Driving control method, driving control device and intelligent driving vehicle

Abstract

The present disclosure proposes a driving control method, a driving control device and an intelligent driving vehicle, which relate to the computer field, and in particular to the intelligent driving field. The driving control method includes: obtaining the position, speed and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle; determining whether there is a risk of conflict between the target and the intelligent driving vehicle according to the position, speed and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle; and determining the need to perform emergency braking when it is determined that there is a risk of conflict. Based on the rich information such as the speed and acceleration of the target, it is more accurately determined whether there is a risk of conflict between the target and the intelligent driving vehicle, and then an accurate decision is made whether emergency braking is required.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, in particular to the field of intelligent driving, and more particularly to a driving control method, a driving control device and an intelligent driving vehicle. Background Art

[0002] Intelligent driving vehicles are used to automatically transport people or objects from one location to another. They collect environmental information through sensors on the vehicle and complete automatic transportation. Intelligent driving vehicles controlled by autonomous driving technology for logistics transportation have greatly improved the convenience of production and life and saved labor costs.

[0003] The active safety module is an important component in the autonomous driving system. It senses and makes decisions about the driving environment. When a dangerous situation occurs, it sends emergency braking commands to the vehicle chassis to ensure vehicle safety.

[0004] 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

[0005] The disclosed embodiment more accurately determines whether there is a risk of conflict between the target and the intelligent driving vehicle based on rich information such as the speed and acceleration of the target, and then accurately decides whether emergency braking is required.

[0006] Some embodiments of the present disclosure provide a driving control method, including: obtaining a position, speed, and acceleration of a target in a smart driving vehicle coordinate system, as well as a speed and braking property of the smart driving vehicle; determining whether there is a risk of conflict between the target and the smart driving vehicle based on the position, speed, and acceleration of the target in the smart driving vehicle coordinate system, as well as the speed and braking property of the smart driving vehicle; and determining the need to perform emergency braking when it is determined that there is a risk of conflict.

[0007] In some embodiments, determining whether there is a risk of conflict between a target and an intelligent driving vehicle includes: determining a relative speed of the target with respect to the intelligent driving vehicle, and determining whether the target is a candidate target for triggering emergency braking based on whether a ray of the target in the direction of the relative speed passes through a safe driving range of the intelligent driving 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 intelligent driving vehicle based on the position, speed, and acceleration of the target in the coordinate system of the intelligent driving vehicle and the speed and braking properties of the intelligent driving vehicle.

[0008] In some embodiments, determining whether there is a risk of conflict between a target and an intelligent driving vehicle includes: determining a braking distance of the target and a braking distance of the intelligent driving vehicle based on a radial component of velocity and a radial component of acceleration of the target and a speed and braking property of the intelligent driving vehicle; determining whether there is a risk of conflict between the target and the intelligent driving vehicle in a radial direction by comparing a difference between the braking distance of the intelligent driving vehicle and the braking distance of the target with a radial distance of the target from the intelligent driving vehicle, wherein the radial distance of the target from the intelligent driving vehicle is determined based on a radial component of a position of the target in a coordinate system of the intelligent driving vehicle.

[0009] In some embodiments, determining the braking distance of the target and the braking distance of the smart driving vehicle includes: when the radial components of the velocities of the target and the smart driving vehicle are in the same direction, if the speeds of the smart driving vehicle and the target do not become the same at a certain moment during the process of the smart driving vehicle decelerating at the maximum braking deceleration, the distance traveled by the smart driving vehicle during the process of decelerating from the current speed to zero at the maximum braking deceleration is determined as the braking distance of the smart driving vehicle, and the distance traveled by the target during the process of decelerating from the current radial component of the speed to zero at the radial component of the acceleration is determined as the braking distance of the target.

[0010] In some embodiments, determining the braking distance of the target and the braking distance of the smart driving vehicle includes: when the radial components of the velocities of the target and the smart driving vehicle are in the same direction, if the speeds of the smart driving vehicle and the target become the same at a certain moment during the deceleration of the smart driving vehicle at the maximum braking deceleration, the distance traveled by the smart driving vehicle during the process of decelerating from the current speed at the maximum braking deceleration to the moment when the speeds are the same is determined as the braking distance of the smart driving vehicle, and the distance traveled by the target during the process of traveling from the current speed radial component at the acceleration radial component to the moment when the speeds are the same 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 smart driving vehicle includes: when the radial components of the speed of the target and the smart driving vehicle are in opposite directions, the distance traveled by the smart 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 smart 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.

[0012] In some embodiments, determining whether there is a risk of conflict between the target and the intelligent driving vehicle in the radial direction includes: when 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 of the target from the intelligent driving vehicle and the safety buffer distance, determining that there is a risk of conflict between the target and the intelligent driving vehicle in the radial direction, wherein the radial distance of the target from the intelligent driving vehicle is determined based on the radial component of the position of the target in the coordinate system of the intelligent driving vehicle.

[0013] In some embodiments, determining whether there is a risk of conflict between a target and an intelligent driving vehicle includes: determining whether the target will move in a lateral direction within the width of the intelligent driving vehicle based on the position, velocity and lateral component of acceleration of the target in the intelligent driving vehicle 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 a lateral direction based on whether the target will move in the lateral direction within the width of the intelligent driving vehicle.

[0014] In some embodiments, determining whether a target will move in a lateral direction within the width of the smart driving vehicle includes: determining a time when a collision occurs between the target and the smart driving vehicle in a radial direction based on a radial distance of the target from the smart driving vehicle and a difference between a speed of the smart driving vehicle and a radial component of a speed of the target, wherein the radial distance of the target from the smart driving vehicle is determined based on a radial component of a position of the target in a coordinate system of the smart driving vehicle; determining a lateral component of a position of the target at the time of the collision based on a lateral component of a current position of the target in the coordinate system of the smart driving vehicle, a lateral component of a speed, and a lateral component of an acceleration, and the time of the collision; and determining whether the target will move in a lateral direction within the width of the smart driving vehicle based on the lateral component of the position of the target at the time of the collision.

[0015] In some embodiments, determining whether there is a risk of conflict between a target and an 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 position of the target in the intelligent driving vehicle coordinate system, the speed of the target, and the speed of the intelligent driving vehicle; if it is determined that there is a potential risk of conflict, determining whether there is a risk of conflict between the target and the intelligent driving vehicle based on the position, speed, and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle.

[0016] In some embodiments, determining whether there is a potential risk of conflict between a target and an intelligent driving vehicle includes: determining whether the target is located behind or in front of the intelligent driving vehicle based on a radial component of the position of the target in the coordinate system of the intelligent driving vehicle; comparing a radial component of the speed of the target with the speed of the intelligent driving vehicle; if the target is located 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, determining that there is no potential risk of conflict between the target and the intelligent driving vehicle; if the target is located 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, determining that there is no potential risk of conflict between the target and the intelligent driving vehicle.

[0017] In some embodiments, determining whether there is a potential risk of conflict between a target and an 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 position of the target in the intelligent driving vehicle coordinate system; when 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, determining that there is no potential risk of conflict between the target and the intelligent driving vehicle; when 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, determining that there is no potential risk of conflict between the target and the intelligent driving vehicle.

[0018] In some embodiments, determining whether there is a potential risk of conflict 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 position of the target in the intelligent driving vehicle coordinate system; and determining that there is no potential risk of conflict between the target and the intelligent driving vehicle if the target is located behind the center point of the intelligent driving vehicle.

[0019] Some embodiments of the present disclosure provide a driving control device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute a driving control method based on instructions stored in the memory.

[0020] Some embodiments of the present disclosure provide an intelligent driving vehicle, comprising: a driving control device configured to execute a driving control method.

[0021] Some embodiments of the present disclosure provide a computer-readable storage medium having computer instructions stored thereon, which implement the steps of a driving control method when executed by a processor.

[0022] Some embodiments of the present disclosure provide a computer program product, including computer instructions, which implement the steps of a driving control method when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. The present disclosure can be more clearly understood according to the following detailed description with reference to the drawings.

[0024] Obviously, the drawings described below are only some embodiments of the present disclosure, and a person skilled in the art can obtain other drawings based on these drawings without creative work.

[0025] Figure 1 A schematic diagram showing the electrical architecture of an intelligent (autonomous) driving vehicle according to some embodiments of the present disclosure.

[0026] Figure 2 A schematic diagram showing the appearance and structure of an intelligent (automatic) driving vehicle according to some embodiments of the present disclosure.

[0027] Figure 3 A schematic diagram showing a neural network for processing point clouds according to some embodiments of the present disclosure.

[0028] Figure 4 A schematic diagram showing a driving control method according to some embodiments of the present disclosure.

[0029] Figure 5 A schematic diagram showing some embodiments of the present disclosure for determining whether a target is a candidate target for triggering emergency braking.

[0030] Figure 6 , Figure 7 and Figure 8 A schematic diagram showing how to determine a braking distance of a target and a braking distance of an intelligent driving vehicle under different circumstances according to some embodiments of the present disclosure.

[0031] Fig. 9 A schematic diagram showing a driving control method according to some embodiments of the present disclosure.

[0032] Fig.10 A schematic diagram showing the structure of a driving control device according to some embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0033] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0034] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0035] It should also be understood that in the embodiments of the present disclosure, “plurality” may refer to two or more than two, and “at least one” may refer to one, two, or more than two.

[0036] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0037] In addition, the term "and / or" in the present disclosure is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are in an "or" relationship.

[0038] It should also be understood that the description of the various embodiments in the present disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0039] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0040] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0041] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0042] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0043] In addition, in order to avoid obscuring the present disclosure due to unnecessary details, only the processing steps and / or device structures closely related to at least the scheme according to the present disclosure are shown in the drawings, and other details that are not closely related to the present 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 does not need to be discussed again for subsequent drawings.

[0044] Figure 1 A schematic diagram showing the electrical architecture of an intelligent (autonomous) driving vehicle according to some embodiments of the present disclosure.

[0045] like Figure 1As shown, the intelligent (automatic) driving vehicle 100 of this embodiment includes, for example, an automatic driving module 110 and a chassis module 120, and may also include a remote monitoring streaming module 130 and a cargo box module 140 as needed. For example, a vehicle with a remote monitoring requirement is provided with a remote monitoring streaming module 130, and a vehicle without a remote monitoring requirement may not be provided with a remote monitoring streaming module 130. For another example, a vehicle with a cargo carrying requirement (such as a truck) is provided with a cargo box module 140, and a vehicle without a cargo carrying requirement (such as a manned car) may not be provided with a cargo box module 140. The intelligent (automatic) driving vehicle may be, for example, an unmanned vehicle, an unmanned delivery vehicle, an unmanned vending vehicle, etc.

[0046] The autonomous driving module 110 includes, as required, one or more of a central processor (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 laser radar 114, a front blind spot radar 1151, a rear blind spot radar 1152, a left blind spot radar 1153, a right blind spot radar 1154, a positioning module (such as Beidou, GPS, etc.) 116, an inertial navigation unit 117, a switch 118, etc. Each camera can communicate with the autonomous driving module. In order to increase the transmission speed and reduce the wiring harness, GMSL (Gigabit Multimedia Serial Links) link communication can be used. The central processor 111 may be implemented by a general-purpose central 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. The central processor 111 may be configured to perform autonomous driving control.

[0047] The chassis module 120 includes, for example, one or more of a battery 121, a power management device 122, a chassis controller 123, a motor driver 124, a power motor 125, and a communication module 126 as required. The battery 121 provides power for the entire autonomous driving vehicle system. The battery 121 includes a main battery 1211 and a standby battery 1212. When the autonomous driving vehicle is running, the main battery 1211 supplies power to each module of the autonomous driving vehicle. When the autonomous driving vehicle is on standby, the standby battery 1212 supplies power to the central processor 111 and the communication module 126. The power management device 122 converts the output of the battery 121 into different levels of voltage that can be used by each module, and controls power on and off. The chassis controller 123 receives motion instructions issued by the autonomous driving module 110, and controls the steering, forward, backward, braking, etc. of the autonomous driving vehicle. The communication module 126 communicates with the background server, and can realize the remote control of the autonomous driving vehicle by the background operator. The communication module 126 includes a cellular wireless communication device 1261 and a radio frequency communication device 1262. The 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. The radio frequency communication device 1262 communicates using radio frequency communication technology.

[0048] The remote monitoring streaming module 130 includes, as required, one or more of the front monitoring camera 1311, the rear monitoring camera 1312, the left monitoring camera 1313, the right monitoring camera 1314 and the streaming module 132. The streaming module 132 transmits the video data collected by the monitoring cameras 1311-1314 to the backend server for viewing by the backend operator.

[0049] The cargo box module 140 includes, for example, a cargo box 141 as required, which is a cargo carrying device of the autonomous driving vehicle. The cargo box module 140 is also provided with a display interaction module 142 for the autonomous driving vehicle to interact with the user. The user can perform operations such as picking up, depositing, and purchasing goods through the display 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 can 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.

[0050] Figure 2 A schematic diagram showing the appearance and structure of an intelligent (automatic) driving vehicle according to some embodiments of the present disclosure. Figure 2 The figure shows the appearance structure of an autonomous driving vehicle that can carry cargo. Autonomous driving vehicles with different functions can have different appearance structures. For example, the appearance structure of an autonomous driving vehicle that can carry people can refer to that of a car. Figure 2As shown, from the current perspective, one can see the chassis 210, cargo box 141, display interaction module 142, right camera 1134, lidar 114, rear blind spot radar 1152, left blind spot radar 1153, right blind spot radar 1154, etc. of the autonomous driving vehicle 200.

[0051] The active safety module of the intelligent driving vehicle of the embodiment of the present disclosure uses the laser wave radar signal as input to sense the surrounding targets and their motion status. Laser radar is denser than millimeter wave radar and can well sense small targets. It also has a higher vertical resolution and can separate static obstacles from the background. Based on the solution proposed in the present disclosure, the following will be combined with Figure 3 It is described that the speed, acceleration, and category information of the target can be perceived by using the time-series lidar signal, and the speed signal predicted by the two-dimensional speed prediction map has high accuracy in both radial and lateral directions, which can reflect the actual motion state of the target and provide rich and high-quality upstream information for the active safety decision-making of the autonomous driving system.

[0052] Figure 3 A schematic diagram of a neural network for processing point clouds according to some embodiments of the present disclosure is shown, and the point cloud processing neural network may exist in the form of a product of a point cloud processing device. Figure 3 As shown, the point cloud processing neural network (point cloud processing device) includes: a feature extraction module 310, which is configured to extract features from the point cloud data of the current frame of the laser radar to obtain a point cloud feature map of the current frame; a time series fusion module 320, which is configured to perform time series fusion processing on the point cloud feature map of the current frame and the fused feature map of the previous frame, that is, to splice the point cloud feature map of the current frame and the fused feature map of the previous frame, and extract time series features to obtain a fused feature map of the current frame; a prediction module 330, which is configured to predict one or more of the speed, acceleration and category of the target according to the fused feature map of the current frame, and obtain a speed prediction map, an acceleration prediction map and a category prediction map.

[0053] The feature extraction module 310 includes, for example, a downsampling network, multiple residual networks, upsampling networks with different step sizes, and a splicing unit. The downsampling network downsamples the point cloud data of the current frame of the laser radar and outputs the first feature map of the current frame. The cascaded multiple residual networks downsample the first feature map and output 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 respectively and output multiple third feature maps of the same size. The splicing unit can splice multiple third feature maps and output the point cloud feature map of the current frame.

[0054] An example of feature extraction module 310 is listed below. Figure 3As shown in the figure, the point cloud information of the current frame (time T) is voxelized to form voxelized point cloud data as input data. The input data is recorded 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 step size of 2 to reduce the feature size and reduce the computational complexity of the subsequent network. The feature map output by the downsampling network is recorded as [H / 2, W / 2, C0], with the height and width reduced to 1 / 2 of the original, and C0 represents the channel. The structure of the downsampling network is, for example: a convolutional layer with a step size of N (N=2 in this example) (the convolutional layer performs downsampling), with a batch normalization layer (BatchNorm) 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. The convolution steps of these four residual networks are all 2, and they 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]. The height and width are reduced to 1 / 4, 1 / 8, 1 / 16, and 1 / 32 of the original. C1C2C3C4 represent channels, respectively. The residual network introduces cross-layer connections, and the input signal is directly added to the output of the residual network, making it easier for the network to transfer gradients during back propagation, thereby solving the gradient vanishing problem in deep network training. Subsequently, the four feature maps of the four residual networks will pass through the upsampling network with stride lengths of 1 / 2 / 4 / 8, and output four feature maps of the same size with the same downsampling rate of 4, all recorded as [H / 4, W / 4, C / 4], with the height and width being 1 / 4 of the original, and H, W, C representing the height, width, and channel, respectively. The structure of the upsampling network is, for example: a deconvolution layer with a stride length of N (N=2 in this example) (the deconvolution layer performs upsampling), with a batch normalization layer and a ReLU activation function layer. These four upsampled feature maps of the same size represent the feature information at different network depths, among which the shallow features mainly focus on the local details and low-level features of the image, which are usually closely related to the pixels of the image, including fine-grained information such as color, texture, edge, and corners. The deep features focus on global and high-level features, which are more abstract and complex feature representations, and these features are usually related to the semantic information of the image. Finally, the multiple feature maps obtained after upsampling are spliced ​​in the channel dimension through the splicing unit to obtain the point cloud feature map of the current frame, which is denoted as [H / 4, W / 4, C].

[0055] The temporal fusion module 320 includes, for example, a coordinate system alignment unit, a splicing unit, and a temporal fusion neural network, and may also include an upsampling network. The coordinate system alignment unit aligns the fusion feature map of the previous frame to the coordinate system of the current frame according to the coordinate transformation matrix between the previous frame and the current frame. The splicing unit splices the fusion feature map of the aligned previous frame with the point cloud feature map of the current frame, and splices them in the channel dimension. The temporal fusion neural network extracts temporal features from the spliced ​​feature map to obtain a fusion feature map of the current frame. The fusion feature map of the current frame includes the temporal information of each historical frame before the current frame, and the fusion feature map of the previous frame includes the temporal information of each historical frame before the previous frame. Among them, the fusion feature map of the initial frame is obtained by extracting the temporal features from the point cloud feature map of the initial frame using the temporal fusion neural network. On this basis, the fusion feature maps of each frame can be obtained through iteration. By introducing the temporal features, the target motion state can be better perceived. The upsampling network performs upsampling processing on the fusion feature map of the current frame to obtain a fusion feature map with the same size as the point cloud data of the current frame.

[0056] An example of the time series fusion module 320 is listed below. Figure 3 As shown, the fused feature map (size [H / 4, W / 4, C]) of the point cloud of the previous frame (T-1 moment) is warped and aligned to the coordinate system of the current frame (T moment) according to the coordinate transformation matrix between the two frames, and then spliced ​​with the point cloud feature map (size [H / 4, W / 4, C]) of the current frame output by the feature extraction module 310, and then input into the temporal fusion neural network, which compares the point cloud features of the two frames, mines the temporal features, and outputs the fused feature map (size [H / 4, W / 4, C]) of the current frame. The temporal fusion neural network includes a plurality of cascaded network blocks (such as 3), each of which includes a convolution layer (step size 1), a batch normalization layer, and an activation layer, such as a ReLU activation function layer. After the fused feature map (size [H / 4, W / 4, C]) of the current frame is upsampled four times (step size 4), a fused feature matrix (size [H, W, C]) of the same size as the original image is obtained.

[0057] The prediction module 330 includes, for example, a speed prediction neural network, an acceleration prediction neural network, and a category prediction neural network. The speed prediction neural network, the acceleration prediction neural network, and the category prediction neural network process the fused feature map of the current frame, respectively, and output a speed prediction map, an acceleration prediction map, and a category prediction map, respectively, whose sizes are the same as the original image (original point cloud voxel grid). The dimension of the speed prediction map [H, W, 2] is 2, representing the radial component and the lateral component of the target's speed. In the vehicle driving plane, the radial direction refers to the vehicle driving direction, that is, the direction of the front of the vehicle, and the lateral direction is perpendicular to the vehicle driving direction, that is, the vehicle body width direction. The dimension of the acceleration prediction map [H, W, 1] is 1, which represents the rate of change of the target's speed. The dimension of the category prediction map [H, W, 1] is 1, which represents the one-dimensional index of the target's category. The speed / acceleration / category prediction neural network includes multiple cascaded network blocks (such as 2), each network block includes a convolution layer (with a step size of 1), a batch normalization layer and an activation layer, such as a ReLU activation function layer, and the last network block cascades a convolution layer (with a step size of 1).

[0058] Point cloud processing neural networks are usually trained and then used for prediction. During training, the speed prediction map and the acceleration prediction map can use the L1 loss function and use the true value for supervision, and the category prediction map can use the Focal Loss 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 respectively. Under this coefficient configuration, the speed, acceleration, and category information can be learned at the same time, which is conducive to the convergence of the point cloud processing neural network. Based on the point cloud processing neural network, the lidar point cloud can be processed to obtain information such as the speed, acceleration, and category of the targets around the intelligent driving vehicle. Among them, the categories of the targets include, but are not limited to, vehicles, pedestrians, etc.

[0059] In addition, by measuring the time difference between the emission and reception of the laser pulse, as well as the emission angle and pitch angle of the laser beam, the lidar can calculate the position coordinates of the target. The position coordinates can be used to describe the specific position of the target in space.

[0060] Based on the upstream perception module, lidar and point cloud processing neural network (point cloud processing device), it is possible to simultaneously perceive the motion state information such as the position, speed, acceleration and category of the targets around the intelligent driving vehicle.

[0061] The input information of the driving control decision algorithm of the intelligent driving vehicle of this embodiment includes: (1) the motion state information of the target around the intelligent driving vehicle perceived by the upstream, for example, the position (x, y), speed (vx, vy), acceleration (ax, ay), category (optional, set to objType), etc. of the target in the coordinate system of the intelligent driving vehicle. The coordinate system of the intelligent driving vehicle, for example, takes the center of the vehicle as the origin, the front direction of the vehicle as the radial direction (x direction), and the width direction of the vehicle body as the lateral direction (y direction). It is assumed that: the front direction of the vehicle is the positive radial direction, the rear direction 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) The motion state information of the intelligent driving vehicle (also called the main vehicle) and the configuration information of the intelligent driving vehicle itself. The motion state information of the intelligent driving vehicle, such as the speed (set to ego_speed), can be directly obtained 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 directly obtained from the vehicle configuration file. The configuration information of the intelligent driving vehicle itself includes, for example: the distance from the center point of the vehicle to the front / rear / left / right boundaries of the vehicle boundary box, and the braking attributes. The distance from the center point of the vehicle to the front boundary box of the vehicle is set to front_edge_to_center, the distance from the center point of the vehicle to the rear boundary box of the vehicle is set to back_edge_to_center, the distance from the center point of the vehicle to the left boundary box of the vehicle is set to left_edge_to_center, and the distance from the center point of the vehicle to the right boundary box of the vehicle is set to right_edge_to_center. Among them, the braking attributes include a delay attribute, set to time_delay, which represents the system transmission delay time from the vehicle issuing a deceleration command to the vehicle actually starting to execute deceleration. The braking attributes also include the maximum braking deceleration, set to ego_max_dec, which represents the maximum braking deceleration that can be achieved after the vehicle issues a deceleration command.

[0062] Driving control can be performed based on the various input information of the driving control decision algorithm of the intelligent driving vehicle obtained above.

[0063] Figure 4 Schematic diagram showing a driving control method according to some embodiments of the present disclosure. Figure 4 As shown, the driving control method of this embodiment includes the following steps. The driving control method can be executed by a driving control device of an intelligent driving vehicle (referred to as "vehicle"), for example.

[0064] In step 410, according to the aforementioned method, the position, velocity and acceleration of the target in the intelligent driving vehicle coordinate system and the velocity and braking properties of the intelligent driving vehicle are obtained.

[0065] In step 420 , it is determined whether there is a risk of conflict between the target and the intelligent driving vehicle based on the position, speed, and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle.

[0066] Therefore, based on the rich information such as the perceived speed and acceleration of the target, it is possible to more accurately determine whether there is a risk of conflict between the target and the intelligent driving vehicle.

[0067] In some embodiments, in step 420, step 420c and step 420d may be executed to determine whether there is a risk of conflict between the target and the intelligent driving vehicle, or at least one of steps 420a and 420b may be executed first, and after preliminarily eliminating the risk of conflict, step 420c and step 420d may be executed to verify whether there is a risk of conflict between the target and the intelligent driving vehicle.

[0068] Step 420a, preliminarily judge the risk of conflict. Determine whether there is a potential risk of conflict between the target and the intelligent driving vehicle based on at least one of the position of the target in the intelligent driving vehicle coordinate system, the speed of the target, and the speed of the intelligent driving vehicle. Based on a simple judgment logic, the potential risk of conflict is quickly judged, and the situation where there is no clear risk of conflict is excluded. If the risk of conflict cannot be excluded, the risk of conflict is further verified through subsequent judgment. In the case of determining that there is a potential risk of conflict, determine whether there is a risk of conflict between the target and the intelligent driving vehicle based on the position, speed and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle.

[0069] Step 420b, determine whether the target is a candidate target for triggering emergency braking. Determine the relative speed of the target relative to the intelligent driving vehicle, and determine whether the target is a candidate target for triggering emergency braking based on whether the ray of the target in the relative speed direction passes through the safe driving range of the intelligent driving vehicle. Figure 5 Description. If it is not a candidate target for triggering emergency braking, no further judgment is required; if it is a candidate target for triggering emergency braking, the conflict risk can be further verified through subsequent judgment. In the case where the target is a candidate target for triggering emergency braking, determine whether there is a conflict risk between the target and the intelligent driving vehicle based on the position, speed and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle.

[0070] Step 420c, determine whether there is a risk of conflict in the radial direction, and then combine Figure 6-Figure 8 Description in order to verify the radial conflict risk situation.

[0071] Step 420d, determining whether there is a risk of conflict in the lateral direction, so as to verify the lateral conflict risk situation.

[0072] 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.

[0073] 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.

[0074] The method for initially determining the risk of conflict in step 420a is described below.

[0075] 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.

[0076] 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 lag distance is gradually increased. 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.

[0077] 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.

[0078] For example, if y>right_edge_to_center and vy>0, it means that the target is on the left side of the smart driving vehicle and the lateral component of the target's velocity is to the left. At this time, the target will gradually move away from the vehicle in the lateral direction to the left. It can be determined that there is no potential conflict risk between the target and the smart driving vehicle, and there is no need to trigger emergency braking. If y<-front_edge_to_center and vy<0, it means that the target is on the right side of the smart driving vehicle and the lateral component of the target's velocity is to the right. At this time, the target will gradually move away from the vehicle in the lateral direction to the right. It can be determined that there is no potential conflict risk between the target and the smart driving vehicle, and there is no need to trigger emergency braking.

[0079] In some embodiments, whether the target is located behind the center point of the intelligent driving vehicle is determined based on the radial component of the target's position in the intelligent driving vehicle coordinate system; when 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.

[0080] For example, if x<0, it means that the target is behind the center point of the intelligent driving vehicle. At this time, the target is behind the vehicle, and it can be determined that there is no potential conflict risk between the target and the intelligent driving vehicle, and there is no need to trigger emergency braking. The active safety module generally only processes targets that are flush with or in front of the vehicle, and does not need to process targets behind it, otherwise it will cause more false triggering of emergency braking, affecting vehicle traffic efficiency and driving experience.

[0081] Therefore, based on the position of the target, it is also possible to combine the speed of the target and the speed of the intelligent driving vehicle and use simple judgment logic to quickly determine the potential conflict risk, exclude situations where there is clearly no conflict risk, and further verify the conflict risk through subsequent judgments when the conflict risk cannot be eliminated.

[0082] The method of determining whether the target is a candidate target for triggering emergency braking in step 420b is described below.

[0083] like Figure 5 As shown, the relative speed of the target relative to the smart driving vehicle is determined, and a ray is drawn with the target position as the starting point and the relative speed direction as the direction. If the ray of the target in the relative speed direction passes through the safe driving range of the smart driving vehicle, it means that the target is approaching the vehicle and there is no risk of conflict. The target is determined to be a candidate target for triggering emergency braking. If the ray of the target in the relative speed direction does not pass through the safe driving range of the smart driving vehicle, it is determined that the target is not a candidate target for triggering emergency braking. Among them, the safe driving range of the smart driving vehicle can be the boundary box of the smart driving vehicle and a certain safety buffer distance is reserved, which is represented by an expansion box. If it is not a candidate target for triggering emergency braking, no further judgment is required; if it is a candidate target for triggering emergency braking, the conflict risk can be further verified through subsequent judgment.

[0084] The method of determining whether there is a risk of collision in the radial direction in step 420c is described below.

[0085] In some embodiments, the braking distance of the target and the braking distance of the smart driving vehicle are determined based on the radial component of the speed and the radial component of the acceleration of the target and the speed and braking properties of the smart driving vehicle; by comparing the difference between the braking distance of the smart driving vehicle and the braking distance of the target with the radial distance of the target from the smart driving vehicle, it is determined whether there is a risk of conflict between the target and the smart driving vehicle in the radial direction, so as to verify the radial conflict risk situation. When the difference between the braking distance of the smart driving vehicle and the braking distance of the target is greater than the difference between the radial distance of the target from the smart driving vehicle and the safety buffer distance, it is determined that there is a risk of conflict between the target and the smart driving vehicle in the radial direction. Among them, the radial distance of the target from the smart driving vehicle is determined according to the radial component of the position of the target in the coordinate system of the smart driving vehicle.

[0086] Due to the system transmission delay time, the intelligent driving vehicle first drives at the current speed for the system transmission delay time (time_delay), and then performs emergency braking at the maximum braking deceleration (ego_max_dec) d slope. The braking distance of the intelligent driving vehicle is the area enclosed by the solid line and the x-axis from t=0 to t=t_ego. The target drives at a certain acceleration, and the target's braking distance is the area enclosed by the dotted line and the x-axis from t=0 to t=t_target, with the area above the x-axis being positive and the area below the x-axis being negative. The following three cases determine the braking distance of the target and the braking distance of the intelligent driving vehicle.

[0087] The first case: Figure 6 As shown in the figure, the initial speed of the target is in the same direction as the smart driving vehicle (vx>0), and the speeds of the two will not be the same at a certain moment during the process of the smart driving vehicle slowing down to 0. In this case, t_ego is the moment when the smart driving vehicle slows down to 0, t_target is the moment when the target slows down to 0, and the slope (acceleration) of the target in the speed v-time t relationship graph is the acceleration of the target.

[0088] When the radial components of the velocities of the target and the intelligent driving vehicle are in the same direction, if the speeds of the intelligent driving vehicle and the target do not become the same at a certain moment during the process of the intelligent driving vehicle decelerating at the maximum braking deceleration, the distance traveled by the intelligent driving vehicle during the process of decelerating from the current speed to zero at the maximum braking deceleration shall be determined as the braking distance of the intelligent driving vehicle, and the distance traveled by the target during the process of decelerating from the current radial component of the velocity to zero at the radial component of the acceleration shall be determined as the braking distance of the target.

[0089] The second case: Figure 7 As shown in the figure, the initial speed of the target is in the same direction as the intelligent driving vehicle (vx>0), and the speeds of the two vehicles will be the same at a certain moment when the intelligent driving vehicle slows down to 0. In this case, t_ego and t_target are both the moment when the speeds of the two vehicles are the same, and the slope (acceleration) of the target in the speed v-time t relationship graph is the acceleration of the target.

[0090] When the radial components of the velocities of the target and the intelligent driving vehicle are in the same direction, if the speeds of the intelligent driving vehicle and the target become the same at a certain moment during the process of the intelligent driving vehicle decelerating at the maximum braking deceleration, the distance traveled by the intelligent driving vehicle during the process of decelerating from the current speed at the maximum braking deceleration to the moment when the speeds become the same shall be determined as the braking distance of the intelligent driving vehicle, and the distance traveled by the target during the process of traveling from the current radial component of the speed according to the radial component of the acceleration to the moment when the speed becomes the same shall be determined as the braking distance of the target.

[0091] The third situation: Figure 8 As shown, the initial speed of the target is in the opposite direction of the smart driving vehicle (vx<0). In this case, t_ego is the moment when the smart driving vehicle decelerates to 0, t_target is the moment when the target decelerates to 0, and the slope (acceleration) of the target in the speed v-time t relationship diagram can be a preset maximum deceleration target_max_dec set in advance, rather than the perceived deceleration of the target in the actual motion state. This is because the target's opposite driving situation is relatively special, and it is necessary to assume that the other party will immediately brake at the maximum deceleration. If there is danger under this assumption, the smart driving vehicle needs to take emergency braking.

[0092] When the radial components of the speed of the target and the intelligent driving vehicle are in opposite directions, the distance traveled by the intelligent 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 intelligent driving vehicle, and the distance traveled by the target during the process of decelerating from the current radial component of the speed to zero according to the preset maximum deceleration is determined as the braking distance of the target.

[0093] After determining the braking distance of the intelligent driving vehicle and the braking distance of the target, when 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 of the target from the intelligent driving vehicle and the safety buffer distance, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle in the radial direction. Among them, the radial distance of the target from the intelligent driving vehicle is determined according to the radial component of the position of the target in the coordinate system of the intelligent driving vehicle. That is, if "the braking distance of the intelligent driving vehicle - the braking distance of the target > the radial distance (x) of the target from the intelligent driving vehicle - the safety buffer distance", it is determined that there is a risk of conflict in the radial direction, and it is necessary to further determine whether there is a risk of conflict in the lateral direction. Otherwise, it is determined that there is no risk of conflict in the radial direction and emergency braking does not need to be triggered.

[0094] The method for determining whether there is a risk of conflict in the lateral direction in step 420d is described below.

[0095] According to the position, speed and lateral component of acceleration of the target in the coordinate system of the intelligent driving vehicle and the speed of the intelligent driving vehicle, it is determined whether the target will move into the width range of the intelligent driving vehicle in the lateral direction; according to whether the target will move into the width range of the intelligent driving vehicle in the lateral direction, it is determined whether there is a risk of conflict between the target and the intelligent driving vehicle in the lateral direction, so as to verify the situation of lateral conflict risk. If the target will move into the width range of the intelligent driving vehicle in the lateral direction, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle in the lateral direction.

[0096] In some embodiments, the method for determining whether the target will move into the width range of the intelligent driving vehicle in the lateral direction includes: according to the radial distance of the target from the intelligent driving vehicle and the difference between the speed ego_speed of the intelligent driving vehicle and the radial component vx of the speed of the target, the time of conflict between the target and the intelligent driving vehicle in the radial direction is determined. The radial distance of the target from the intelligent driving vehicle is determined according to the radial component x of the position of the target in the coordinate system of the intelligent driving vehicle. That is, the time t of conflict in the radial direction is calculated according to t = x / (ego_speed - vx); according to the current lateral component y, lateral component of speed vy and lateral component of acceleration ay of the target in the coordinate system of the intelligent driving vehicle and the time t of conflict, the lateral component of the position of the target at the time of conflict is determined. That is, according to y’ = y + vy×t + ay×t×t / 2, the lateral component of the position of the target at the time of conflict y’ is determined; according to the lateral component of the position of the target at the time of conflict, it is determined whether the target will move into the width range of the intelligent driving vehicle in the lateral direction. That is, 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.

[0097] Combine the following Fig. 9 An example of determining whether there is a risk of conflict between a target and an intelligent driving vehicle and corresponding driving control is described. 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.

[0098] Fig. 9 Schematic diagram showing a driving control method according to some embodiments of the present disclosure. Fig. 9 As shown, the driving control method of this embodiment includes the following steps.

[0099] 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 risk of conflict 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 the intelligent driving vehicle or in front of the intelligent driving vehicle, and does not need to process targets behind the intelligent driving vehicle, otherwise it will cause more false triggering of emergency braking, affecting the traffic efficiency and driving experience of the intelligent driving vehicle.

[0100] In step 920, if the target is located behind the front boundary of the smart driving vehicle and the radial speed is less than the speed of the smart driving vehicle, it means that the target is located behind the smart driving vehicle and is gradually lagging behind the smart driving vehicle, and the distance behind the smart driving vehicle is gradually increasing. It is determined that there is no conflict risk and there is no need to trigger emergency braking of the smart driving vehicle.

[0101] In step 930, if the target is located in front of the smart driving vehicle and the radial speed is greater than the speed of the smart driving vehicle, it means that the target will gradually move away from the smart driving vehicle in the radial direction, and it is determined that there is no conflict risk, and there is no need to trigger emergency braking of the smart driving vehicle.

[0102] In step 940, if the target is located on the left side of the smart driving vehicle and the lateral speed is to the left, it means that the target will gradually move away from the smart driving vehicle in the lateral direction to the left, and it is determined that there is no conflict risk, and there is no need to trigger emergency braking of the smart driving vehicle.

[0103] In step 950, if the target is located to the right of the smart driving vehicle and the lateral speed is to the right, it means that the target will gradually move away from the smart driving vehicle in the lateral right direction, and it is determined that there is no conflict risk, and there is no need to trigger emergency braking of the smart driving vehicle.

[0104] In step 960, calculate the relative speed of the target with respect to the intelligent driving vehicle. Starting from the target position, draw a ray in the direction of the relative speed. 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 risk of conflict, and it is necessary to enter the subsequent determination process for further verification, that is, continue to execute step 970; otherwise, there is no obvious collision 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 bounding box of the intelligent driving vehicle is used as the extended box.

[0105] In step 970, determine whether there is a risk of conflict in the radial direction (x direction) and whether emergency braking needs to be taken. Referring to the description of step 420 c above, 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 risk of conflict in the radial direction, and continue with the subsequent determination; otherwise, it is determined that there is no risk of conflict in the radial direction and there is no need to trigger emergency braking.

[0106] In step 980, determine whether there is a risk of conflict in the lateral direction (y direction) and whether emergency braking needs to be taken. Referring to the description of step 420 d above, 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 risk of conflict. If there is a risk of conflict in both the x direction and the y direction, it is determined that emergency braking needs to be executed.

[0107] Thus, based on rich information such as the speed and acceleration of the perceived target, 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 needed.

[0108] Fig.10 The structural schematic diagram of the driving control device showing some embodiments of the present disclosure is shown. The intelligent driving vehicle includes a driving control device, and the driving control device can execute the driving control method in each embodiment.

[0109] As Fig.10 shown, 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 the instructions stored in the memory 1010.

[0110] The driving control device 1000 may further include an input / output interface 1030 , a network interface 1040 , a storage interface 1050 , etc. These interfaces 1030 , 1040 , 1050 , the memory 1010 , and the processor 1020 may be connected via a bus 1060 , for example.

[0111] The memory 1010 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, and other programs.

[0112] The processor 1020 may be implemented by 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 and other discrete hardware components.

[0113] Among them, the input and output interface 1030 provides a connection interface for input and output devices such as a display, a mouse, a keyboard, and a touch screen. 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 structure among a variety of bus structures. For example, the bus structure includes but is not limited to the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MCA) bus, and the Peripheral Component Interconnect (PCI) bus.

[0114] It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product implemented on one or more (non-transient) 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 implements its solution mainly through a computer program.

[0115] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A driving control method, characterized in that: include: Obtaining the position, velocity and acceleration of the target in the intelligent driving vehicle coordinate system and the velocity and braking properties of the intelligent driving vehicle; Determining whether there is a risk of conflict between the target and the intelligent driving vehicle according to the position, speed and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle, including: determining the braking distance of the target and the braking distance of the intelligent driving vehicle according to the radial component of the speed and the radial component of the acceleration of the target and the speed and braking properties of the intelligent driving vehicle; 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 of the target from the intelligent driving vehicle, wherein the radial distance of the target from the intelligent driving vehicle is determined according to the radial component of the position of the target in the intelligent driving vehicle coordinate system; and 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 position, the lateral component of the speed and the acceleration of the target in the intelligent driving vehicle coordinate system and the speed of the intelligent driving vehicle; determining whether there is a risk of conflict between the target and the intelligent driving vehicle in the lateral direction according to whether the target will move within the width range of the intelligent driving vehicle in the lateral direction; In the event that a risk of conflict is determined, emergency braking is determined to be necessary.

2. The driving control method according to claim 1, wherein: Determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: Determine the relative speed of the target with respect to the intelligent driving vehicle, and determine whether the target is a candidate target for triggering emergency braking based on whether the ray of the target in the relative speed direction passes through the safe driving range of the intelligent driving vehicle; if the target is a candidate target for triggering emergency braking, determine whether there is a risk of conflict between the target and the intelligent driving vehicle based on the position, speed and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle.

3. The driving control method according to claim 1, wherein: Determining the braking distance of the target and the braking distance of the intelligent driving vehicle includes: When the radial components of the velocities of the target and the intelligent driving vehicle are in the same direction, if the speeds of the intelligent driving vehicle and the target do not become the same at a certain moment during the process of the intelligent driving vehicle decelerating at the maximum braking deceleration, the distance traveled by the intelligent driving vehicle during the process of decelerating from the current speed to zero at the maximum braking deceleration shall be determined as the braking distance of the intelligent driving vehicle, and the distance traveled by the target during the process of decelerating from the current radial component of the velocity to zero at the radial component of the acceleration shall be determined as the braking distance of the target.

4. The driving control method according to claim 1, wherein: Determining the braking distance of the target and the braking distance of the intelligent driving vehicle includes: When the radial components of the velocities of the target and the intelligent driving vehicle are in the same direction, if the speeds of the intelligent driving vehicle and the target become the same at a certain moment during the process of the intelligent driving vehicle decelerating at the maximum braking deceleration, the distance traveled by the intelligent driving vehicle during the process of decelerating from the current speed at the maximum braking deceleration to the moment when the speeds become the same shall be determined as the braking distance of the intelligent driving vehicle, and the distance traveled by the target during the process of traveling from the current radial component of the speed according to the radial component of the acceleration to the moment when the speed becomes the same shall be determined as the braking distance of the target.

5. The driving control method according to claim 1, wherein: Determining the braking distance of the target and the braking distance of the intelligent driving vehicle includes: When the radial components of the speed of the target and the intelligent driving vehicle are in opposite directions, the distance traveled by the intelligent 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 intelligent driving vehicle, and the distance traveled by the target during the process of decelerating from the current radial component of the speed to zero according to the preset maximum deceleration is determined as the braking distance of the target.

6. The driving control method according to claim 1, wherein: Determining whether there is a risk of collision between the target and the intelligent driving vehicle in the radial direction includes: When 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 of the target from the intelligent driving vehicle and the safety buffer distance, it is determined that there is a risk of radial conflict between the target and the intelligent driving vehicle, wherein the radial distance of the target from the intelligent driving vehicle is determined according to the radial component of the position of the target in the coordinate system of the intelligent driving vehicle.

7. The driving control method according to claim 1, wherein: Determining whether the target will move in the lateral direction within the width of the intelligent driving vehicle includes: Determine the time when the target and the intelligent driving vehicle collide 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 and the radial component of the speed of the target, wherein the radial distance of the target from the intelligent driving vehicle is determined according to the radial component of the position of the target in the coordinate system of the intelligent driving vehicle; Determine the lateral component of the target's position at the time of the conflict according to the target's current lateral component of position, lateral component of velocity and lateral component of acceleration in the intelligent driving vehicle coordinate system, and the time when the conflict occurs; According to the lateral component of the position of the target at the time of the collision, it is determined whether the target will move into the width range of the intelligent driving vehicle in the lateral direction.

8. The driving control method according to any one of claims 1 to 7, wherein: Determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: Determining whether there is a potential conflict risk between the target and the intelligent driving vehicle according to at least one of a position of the target in the intelligent driving vehicle coordinate system, a speed of the target, and a speed of the intelligent driving vehicle; When it is determined that there is a potential risk of conflict, it is determined whether there is a risk of conflict between the target and the intelligent driving vehicle based on the position, speed and acceleration of the target in the intelligent driving vehicle coordinate system and the speed and braking properties of the intelligent driving vehicle.

9. The driving control method according to claim 8, wherein: Determining whether there is a potential conflict risk between the target and the intelligent driving vehicle includes: Determine whether the target is located behind or in front of the intelligent driving vehicle according to the radial component of the position of the target in the intelligent driving vehicle coordinate system; comparing the radial component of the velocity of the target and the velocity of the intelligent driving vehicle; When the target is located behind the intelligent driving vehicle and the radial component of the target's speed is less than the speed of the intelligent driving vehicle, it is determined that there is no potential conflict risk between the target and the intelligent driving vehicle; When the target is located 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 conflict risk between the target and the intelligent driving vehicle.

10. The driving control method according to claim 8, wherein: Determining whether there is a potential conflict risk between the target and the intelligent driving vehicle includes: Determine whether the target is located to the left or right of the intelligent driving vehicle according to the lateral component of the position of the target in the intelligent driving vehicle coordinate system; When 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, it is determined that there is no potential conflict risk between the target and the intelligent driving vehicle; When 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, it is determined that there is no potential conflict risk between the target and the intelligent driving vehicle.

11. The driving control method according to claim 8, wherein: Determining whether there is a potential conflict risk between the target and the intelligent driving vehicle includes: Determine whether the target is located behind the center point of the intelligent driving vehicle according to the radial component of the position of the target in the intelligent driving vehicle coordinate system; When the target is located behind the center point of the intelligent driving vehicle, it is determined that there is no potential conflict risk between the target and the intelligent driving vehicle.

12. A driving control device, comprising: Memory; And a processor coupled to the memory, characterized in that the processor is configured to execute the driving control method according to any one of claims 1-11 based on instructions stored in the memory.

13. An intelligent driving vehicle, characterized in that: include: A driving control device, configured to execute the driving control method according to any one of claims 1 to 11.

14. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the steps of the driving control method described in any one of claims 1 to 11 are implemented.

15. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the driving control method described in any one of claims 1 to 11 are implemented.

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