds according to some embodiments of the present disclosure. This point cloud processing neural network can exist in the product shape of a point cloud processing device. As shown in Figure 3, the point cloud processing neural network (point cloud processing device) includes: a feature extraction module 310, configured to extract features from the point cloud data of the current frame of the lidar to obtain a point cloud feature map of the current frame; a temporal fusion module 320, configured to perform temporal fusion processing on the point cloud feature map of the current frame and the fused feature map of the previous frame, that is, to concatenate the point cloud feature map of the current frame and the fused feature map of the previous frame and extract temporal features to obtain a fused feature map of the current frame; and a prediction module 330, configured to predict one or more of the target's velocity, acceleration, and category based on the fused feature map of the current frame to obtain a velocity prediction map, an acceleration prediction map, and a category prediction map.

[0055] The feature extraction module 310 includes, for example, a downsampling network, multiple residual networks, upsampling networks with different step sizes, and a stitching unit. The downsampling network downsamples the point cloud data of the current frame of the LiDAR, outputting a first feature map of the current frame. Multiple cascaded residual networks downsample the first feature map, outputting multiple second feature maps with different downsampling rates. Multiple upsampling networks with different step sizes upsample the multiple second feature maps with different downsampling rates, outputting multiple third feature maps of the same size. The stitching unit stitches together the multiple third feature maps to output the point cloud feature map of the current frame.

[0056] The following is an example of the feature extraction module 310. As shown in Figure 3, the point cloud information of the current frame (time T) is voxelized to form voxelized point cloud data, which serves as the input data. The input data is denoted as [H, W, D], where H, W, and D represent height, width, and depth, respectively. The input data first passes through a downsampling network with a stride of 2 to reduce the feature size and alleviate the computational burden of subsequent networks. The feature map output by the downsampling network is denoted as [H / 2, W / 2, C0], with the height and width reduced to half of their original values, and C0 representing the channel. The structure of the downsampling network is, for example, a convolutional layer with a stride of N (N = 2 in this example) (the convolutional layer performs downsampling), and includes a batch normalization layer and a ReLU (Rectified Linear Unit) activation function layer. Next, the data output by the downsampling network passes through four consecutive residual networks to extract deeper features. These four residual networks all have a convolution stride of, for example, 2, and output feature maps with downsampling rates of 4 / 8 / 16 / 32, denoted as [H / 4,W / 4,C1], [H / 8,W / 8,C2], [H / 16,W / 16,C3], and [H / 32,W / 32,C4], respectively. The height and width are reduced to 1 / 4, 1 / 8, 1 / 16, and 1 / 32 of their original values, respectively. C1, C2, C3, and C4 represent the channels. By introducing cross-layer connections, the input signal is directly added to the output of the residual network, making it easier for the network to propagate gradients during backpropagation, thus solving the gradient vanishing problem in deep network training. Subsequently, the four feature maps from these four residual networks are passed through upsampling networks with strides of 1 / 2 / 4 / 8, outputting four feature maps of the same size with a downsampling rate of 4, denoted as [H / 4, W / 4, C / 4]. The height and width are 1 / 4 of the original, where H, W, and C represent height, width, and channels, respectively. The structure of the upsampling network is, for example, a deconvolutional layer with a stride of N (N=2 in this example) (the deconvolutional layer performs upsampling), along with a batch normalization layer and a ReLU activation function layer. These four upsampled feature maps of the same size represent feature information at different network depths. Shallow features mainly focus on local details and low-level features of the image, which are usually closely related to the image's pixels, including fine-grained information such as color, texture, edges, and corners. Deep features focus on global and high-level features, representing more abstract and complex feature representations, which are usually related to the semantic information of the image. Finally, the multiple feature maps obtained after upsampling are concatenated along the channel dimension by the concatenation unit to obtain the point cloud feature map of the current frame, denoted as [H / 4, W / 4, C].

[0057] The temporal fusion module 320 includes, for example, a coordinate system alignment unit, a stitching unit, and a temporal fusion neural network, and may also include an upsampling network. The coordinate system alignment unit aligns the fused feature map of the previous frame to the coordinate system of the current frame based on the coordinate transformation matrix between the previous and current frames. The stitching unit stitches the aligned fused feature map of the previous frame with the point cloud feature map of the current frame, performing the stitching along the channel dimension. The temporal fusion neural network extracts temporal features from the stitched feature map to obtain the fused feature map of the current frame. The fused feature map of the current frame includes temporal information from all historical frames preceding the current frame, and the fused feature map of the previous frame includes temporal information from all historical frames preceding that previous frame. Specifically, the fused feature map of the initial frame is obtained by extracting temporal features from the point cloud feature map of the initial frame using the temporal fusion neural network. Based on this, through iteration, the fused feature maps of each frame can be obtained. By introducing temporal features, the target motion state can be better perceived. The upsampling network upsamples the fused feature map of the current frame to obtain a fused feature map with the same size as the point cloud data of the current frame.

[0058] The following is an example of the temporal fusion module 320. As shown in Figure 3, the fused feature map of the point cloud of the previous frame (time T-1) (size [H / 4, W / 4, C]) is distorted and aligned to the coordinate system of the current frame (time T) according to the coordinate transformation matrix between the two frames. Then, it is concatenated with the point cloud feature map of the current frame (size [H / 4, W / 4, C]) output by the feature extraction module 310 and input into the temporal fusion neural network. This network compares the point cloud features of the two frames, mines temporal features, and outputs the fused feature map of the current frame (size [H / 4, W / 4, C]). The temporal fusion neural network includes multiple cascaded network blocks (e.g., 3). Each network block includes a convolutional layer (stride of 1), a batch normalization layer, and an activation layer, such as a ReLU activation function layer. After upsampling the fused feature map of the current frame (size [H / 4, W / 4, C]) by a factor of four (step size 4), a fused feature matrix (size [H, W, C]) with the same size as the original image is obtained.

[0059] The prediction module 330 includes, for example, a velocity prediction neural network, an acceleration prediction neural network, and a category prediction neural network. These three neural networks process the fused feature map of the current frame, respectively, and output a velocity prediction map, an acceleration prediction map, and a category prediction map, each with the same dimensions as the original image (original point cloud voxel mesh). The velocity prediction map [H,W,2] has a dimension of 2, representing the radial and lateral components of the target's velocity. In the vehicle's driving plane, radial refers to the vehicle's direction of travel, i.e., the direction of the front of the vehicle, and lateral refers to the direction perpendicular to the vehicle's direction of travel, i.e., the width of the vehicle body. The acceleration prediction map [H,W,1] has a dimension of 1, representing the rate of change of the target's velocity. The category prediction map [H,W,1] has a dimension of 1, representing the one-dimensional index of the target's category. The velocity / acceleration / class prediction neural network consists of multiple cascaded network blocks (e.g., two), each network block including a convolutional layer (with a stride of 1), a batch normalization layer, and an activation layer, such as a ReLU activation function layer, with the last network block cascaded with a convolutional layer (with a stride of 1).

[0060] Point cloud processing neural networks are typically trained before being used for prediction. During training, the velocity and acceleration prediction maps can use the L1 loss function with ground truth for supervision, while the category prediction map can use the Focal Loss function for supervised learning. The coefficients of the three loss functions can be set, for example, to 1.0, 2.0, and 4.0. With this coefficient configuration, velocity, acceleration, and category information can be learned simultaneously, which is beneficial for the convergence of the point cloud processing neural network. Based on the point cloud processing neural network, LiDAR point clouds can be processed to obtain information such as the velocity, acceleration, and category of targets around the autonomous driving vehicle. The target categories include, but are not limited to, vehicles and pedestrians.

[0061] In addition, lidar can calculate the target's position coordinates by measuring the time difference between the emission and reception of the laser pulse, as well as the emission and elevation angles of the laser beam. These position coordinates can be used to describe the target's specific location in space.

[0062] Based on upstream sensing modules, LiDAR, and point cloud processing neural networks (point cloud processing devices), the vehicle can simultaneously perceive motion state information such as the position, speed, acceleration, and category of targets around it.

[0063] The input information of the decision algorithm for driving control of the intelligent driving vehicle in this embodiment includes: (1) motion state information of targets around the intelligent driving vehicle sensed upstream, such as the target's position (x, y), speed (vx, vy), acceleration (ax, ay), and category (optional, set to objType) in the intelligent driving vehicle coordinate system. The intelligent driving vehicle coordinate system is, for example, with the vehicle center as the origin, the direction of the front of the vehicle as the radial (x direction), and the direction of the vehicle width as the lateral (y direction). It is assumed that the direction of the front of the vehicle is the positive radial direction, the direction of the rear of the vehicle is the negative radial direction, and facing the positive radial direction, the left side is the positive lateral direction, and the right side is the negative lateral direction. (2) motion state information of the intelligent driving vehicle (also called the main vehicle) and configuration information of the intelligent driving vehicle itself. The motion state information of the intelligent driving vehicle, such as speed (set to ego_speed), can be obtained directly from the vehicle chassis in real time without the need for prediction by the perception module. The configuration information of the intelligent driving vehicle itself can be obtained directly from the vehicle configuration file. The configuration information of the intelligent driving vehicle itself includes, for example, the distances from the vehicle's center point to the four boundaries of the vehicle's bounding box (front, rear, left, and right), and braking attributes. The distance from the vehicle's center point to the front boundary is set as `front_edge_to_center`, the distance from the vehicle's center point to the rear boundary is set as `back_edge_to_center`, the distance from the vehicle's center point to the left boundary is set as `left_edge_to_center`, and the distance from the vehicle's center point to the right boundary is set as `right_edge_to_center`. The braking attributes include a delay attribute, set as `time_delay`, representing the system transmission delay time from when the vehicle issues a deceleration command to when the vehicle actually begins to decelerate. The braking attributes also include the maximum braking deceleration, set as `ego_max_dec`, representing the maximum braking deceleration the vehicle can achieve after issuing the deceleration command.

[0064] Based on the various input information obtained above for the decision-making algorithm of driving control of intelligent driving vehicles, driving control can be carried out.

[0065] Figure 4 shows a schematic diagram of a driving control method according to some embodiments of the present disclosure. As shown in Figure 4, the driving control method of this embodiment includes the following steps. This driving control method can be executed, for example, by a driving control device of an intelligent driving vehicle (hereinafter referred to as "vehicle").

[0066] In step 410, the position, velocity, and acceleration of the target in the coordinate system of the intelligent driving vehicle, as well as the speed and braking attributes of the intelligent driving vehicle, are obtained according to the aforementioned method.

[0067] In step 420, based on the target's position, velocity, and acceleration in the intelligent driving vehicle coordinate system, as well as the intelligent driving vehicle's speed and braking attributes, it is determined whether there is a risk of conflict between the target and the intelligent driving vehicle.

[0068] Therefore, based on the rich information such as the speed and acceleration of the perceived target, it is possible to more accurately determine whether there is a risk of conflict between the target and the intelligent driving vehicle.

[0069] In some embodiments, step 420 may involve performing steps 420c and 420d to determine whether there is a risk of conflict between the target and the intelligent driving vehicle. Alternatively, at least one of steps 420a and 420b may be performed first to preliminarily eliminate the risk of conflict before performing steps 420c and 420d to verify whether there is a risk of conflict between the target and the intelligent driving vehicle.

[0070] Step 420a: Preliminary assessment of conflict risk. Based on at least one of the target's position in the autonomous vehicle's coordinate system, the target's velocity, and the autonomous vehicle's velocity, determine whether there is a potential conflict risk between the target and the autonomous vehicle. This simple judgment logic quickly assesses potential conflict risks, eliminating cases where there is clearly no conflict risk. For cases where conflict risk cannot be eliminated, further verification of the conflict risk is conducted through subsequent judgments. If a potential conflict risk is determined, the presence of a conflict risk between the target and the autonomous vehicle is determined based on the target's position, velocity, and acceleration in the autonomous vehicle's coordinate system, as well as the autonomous vehicle's velocity and braking attributes.

[0071] Step 420b: Determine if the target is a candidate target for triggering emergency braking. Determine the target's relative speed to the autonomous vehicle, and determine if the target is a candidate target for triggering emergency braking based on whether the ray from the target in the relative speed direction crosses the safe driving range of the autonomous vehicle. This will be described later with reference to Figure 5. If the target is not a candidate target for triggering emergency braking, no further determination is needed; if it is a candidate target, the conflict risk can be further verified through subsequent determinations. If the target is a candidate target for triggering emergency braking, determine whether there is a conflict risk between the target and the autonomous vehicle based on the target's position, speed, and acceleration in the autonomous vehicle's coordinate system, as well as the speed and braking attributes of the autonomous vehicle.

[0072] Step 420c: Determine if there is a risk of conflict in the radial direction. The following description, in conjunction with Figures 6-8, will be used to verify the radial conflict risk situation.

[0073] Step 420d: Determine if there is a risk of conflict in the lateral direction in order to verify the lateral conflict risk situation.

[0074] In step 430, when it is determined that there is a risk of conflict, it is determined that emergency braking needs to be performed. If it is determined that there is no risk of conflict, emergency braking is not required and normal driving can continue.

[0075] Thus, based on rich information such as the speed and acceleration of the perceived target, it is possible to more accurately determine whether there is a risk of conflict between the target and the intelligent driving vehicle, and then accurately decide whether emergency braking is required.

[0076] The method for initially determining the risk of conflict in step 420a is described below.

[0077] In some embodiments, according to the radial component of the position of the target in the coordinate system of the intelligent driving vehicle, it is determined whether the target is behind or in front of the intelligent driving vehicle; the radial component of the speed of the target is compared with the speed of the intelligent driving vehicle; when the target is behind the intelligent driving vehicle and the radial component of the speed of the target is less than the speed of the intelligent driving vehicle, it is determined that there is no potential risk of conflict between the target and the intelligent driving vehicle; when the target is in front of the intelligent driving vehicle and the radial component of the speed of the target is greater than the speed of the intelligent driving vehicle, it is determined that there is no potential risk of conflict between the target and the intelligent driving vehicle.

[0078] For example, if x < front_edge_to_center and vx < ego_speed, it means that the target is behind the intelligent driving vehicle and the radial component of the speed of the target is less than the speed of the intelligent driving vehicle. At this time, the target is located behind the vehicle and gradually lags behind the vehicle, and the distance of lag gradually increases. It can be determined that there is no potential risk of conflict between the target and the intelligent driving vehicle, and emergency braking does not need to be triggered. If x > front_edge_to_center and vx > ego_speed, it means that the target is in front of the intelligent driving vehicle and the radial component of the speed of the target is greater than the speed of the intelligent driving vehicle. At this time, the target will gradually move away from the host vehicle in the radial direction, so it can be determined that there is no potential risk of conflict between the target and the intelligent driving vehicle, and emergency braking does not need to be triggered.

[0079] In some embodiments, according to the lateral component of the position of the target in the coordinate system of the intelligent driving vehicle, it is determined whether the target is on the left or right of the intelligent driving vehicle; when the target is on the left of the intelligent driving vehicle and the lateral component of the speed of the target is to the left, it is determined that there is no potential risk of conflict between the target and the intelligent driving vehicle; when the target is on the right of the intelligent driving vehicle and the lateral component of the speed of the target is to the right, it is determined that there is no potential risk of conflict between the target and the intelligent driving vehicle.

[0080] For example, if y > right_edge_to_center and vy > 0, it means the target is to the left of the autonomous vehicle and its lateral velocity component is to the left. In this case, the target will gradually move away from the vehicle in the lateral left direction, and it can be determined that there is no potential collision risk between the target and the autonomous vehicle, so there is no need to trigger emergency braking. If y < - front_edge_to_center and vy < 0, it means the target is to the right of the autonomous vehicle and its lateral velocity component is to the right. In this case, the target will gradually move away from the vehicle in the lateral right direction, and it can be determined that there is no potential collision risk between the target and the autonomous vehicle, so there is no need to trigger emergency braking.

[0081] In some embodiments, it is determined whether the target is located behind the center point of the intelligent driving vehicle based on the radial component of the target's position in the intelligent driving vehicle's coordinate system; if the target is located behind the center point of the intelligent driving vehicle, it is determined that there is no potential risk of conflict between the target and the intelligent driving vehicle.

[0082] For example, if x < 0, it means the target is located behind the center point of the autonomous vehicle. Since the target is behind the vehicle, there is no potential risk of conflict between the target and the autonomous vehicle, and emergency braking is unnecessary. Active safety modules generally only process targets that are parallel to or in front of the vehicle; they do not need to process targets behind the vehicle. Otherwise, it would lead to more false emergency braking triggers, affecting traffic efficiency and driving experience.

[0083] Therefore, based on the target's location, and combined with the target's speed and the speed of the autonomous vehicle, potential conflict risks can be quickly assessed through simple judgment logic. Situations where there is clearly no conflict risk can be ruled out. For situations where conflict risk cannot be ruled out, further judgments can be made to verify the conflict risk.

[0084] The method for determining whether a target is a candidate target for triggering emergency braking in step 420b is described below.

[0085] As shown in Figure 5, the relative speed of the target relative to the autonomous vehicle is determined. Starting from the target's position and pointing in the direction of the relative speed, a ray is drawn. If the ray in the relative speed direction crosses the safe driving range of the autonomous vehicle, it indicates that the target is approaching the vehicle with no risk of conflict, and the target is determined to be a candidate target for triggering emergency braking. If the ray in the relative speed direction does not cross the safe driving range of the autonomous vehicle, the target is determined not to be a candidate target for triggering emergency braking. The safe driving range of the autonomous vehicle can be the bounding box of the autonomous vehicle with a certain safety buffer distance, represented by an expanded box. If the target is not a candidate target for triggering emergency braking, no further judgment is needed; if it is a candidate target for triggering emergency braking, the risk of conflict can be further verified through subsequent judgments.

[0086] The method for determining whether there is a risk of conflict in the radial direction in step 420c is described below.

[0087] In some embodiments, a risk of conflict between the target and the intelligent driving vehicle in the radial direction is determined based on the radial components of the target's velocity and acceleration at the last trajectory point, the speed and braking attributes of the intelligent driving vehicle, and the radial distance between the target and the intelligent driving vehicle. The radial distance between the target and the intelligent driving vehicle is determined based on the radial component of the target's position in the intelligent driving vehicle's coordinate system.

[0088] Therefore, it is possible not only to accurately determine whether there is a risk of conflict between the target and the intelligent driving vehicle, but also to further determine that the direction of the risk of conflict is radial.

[0089] In some embodiments, the braking distances of the target and the autonomous vehicle are determined based on the radial components of the target's velocity and acceleration, as well as the speed and braking attributes of the autonomous vehicle. By comparing the difference between the braking distance of the autonomous vehicle and the target with the radial distance between the target and the autonomous vehicle, it is determined whether there is a risk of conflict between the target and the autonomous vehicle in the radial direction, in order to verify the radial conflict risk situation. If the difference between the braking distance of the autonomous vehicle and the target is greater than the difference between the radial distance between the target and the autonomous vehicle and the safety buffer distance, it is determined that there is a risk of conflict between the target and the autonomous vehicle in the radial direction. The radial distance between the target and the autonomous vehicle is determined based on the radial component of the target's position in the autonomous vehicle's coordinate system.

[0090] Due to system transmission delay, the autonomous vehicle first travels at the current speed for the duration of the system transmission delay (time_delay), and then performs emergency braking according to the slope of the maximum braking deceleration (ego_max_dec)d. The braking distance of the autonomous vehicle is the area enclosed by the solid line and the x-axis from t=0 to t=t_ego. If the target is traveling with a certain acceleration, the target's braking distance is the area enclosed by the dashed line and the x-axis from t=0 to t=t_target, with positive values ​​above the x-axis and negative values ​​below. The following sections determine the target's braking distance and the autonomous vehicle's braking distance in three different scenarios.

[0091] The first scenario: As shown in Figure 6, the target's initial velocity is in the same direction as the autonomous vehicle (vx>0), and the two velocities will not reach the same level at any given moment during the autonomous vehicle's deceleration to 0. In this case, t_ego is the moment when the autonomous vehicle decelerates to 0, t_target is the moment when the target decelerates to 0, and the slope (acceleration) of the velocity v-time t graph is the target's acceleration.

[0092] If the radial components of the velocity of the target and the autonomous vehicle are in the same direction, and if the velocities of the autonomous vehicle and the target will not reach the same at any time during the deceleration process of the autonomous vehicle at its maximum braking deceleration, the distance traveled by the autonomous vehicle from its current speed to zero at its maximum braking deceleration is determined as the braking distance of the autonomous vehicle, and the distance traveled by the target from its current speed radial component to zero at its acceleration radial component is determined as the braking distance of the target.

[0093] The second scenario: As shown in Figure 7, the target's initial velocity is in the same direction as the autonomous vehicle (vx>0), and during the autonomous vehicle's deceleration to 0, their velocities will reach the same point at some moment. In this case, t_ego and t_target represent the moment when their velocities reach the same point, and the slope (acceleration) of the velocity v-time t graph is the target's acceleration.

[0094] When the radial components of the target's and the autonomous vehicle's velocities are in the same direction, if the speeds of the autonomous vehicle and the target reach the same point at a certain moment during the autonomous vehicle's deceleration at its maximum braking deceleration, the distance traveled by the autonomous vehicle from its current speed to the moment when its speeds are the same is determined as the braking distance of the autonomous vehicle, and the distance traveled by the target from its current speed radial component to the moment when its speeds are the same is determined as the braking distance of the target.

[0095] The third scenario: As shown in Figure 8, the target's initial velocity is in the opposite direction to the autonomous vehicle (vx < 0). In this case, t_ego represents the moment the autonomous vehicle decelerates to 0, and t_target represents the moment the target decelerates to 0. The slope (acceleration) of the target's velocity-time t graph can be a pre-set maximum deceleration target_max_dec, rather than the perceived deceleration of the target's actual motion. This is because the situation of the target traveling in the opposite direction is special, requiring the assumption that the other party immediately brakes at the maximum deceleration. If there is a danger under this assumption, the autonomous vehicle needs to take emergency braking measures.

[0096] When the radial component of the target's velocity is opposite to that of the autonomous driving vehicle, the distance traveled by the autonomous driving vehicle during the process of decelerating from the current speed to zero according to the maximum braking deceleration is determined as the braking distance of the autonomous driving vehicle, and the distance traveled by the target during the process of decelerating from the current speed radial component to zero according to the preset maximum deceleration is determined as the braking distance of the target.

[0097] After determining the braking distances of the autonomous vehicle and the target, if the difference between their braking distances is greater than the difference between the radial distance between the target and the autonomous vehicle and the safety buffer distance, a radial collision risk is identified between the target and the autonomous vehicle. The radial distance between the target and the autonomous vehicle is determined based on the radial component of the target's position in the autonomous vehicle's coordinate system. That is, if "autonomous vehicle braking distance - target braking distance > target radial distance (x) - safety buffer distance," a radial collision risk is identified, and further assessment of lateral collision risk is required. Otherwise, no radial collision risk is identified, and emergency braking is not triggered.

[0098] The method for determining whether there is a risk of conflict in the lateral direction in step 420d is described below.

[0099] In some embodiments, the risk of conflict between the target and the intelligent driving vehicle in the lateral direction is determined based on the target's position, velocity, and lateral acceleration components in the intelligent driving vehicle coordinate system at the last trajectory point, as well as the speed and width range of the intelligent driving vehicle.

[0100] Therefore, it is possible not only to accurately determine whether there is a risk of conflict between the target and the intelligent driving vehicle, but also to further determine that the direction of the conflict risk is from the lateral direction.

[0101] In some embodiments, based on the target's position, velocity, and lateral acceleration components in the autonomous vehicle's coordinate system, and the autonomous vehicle's velocity, it is determined whether the target will move into the width range of the autonomous vehicle in the lateral direction. Based on whether the target will move into the width range of the autonomous vehicle in the lateral direction, it is determined whether there is a risk of conflict between the target and the autonomous vehicle in the lateral direction, in order to verify the lateral conflict risk situation. If the target will move into the width range of the autonomous vehicle in the lateral direction, it is determined that there is a risk of conflict between the target and the autonomous vehicle in the lateral direction.

[0102] In some embodiments, the method for determining whether a target will move within the width range of an intelligent driving vehicle in the lateral direction includes: determining the time of conflict between the target and the intelligent driving vehicle in the radial direction according to the radial distance of the target from the intelligent driving vehicle and the difference between the speed of the intelligent driving vehicle, ego_speed, and the radial component of the target's speed, vx. The radial distance of the target from the intelligent driving vehicle is determined according to the radial component of the target's position in the coordinate system of the intelligent driving vehicle, x. That is, the time of conflict in the radial direction, t, is calculated according to t = x / (ego_speed - vx); determining the lateral component of the target's position at the time of conflict according to the current lateral component of the target's position, y, the lateral component of the speed, vy, and the lateral component of the acceleration, ay, in the coordinate system of the intelligent driving vehicle, and the time of conflict, t. That is, the lateral component of the target's position at the time of conflict, y', is determined according to y' = y + vy × t + ay × t × t / 2; determining whether the target will move within the width range of the intelligent driving vehicle in the lateral direction according to the lateral component of the target's position at the time of conflict. That is, if y' > -right_edge_to_center and y' < left_edge_to_center, it indicates that the target will appear within the width range of the intelligent driving vehicle.

[0103] An example of determining whether there is a conflict risk between a target and an intelligent driving vehicle and the corresponding driving control is described below with reference to FIG. 9. The steps and their execution order in this example are only an example of driving control, and the steps and their execution order can also be executed by other methods.

[0104] FIG. 9 shows a schematic diagram of a driving control method according to some embodiments of the present disclosure. As shown in FIG. 9, the driving control method of this embodiment includes the following steps.

[0105] In step 910, if the target is located behind the center point of the intelligent driving vehicle (x < 0), it is determined that there is no conflict risk, and there is no need to trigger the emergency braking of the intelligent driving vehicle. The active safety module generally only processes targets that are flush with or in front of the intelligent driving vehicle, and does not need to process targets behind, otherwise it will cause more false triggers of emergency braking, affecting the traffic efficiency and driving experience of the intelligent driving vehicle.

[0106] In step 920, if the target is located behind the front boundary of the intelligent driving vehicle and the radial speed is less than the speed of the intelligent driving vehicle, it means that the target is located behind the intelligent driving vehicle and is gradually falling behind the intelligent driving vehicle, and the distance from the intelligent driving vehicle is gradually increasing. It is determined that there is no conflict risk, and there is no need to trigger the emergency braking of the intelligent driving vehicle.

[0107] In step 930, if the target is in front of the intelligent driving vehicle and the radial velocity is greater than the speed of the intelligent driving vehicle, it indicates that the target will gradually move away from the intelligent driving vehicle in the radial direction. It is determined that there is no conflict risk, and there is no need to trigger the emergency braking of the intelligent driving vehicle.

[0108] In step 940, if the target is on the left side of the intelligent driving vehicle and the lateral velocity is to the left, it indicates that the target will gradually move away from the intelligent driving vehicle in the lateral left direction. It is determined that there is no conflict risk, and there is no need to trigger the emergency braking of the intelligent driving vehicle.

[0109] In step 950, if the target is on the right side of the intelligent driving vehicle and the lateral velocity is to the right, it indicates that the target will gradually move away from the intelligent driving vehicle in the lateral right direction. It is determined that there is no conflict risk, and there is no need to trigger the emergency braking of the intelligent driving vehicle.

[0110] In step 960, calculate the relative velocity of the target with respect to the intelligent driving vehicle. Starting from the target position, draw a ray in the direction of the relative velocity. Determine whether the ray passes through the extended box of the intelligent driving vehicle. If the ray passes through the extended box of the intelligent driving vehicle, it indicates that the target is approaching the intelligent driving vehicle and there is a certain conflict risk, and it is necessary to enter the subsequent determination process for further verification, that is, continue to execute step 970; otherwise, there is no conflict risk between the target and the intelligent driving vehicle, and there is no need to trigger emergency braking. Among them, a certain safety buffer distance needs to be reserved during driving, so the extended box of the intelligent driving vehicle is used for risk determination. A certain range outside the boundary box of the intelligent driving vehicle is used as the extended box.

[0111] In step 970, determine whether there is a conflict risk in the radial direction (x direction) and whether emergency braking needs to be taken. Referring to the description of the foregoing step 420c, after calculating the braking distances of the intelligent driving vehicle and the target, if "the braking distance of the intelligent driving vehicle - the braking distance of the target > the radial distance of the target from the intelligent driving vehicle (x) - the safety buffer distance", it is determined that there is a conflict risk in the radial direction, and continue with the subsequent determination; otherwise, it is determined that there is no conflict risk in the radial direction, and there is no need to trigger emergency braking.

[0112] [[ID=1!5]]In step 980, determine whether there is a conflict risk in the lateral direction (y direction) and whether emergency braking needs to be taken. Referring to the description of the foregoing step 420d, calculate the time t of conflict in the radial direction, and calculate the lateral position y' of the target at the conflict moment. If y' > -right_edge_to_center and y' < left_edge_to_center, it means that the target will appear within the width range of the intelligent driving vehicle, and there is a conflict risk. If there is a conflict risk in both the x direction and the y direction, it is determined that emergency braking needs to be executed.

[0113] Therefore, based on the rich information such as the speed and acceleration of the perceived target, it is possible to more accurately determine whether there is a risk of conflict between the target and the intelligent driving vehicle, and thus make an accurate decision on whether emergency braking is necessary.

[0114] Figure 10 shows a schematic diagram of the structure of a driving control device according to some embodiments of the present disclosure. The intelligent driving vehicle includes a driving control device, which can execute the driving control methods in various embodiments.

[0115] As shown in FIG10, the driving control device 1000 of this embodiment includes: a memory 1010 and a processor 1020 coupled to the memory 1010. The processor 1020 is configured to execute the driving control method in any of the foregoing embodiments based on instructions stored in the memory 1010.

[0116] The driving control device 1000 may also include an input / output interface 1030, a network interface 1040, a storage interface 1050, etc. These interfaces 1030, 1040, 1050, as well as the memory 1010 and the processor 1020, can be connected, for example, via a bus 1060.

[0117] The memory 1010 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory may store, for example, the operating system, application programs, boot loader, and other programs.

[0118] The processor 1020 can be implemented using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates, or transistors, or other discrete hardware components.

[0119] The input / output interface 1030 provides a connection interface for input / output devices such as monitors, mice, keyboards, and touchscreens. The network interface 1040 provides a connection interface for various networked devices. The storage interface 1050 provides a connection interface for external storage devices such as SD cards and USB flash drives. The bus 1060 can use any bus architecture from various bus structures. For example, bus architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, and Peripheral Component Interconnect (PCI) bus.

[0120] This disclosure provides a computer program including computer instructions that, when executed by a processor, implement a driving control method.

[0121] This disclosure provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement a driving control method.

[0122] This disclosure provides a computer program product including computer instructions that, when executed by a processor, implement a driving control method.

[0123] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more (non-transitory) computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, cloud storage, etc.) containing computer program code. A computer program product should be understood as a software product that primarily implements its solution through a computer program.

[0124] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0127] The above description is only a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A driving control method, comprising: Acquire the target's position, velocity, and acceleration in the autonomous driving vehicle's coordinate system, as well as the autonomous driving vehicle's speed and braking attributes; Based on the target's position, velocity, and acceleration in the autonomous vehicle's coordinate system, as well as the autonomous vehicle's speed and braking properties, determine whether there is a risk of conflict between the target and the autonomous vehicle. If a risk of conflict is identified, it is determined that emergency braking is necessary.

2. The driving control method according to claim 1, wherein, Determining whether there is a conflict risk between the target and the autonomous vehicle includes: Determine the relative speed of the target with respect to the autonomous vehicle, and determine whether the target is a candidate target for triggering emergency braking based on whether the ray of the target in the direction of relative speed crosses the safe driving range of the autonomous vehicle. When the target is a candidate target that could trigger emergency braking, the system determines whether there is a risk of conflict between the target and the intelligent driving vehicle based on the target's position, velocity, and acceleration in the intelligent driving vehicle's coordinate system, as well as the intelligent driving vehicle's speed and braking attributes.

3. The driving control method according to any one of claims 1-2, wherein, Determining whether there is a conflict risk between the target and the autonomous vehicle includes: Based on the radial components of the target's velocity and acceleration, as well as the speed and braking properties of the autonomous vehicle, determine the target's braking distance and the autonomous vehicle's braking distance. By comparing the difference between the braking distance of the autonomous vehicle and the braking distance of the target with the radial distance between the target and the autonomous vehicle, it is determined whether there is a risk of conflict between the target and the autonomous vehicle in the radial direction. The radial distance between the target and the autonomous vehicle is determined based on the radial component of the target's position in the autonomous vehicle's coordinate system.

4. The driving control method according to claim 3, wherein, Determining the braking distance of the target and the braking distance of the autonomous vehicle includes: If the radial components of the velocity of the target and the autonomous vehicle are in the same direction, and if the velocities of the autonomous vehicle and the target will not reach the same at any time during the deceleration process of the autonomous vehicle at its maximum braking deceleration, the distance traveled by the autonomous vehicle from its current speed to zero at its maximum braking deceleration is determined as the braking distance of the autonomous vehicle, and the distance traveled by the target from its current speed radial component to zero at its acceleration radial component is determined as the braking distance of the target.

5. The driving control method according to any one of claims 3-4, wherein, Determining the braking distance of the target and the braking distance of the autonomous vehicle includes: When the radial components of the target's and the autonomous vehicle's velocities are in the same direction, if the speeds of the autonomous vehicle and the target reach the same point at a certain moment during the autonomous vehicle's deceleration at its maximum braking deceleration, the distance traveled by the autonomous vehicle from its current speed to the moment when its speeds are the same is determined as the braking distance of the autonomous vehicle, and the distance traveled by the target from its current speed radial component to the moment when its speeds are the same is determined as the braking distance of the target.

6. The driving control method according to any one of claims 3-5, wherein, Determining the braking distance of the target and the braking distance of the autonomous vehicle includes: When the radial component of the target's velocity is opposite to that of the autonomous driving vehicle, the distance traveled by the autonomous driving vehicle during the process of decelerating from the current speed to zero according to the maximum braking deceleration is determined as the braking distance of the autonomous driving vehicle, and the distance traveled by the target during the process of decelerating from the current speed radial component to zero according to the preset maximum deceleration is determined as the braking distance of the target.

7. The driving control method according to any one of claims 3-6, wherein, Determining whether there is a risk of conflict between the target and the autonomous vehicle in the radial direction includes: If the difference between the braking distance of the autonomous vehicle and the braking distance of the target is greater than the difference between the radial distance between the target and the autonomous vehicle and the safety buffer distance, it is determined that there is a risk of conflict between the target and the autonomous vehicle in the radial direction. The radial distance between the target and the autonomous vehicle is determined based on the radial component of the target's position in the autonomous vehicle's coordinate system.

8. The driving control method according to any one of claims 1-7, wherein, Determining whether there is a conflict risk between the target and the autonomous vehicle includes: Based on the target's position, velocity, and lateral acceleration components in the autonomous driving vehicle's coordinate system, as well as the autonomous driving vehicle's velocity, determine whether the target will move into the width range of the autonomous driving vehicle in the lateral direction. Based on whether the target will move into the width range of the intelligent driving vehicle in the lateral direction, determine whether there is a risk of conflict between the target and the intelligent driving vehicle in the lateral direction.

9. The driving control method according to claim 8, wherein, Determining whether a target will move into the width range of the autonomous driving vehicle in the lateral direction includes: The time when the target and the intelligent driving vehicle collide in the radial direction is determined based on the radial distance between the target and the intelligent driving vehicle, and the difference between the radial components of the speed of the intelligent driving vehicle and the speed of the target. The radial distance between the target and the intelligent driving vehicle is determined based on the radial component of the target's position in the coordinate system of the intelligent driving vehicle. Based on the target's current position lateral component, velocity lateral component, and acceleration lateral component in the intelligent driving vehicle coordinate system, as well as the time of the collision, determine the target's position lateral component at the time of the collision. Based on the lateral component of the target's position at the time of the conflict, determine whether the target will move into the width range of the autonomous driving vehicle in the lateral direction.

10. The driving control method according to any one of claims 1-9, wherein, Determining whether there is a conflict risk between the target and the autonomous vehicle includes: Based on at least one of the target's position in the autonomous vehicle's coordinate system, the target's velocity, and the autonomous vehicle's velocity, determine whether there is a potential risk of conflict between the target and the autonomous vehicle. In cases where a potential conflict risk is identified, the risk of conflict between the target and the intelligent driving vehicle is determined based on the target's position, velocity, and acceleration in the intelligent driving vehicle's coordinate system, as well as the intelligent driving vehicle's speed and braking attributes.

11. The driving control method according to claim 10, wherein, Determining whether there are potential conflicts between the target and the autonomous vehicle includes: Based on the radial component of the target's position in the coordinate system of the intelligent driving vehicle, determine whether the target is located behind or in front of the intelligent driving vehicle. Compare the radial component of the target's velocity with the velocity of the autonomous vehicle; If the target is located behind the autonomous vehicle and the radial component of the target's velocity is less than the velocity of the autonomous vehicle, it is determined that there is no potential risk of conflict between the target and the autonomous vehicle. If the target is located in front of the autonomous vehicle and the radial component of the target's velocity is greater than the speed of the autonomous vehicle, it is determined that there is no potential risk of conflict between the target and the autonomous vehicle.

12. The driving control method according to any one of claims 10-11, wherein, Determining whether there are potential conflicts between the target and the autonomous vehicle includes: Based on the lateral component of the target's position in the autonomous driving vehicle's coordinate system, determine whether the target is located to the left or right of the autonomous driving vehicle. If the target is located to the left of the autonomous vehicle and the lateral component of the target's velocity is to the left, it is determined that there is no potential risk of conflict between the target and the autonomous vehicle. If the target is located to the right of the autonomous vehicle and the lateral component of the target's velocity is to the right, it is determined that there is no potential risk of conflict between the target and the autonomous vehicle.

13. The driving control method according to any one of claims 10-12, wherein, Determining whether there are potential conflicts between the target and the autonomous vehicle includes: Based on the radial component of the target's position in the intelligent driving vehicle's coordinate system, determine whether the target is located behind the center point of the intelligent driving vehicle. When the target is located behind the center point of the autonomous vehicle, it is determined that there is no potential risk of conflict between the target and the autonomous vehicle.

14. A driving control device, comprising: Memory; And a processor coupled to the memory, the processor being configured to execute the driving control method of any one of claims 1-13 based on instructions stored in the memory.

15. An intelligent driving vehicle, comprising: A driving control device is configured to perform the driving control method according to any one of claims 1-13.

16. A computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement the steps of the driving control method according to any one of claims 1-13.

17. A computer program product comprising computer instructions that, when executed by a processor, implement the steps of the driving control method according to any one of claims 1-13.

Images