Driving control method, driving control device and intelligent driving vehicle

Abstract

The invention provides a driving control method, a driving control device and an intelligent driving vehicle, and relates to the field of computers, in particular to the field of intelligent driving. The driving control method comprises the steps that in response to an emergency braking decision, track points of the intelligent driving vehicle and a target triggering the emergency braking decision in a future specified time period are determined; according to the trajectory points of the intelligent driving vehicle and the target triggering the emergency braking decision in the specified time period in the future, whether the target and the intelligent driving vehicle have a conflict risk or not is determined; and if the conflict risk is determined, the emergency braking decision is kept, and if the conflict risk is not determined, the emergency braking decision is canceled. After the emergency braking decision, a trajectory-based emergency braking decision filtering method is additionally introduced to maintain or cancel the emergency braking decision, so that decision conflicts between the AEB system and the PNC system are avoided, the false triggering behavior of emergency braking of the AEB system is reduced, and the operation efficiency of the intelligent driving vehicle is improved.

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. Its autonomous emergency braking system (AEB) can perceive and make decisions about the driving environment. When a dangerous situation occurs, it will issue an emergency braking command to the vehicle chassis to ensure vehicle safety. The autonomous driving system also includes a planning and control system (Planning and Control, PNC), which is responsible for planning the vehicle's driving path and controlling the vehicle's movements.

[0004] There may be a decision conflict between the AEB system and the PNC system. For example, the AEB system makes a judgment based on the current environmental information and believes that there is a risk of conflict between the vehicle and the target, and emergency braking needs to be performed; but the PNC system believes that there is no risk of conflict and that obstacle avoidance can maintain normal driving. In this case, the emergency braking performed by the AEB system is a false trigger, and the emergency braking does not need to be triggered, which greatly affects the operating efficiency of the intelligent driving vehicle. Summary of the invention

[0005] Regarding the decision conflict between the AEB system and the PNC system, the responses made by the AEB system and the PNC system based on the information obtained by each system are correct and difficult to optimize. In the embodiment of the present disclosure, after the emergency braking decision is made, an additional trajectory-based emergency braking decision filtering method is introduced to maintain or cancel the emergency braking decision, avoid the decision conflict between the AEB system and the PNC system, reduce the false triggering behavior of the emergency braking of the AEB system, and improve the operating efficiency of the intelligent driving vehicle.

[0006] Some embodiments of the present disclosure provide a driving control method, including: in response to an emergency braking decision, determining trajectory points of an intelligent driving vehicle and a target that triggers the emergency braking decision within a specified future time period; determining whether there is a risk of conflict between the target and the intelligent driving vehicle based on the trajectory points of the intelligent driving vehicle and the target that triggers the emergency braking decision within the specified future time period; if it is determined that there is a risk of conflict, maintaining the emergency braking decision, and if it is determined that there is no risk of conflict, canceling the emergency braking decision.

[0007] In some embodiments, based on the trajectory points of the smart driving vehicle and the target that triggers the emergency braking decision within a specified future time period, determining whether there is a risk of conflict between the target and the smart driving vehicle includes: if the trajectory point of the target that triggers the emergency braking decision at any moment within the specified future time period is within the vehicle frame range of the trajectory point of the smart driving vehicle at that moment, determining that the target has a risk of conflict with the smart driving vehicle.

[0008] In some embodiments, based on the trajectory points of the smart driving vehicle and the target that triggers the emergency braking decision within a specified future time period, determining whether there is a risk of conflict between the target and the smart driving vehicle includes: if a line connecting the trajectory points of the target that triggers the emergency braking decision within a specified future time period intersects with a line connecting the trajectory points of the smart driving vehicle within a specified future time period, determining that there is a risk of conflict between the target and the smart driving vehicle.

[0009] In some embodiments, based on the trajectory points of the smart driving vehicle and the target that triggers the emergency braking decision in a specified future time period, determining whether there is a risk of conflict between the target and the smart driving vehicle includes: judging whether the trajectory point of the target that triggers the emergency braking decision at any moment in the specified future time period is within the vehicle frame range of the trajectory point of the smart driving vehicle at that moment; if the judgment result is yes, determining that there is a risk of conflict between the target and the smart driving vehicle; if the judgment result is no, judging whether the line connecting the trajectory points of the target that triggers the emergency braking decision in the specified future time period intersects with the line connecting the trajectory points of the smart driving vehicle in the specified future time period; if the judgment result is intersect, determining that there is a risk of conflict between the target and the smart driving vehicle.

[0010] In some embodiments, determining whether there is a risk of conflict between the target and the smart driving vehicle based on the trajectory points of the smart driving vehicle and the target that triggers the emergency braking decision within a specified future time period includes: when it is determined that there is no risk of conflict based on the trajectory points, making an emergency braking judgment again based on the motion state information of the last trajectory point of the smart driving vehicle and the target that triggers the emergency braking decision within the specified future time period; if the result of the second judgment is still that emergency braking is required, it is determined that there is a risk of conflict between the target and the smart driving vehicle; if the result of the second judgment is that emergency braking is not required, it is determined that there is no risk of conflict between the target and the smart driving vehicle.

[0011] In some embodiments, the determination of whether to perform emergency braking again includes: 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 at the last trajectory point and the speed and braking properties of the intelligent driving vehicle; if it is determined that there is a risk of conflict, determining that emergency braking needs to be performed.

[0012] In some embodiments, determining whether there is a risk of conflict between the target and the smart driving vehicle includes: determining the relative speed of the target with respect to the smart driving vehicle at the last trajectory point, and determining whether the target is a candidate target for triggering emergency braking based on whether the ray of the target in the direction of the relative speed passes through the safe driving range of the smart 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 smart driving vehicle based on the position, speed and acceleration of the target in the smart driving vehicle coordinate system at the last trajectory point and the speed and braking properties of the smart driving vehicle.

[0013] 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 component of the target's velocity and the radial component of the acceleration at the last trajectory point 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 based on the radial component of the position of the target in the coordinate system of the intelligent driving vehicle.

[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 within the width of the intelligent driving vehicle in the lateral direction based on the position, velocity and lateral component of acceleration of the target in the intelligent driving vehicle coordinate system at the last trajectory point 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 based on whether the target will move within the width of the intelligent driving vehicle in the lateral direction.

[0015] 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.

[0016] Some embodiments of the present disclosure provide an intelligent driving vehicle, comprising: a driving control device configured to execute a driving control method.

[0017] 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.

[0018] 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

[0019] 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.

[0020] 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.

[0021] Figure 1 A schematic diagram showing the electrical architecture of an intelligent (autonomous) driving vehicle according to some embodiments of the present disclosure.

[0022] Figure 2 A schematic diagram showing the appearance and structure of an intelligent (automatic) driving vehicle according to some embodiments of the present disclosure.

[0023] Figure 3 A schematic diagram showing a neural network for processing point clouds according to some embodiments of the present disclosure.

[0024] Figure 4 A schematic diagram showing a decision conflict between an AEB system and a PNC system according to some embodiments of the present disclosure.

[0025] Figure 5 A flowchart diagram of a driving control method according to some embodiments of the present disclosure is shown.

[0026] Figure 6 A schematic diagram showing a method for determining collision risk based on trajectory points according to some embodiments of the present disclosure.

[0027] Figure 7 A schematic diagram showing a trajectory-based conflict risk determination method according to some embodiments of the present disclosure.

[0028] Figure 8 A schematic diagram illustrating a conflict risk determination scenario based on emergency braking re-judgment according to some embodiments of the present disclosure.

[0029] Fig. 9 A flowchart diagram of a driving control method according to some embodiments of the present disclosure is shown.

[0030] Fig.10 A flowchart illustrating an emergency braking decision-making method according to some embodiments of the present disclosure is shown.

[0031] Fig.11 A schematic diagram showing some embodiments of the present disclosure for determining whether a target is a candidate target for triggering emergency braking.

[0032] Fig.12 , Fig.13 and Fig.14A 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.

[0033] Fig.15 A schematic diagram showing the structure of a driving control device according to some embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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 as part of the specification.

[0043] 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.

[0044] 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.

[0045] Figure 1 A schematic diagram showing the electrical architecture of an intelligent (autonomous) driving vehicle according to some embodiments of the present disclosure.

[0046] like Figure 1 As 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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 2 As 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.

[0052] 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.

[0053] 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 3As 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.

[0054] 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.

[0055] 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, and 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].

[0056] 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.

[0057] 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.

[0058] 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).

[0059] 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.

[0060] 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.

[0061] 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.

[0062] As mentioned above, in an autonomous driving system, there may be a decision conflict between the AEB system and the PNC system. Figure 4As shown, the obstacle appears right in front of the smart driving vehicle, and the smart driving vehicle is moving forward at a relatively high speed. If judged according to the current instantaneous motion state, it is correct for the AEB system to perform emergency braking, because the smart driving vehicle is moving at a relatively high speed and is close to the obstacle, so there is no time to brake and emergency braking is required. However, from the perspective of the PNC system, the PNC system will plan the smart driving vehicle to bypass the obstacle to the left at a certain moment in the future, so the PNC system determines that braking is not necessary. In the above scenario, the responses made by the AEB system and the PNC system based on the information they each obtained are correct, but this will cause the AEB system to falsely trigger emergency braking in the scenario of "can bypass but no time to brake".

[0063] The disclosed embodiment additionally introduces a trajectory-based emergency braking decision filtering method (called "trajectory filtering decision algorithm") after the emergency braking decision is made to supplement the emergency braking decision algorithm of the AEB system. If the emergency braking decision algorithm determines that emergency braking does not need to be performed, the trajectory filtering decision algorithm does not need to be started. If the emergency braking decision algorithm determines that emergency braking needs to be performed, the trajectory filtering decision algorithm is started to make a judgment again to maintain or cancel the emergency braking decision. The emergency braking decision triggered by the AEB system can only be maintained if the judgment of the trajectory filtering decision algorithm is passed.

[0064] The input information of the "trajectory filtering decision algorithm" of this embodiment includes: (1) the motion state information of the target that triggers emergency braking in the upstream AEB system, for example, the position (x, y), speed (vx, vy), acceleration (ax, ay), category (optional, set as objType), etc. of the target in the intelligent driving vehicle coordinate system. According to the motion state information of the target, the trajectory points of the target in the future specified time period can be calculated. Among them, the intelligent driving vehicle coordinate system, 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 radial positive direction, the rear direction of the vehicle is the radial negative direction, and facing the radial positive direction, the left side is the lateral positive direction, and the right side is the lateral negative direction. (2) The trajectory points of the intelligent driving vehicle (also called the main vehicle) in the future specified time period calculated by the chassis control module. Among them, the motion state information of the intelligent driving vehicle, such as speed (set as ego_speed), is directly obtained from the vehicle chassis in real time without the need for prediction by the perception module. According to the motion state information of the intelligent driving vehicle, the chassis control module can calculate the trajectory points of the intelligent driving vehicle in the future specified time period. For example, the intelligent driving vehicle has 8 future trajectory points every 0.15 seconds in the next 1.2 seconds. Each trajectory point contains the following information: the position (ego_x, ego_y) and speed information (ego_speed, which can be decomposed into ego_vx, ego_vy) of the main vehicle at that moment.

[0065] Based on the various input information of the "trajectory filtering decision algorithm" obtained above, the "trajectory filtering decision algorithm" can be executed to perform driving control.

[0066] Figure 5 A schematic diagram showing a flow chart of a driving control method according to some embodiments of the present disclosure is shown. Figure 5 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.

[0067] In step 510 , in response to an upstream emergency braking decision, trajectory points of the intelligent driving vehicle and a target triggering the emergency braking decision within a specified future time period are determined.

[0068] As mentioned above, based on the target's motion state information, the target's trajectory points in the future specified time period can be calculated; based on the intelligent driving vehicle's motion state information, the intelligent driving vehicle's trajectory points in the future specified time period can be calculated. Thus, the trajectory points of the intelligent driving vehicle and the target that triggers the emergency braking decision in the future specified time period are obtained.

[0069] In step 520 , based on the trajectory points of the intelligent driving vehicle and the target triggering the emergency braking decision within a specified future time period, it is determined whether there is a risk of conflict between the target and the intelligent driving vehicle.

[0070] In some embodiments, step 520 may perform one or more of steps 520a, 520b, and 520c to determine whether there is a risk of conflict between the target and the intelligent driving vehicle. For example, step 520a is performed first, followed by step 520b, and then step 520c.

[0071] Step 520a, a collision risk determination method based on trajectory points. Figure 6 As shown, if the trajectory point of the target that triggers the emergency braking decision at any time in the future specified time period is located within the vehicle frame range of the trajectory point of the intelligent driving vehicle at that time, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle.

[0072] Step 520b, a collision risk determination method based on trajectory lines. Figure 7 As shown, if the line connecting the trajectory points of the target that triggers the emergency braking decision in the future specified time period intersects with the line connecting the trajectory points of the intelligent driving vehicle in the future specified time period, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle. Thus, the conflict risk caused by the situation where the target "crosses" the intelligent driving vehicle is identified.

[0073] Step 520c, a method for determining the conflict risk based on emergency braking. Figure 8As shown, based on the motion state information of the last trajectory point of the intelligent driving vehicle and the target that triggers the emergency braking decision in the future specified time period, the emergency braking judgment is performed again; if the result of the second judgment is still that emergency braking is required, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle; if the result of the second judgment is that emergency braking is not required, it is determined that there is no risk of conflict between the target and the intelligent driving vehicle. In this way, the risk of conflict approaching after the future specified time period is identified. In addition, it will be combined later Fig.10 Describe the emergency braking decision-making method.

[0074] In step 530 , if it is determined that there is a risk of conflict, the emergency braking decision is maintained; if it is determined that there is no risk of conflict, the emergency braking decision is cancelled.

[0075] The disclosed embodiment of the present invention additionally introduces a trajectory-based emergency braking decision filtering method after the emergency braking decision is made to maintain or cancel the emergency braking decision, avoid decision conflicts between the AEB system and the PNC system, reduce false triggering of emergency braking by the AEB system, and improve the operating efficiency of the intelligent driving vehicle.

[0076] For example, in the scenario of "being able to detour but not having time to brake", emergency braking would be mistakenly triggered before the introduction of the "trajectory filtering decision algorithm". After the introduction of the "trajectory filtering decision algorithm", the emergency braking decision of the AEB system would be canceled to avoid decision conflicts between the AEB system and the PNC system.

[0077] Combine the following Fig. 9 An example of a travel control method is described. The steps and their execution order in this example are only an example of travel control, and the steps and their execution order can also be executed by other methods.

[0078] Fig. 9 A schematic diagram showing a flow chart of a driving control method according to some embodiments of the present disclosure is shown. Fig. 9 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.

[0079] In step 910 , in response to an upstream emergency braking decision, trajectory points of the intelligent driving vehicle and a target triggering the emergency braking decision within a specified future time period are determined.

[0080] In step 920, it is determined whether the trajectory point of the target triggering the emergency braking decision at any time in the future specified time period is within the vehicle frame range of the trajectory point of the intelligent driving vehicle at that time. If yes, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle, and step 950a is executed. If not, step 930 is executed to continue the judgment.

[0081] In step 930, it is determined whether the line connecting each trajectory point of the target triggering the emergency braking decision in the future specified time period intersects with the line connecting each trajectory point of the intelligent driving vehicle in the future specified time period. If so, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle, and step 950a is executed. If not, step 940 is executed to continue the judgment.

[0082] In step 940, it is determined whether the emergency braking re-determination result is still required for emergency braking. If so, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle, and step 950a is executed. If not, that is, the result of the re-determination is that emergency braking is not required, it is determined that there is no risk of conflict between the target and the intelligent driving vehicle, and step 950b is executed.

[0083] Based on the motion state information of the last trajectory point of the intelligent driving vehicle and the target that triggers the emergency braking decision within a specified time period in the future, the emergency braking decision is made again.

[0084] In step 950a, if it is determined that there is a risk of conflict, the emergency braking decision is maintained.

[0085] In step 950b, when it is determined that there is no risk of conflict, the emergency braking decision is cancelled.

[0086] The disclosed embodiment of the present invention additionally introduces a trajectory-based emergency braking decision filtering method after the emergency braking decision is made to maintain or cancel the emergency braking decision, avoid decision conflicts between the AEB system and the PNC system, reduce false triggering of emergency braking by the AEB system, and improve the operating efficiency of the intelligent driving vehicle.

[0087] Combine the following Fig.10 , describes a method for determining whether to perform emergency braking again. Fig.10 A schematic diagram showing a flow chart of an emergency braking decision method according to some embodiments of the present disclosure. Fig.10 As shown, the emergency braking decision method of this embodiment includes the following steps.

[0088] In step 1010, the position, velocity and acceleration of the target in the intelligent driving vehicle coordinate system at the last trajectory point and the velocity and braking properties of the intelligent driving vehicle are obtained.

[0089] In step 1020, it is determined 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 intelligent driving vehicle coordinate system at the last trajectory point and the velocity and braking properties of the intelligent driving vehicle.

[0090] 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.

[0091] In some embodiments, in step 1020, step 1020c and step 1020d 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 1020a and 1020b may be executed first to preliminarily eliminate the risk of conflict, and then step 1020c and step 1020d may be executed to verify whether there is a risk of conflict between the target and the intelligent driving vehicle.

[0092] Step 1020a, preliminarily judge the conflict risk. Determine whether there is a potential conflict risk 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 conflict risk is quickly judged, and the situation where there is no conflict risk is excluded. If the conflict risk cannot be excluded, the conflict risk is further verified through subsequent judgment. In the case of determining that there is a potential conflict risk, 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.

[0093] Step 1020b, 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. Fig.11 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.

[0094] Step 1020c, determine whether there is a risk of conflict in the radial direction, and then combine Figure 12-14 Description in order to verify the radial conflict risk situation.

[0095] Step 1020d, determining whether there is a risk of conflict in the lateral direction, so as to verify the lateral conflict risk situation.

[0096] In step 1030, if 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.

[0097] 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, and then accurately decide whether emergency braking is required.

[0098] The method for initially determining the conflict risk in step 1020a is described below.

[0099] In some embodiments, according to the radial component of the position of the target in the intelligent driving vehicle coordinate system, 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; in the case where 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 conflict risk between the target and the intelligent driving vehicle; in the case where 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 conflict risk between the target and the intelligent driving vehicle.

[0100] For example, if x < front_edge_to_center and vx < ego_speed, it indicates 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 at the rear side of the vehicle and is gradually falling behind the vehicle, and the distance of falling behind is gradually increasing. 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. If x > front_edge_to_center and vx > ego_speed, it indicates 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, 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.

[0101] In some embodiments, according to the lateral component of the position of the target in the intelligent driving vehicle coordinate system, it is determined whether the target is on the left or right of the intelligent driving vehicle; in the case where 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 conflict risk between the target and the intelligent driving vehicle; in the case where 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 conflict risk between the target and the intelligent driving vehicle.

[0102] For example, if y > right_edge_to_center and vy > 0, it indicates that 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. At this time, the target will gradually move away from the vehicle in the lateral left direction, 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. If y < -front_edge_to_center and vy < 0, it indicates that 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. At this time, the target will gradually move away from the vehicle in the lateral right direction, 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The method of determining whether the target is a candidate target for triggering emergency braking in step 1020b is described below.

[0107] like Fig.11 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.

[0108] The method of determining whether there is a risk of collision in the radial direction in step 1020c is described below.

[0109] 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.

[0110] 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.

[0111] The first case: Fig.12 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.

[0112] 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.

[0113] The second case: Fig.13As 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 decelerates 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.

[0114] 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.

[0115] The third situation: Fig.14 As shown, the target initial speed 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.

[0116] 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.

[0117] After determining the braking distance of the smart driving vehicle and the braking distance of the target, 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 in the radial direction between the target and the smart driving vehicle. 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. That is, if "the braking distance of the smart driving vehicle - the braking distance of the target > the radial distance of the target from the smart driving vehicle (x) - 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 there is no need to trigger emergency braking.

[0118] The method of determining whether there is a risk of conflict in the horizontal direction in step 1020d is described below.

[0119] According to 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, determine 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, determine whether there is a risk of conflict between the target and the intelligent driving vehicle in the lateral direction, so as to verify the risk of lateral conflict. If the target will move into the width range of the intelligent driving vehicle in the lateral direction, determine that there is a risk of conflict between the target and the intelligent driving vehicle in the lateral direction.

[0120] In some embodiments, a method for determining whether a target moves into a width range of an intelligent driving vehicle in a lateral direction includes: determining a time when a collision occurs between the target and the intelligent driving vehicle in a radial direction according to a radial distance between the target and the intelligent driving vehicle and a difference between a speed ego_speed of the intelligent driving vehicle and a radial component vx of a speed of the target, wherein the radial distance between the target and the intelligent driving vehicle is determined according to a radial component x of a position of the target in a coordinate system of the intelligent driving vehicle, that is, calculating a time t when a collision occurs in the radial direction according to t = x / (ego_speed - vx); determining a lateral component of a position of the target at the time of the collision according to a current lateral component y of a position of the target in the coordinate system of the intelligent driving vehicle, a lateral component vy of a speed, and a lateral component ay of an acceleration, and a time t when the collision occurs, that is, determining a lateral component y' of a position of the target at the time of the collision according to y' = y + vy×t + ay×t×t / 2; determining whether the target moves into a width range of the intelligent driving vehicle in a lateral direction according to the lateral component of the position of the target at the time of the collision, 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.

[0121] Fig.15 The schematic diagram of the structure of the driving control device of 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.

[0122] like Fig.15 As shown, the driving control device 1500 of this embodiment includes: a memory 1510 and a processor 1520 coupled to the memory 1510 , and the processor 1520 is configured to execute the driving control method in any of the aforementioned embodiments based on instructions stored in the memory 1510 .

[0123] The driving control device 1500 may further include an input / output interface 1530 , a network interface 1540 , a storage interface 1550 , etc. These interfaces 1530 , 1540 , 1550 , the memory 1510 , and the processor 1520 may be connected via a bus 1560 , for example.

[0124] The memory 1510 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.

[0125] The processor 1520 may 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 and other discrete hardware components.

[0126] Among them, the input and output interface 1530 provides a connection interface for input and output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 1540 provides a connection interface for various networked devices. The storage interface 1550 provides a connection interface for external storage devices such as SD cards and USB flash drives. The bus 1560 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.

[0127] The driving control schemes of the various embodiments of the present disclosure can be applied, for example, to new energy vehicles or other intelligent driving vehicles for emergency braking decisions under new energy driving modes such as plug-in hybrid drive, pure electric drive and fuel cell drive.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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: In response to the emergency braking decision, determining trajectory points of the intelligent driving vehicle and the target that triggered the emergency braking decision within a specified future time period; Determine whether there is a risk of conflict between the intelligent driving vehicle and the target that triggers the emergency braking decision based on the trajectory points of the intelligent driving vehicle and the target in a future specified time period; If it is determined that there is a risk of conflict, the emergency braking decision is maintained; if it is determined that there is no risk of conflict, the emergency braking decision is canceled.

2. The driving control method according to claim 1, characterized in that: Based on the trajectory points of the intelligent driving vehicle and the target that triggers the emergency braking decision in a specified time period in the future, determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: If the trajectory point of the target that triggers the emergency braking decision at any time in the future specified time period is within the vehicle frame range of the trajectory point of the intelligent driving vehicle at that time, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle.

3. The driving control method according to claim 1, characterized in that: Based on the trajectory points of the intelligent driving vehicle and the target that triggers the emergency braking decision in a specified time period in the future, determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: If the line connecting the various trajectory points of the target that triggers the emergency braking decision in a specified future time period intersects with the line connecting the various trajectory points of the intelligent driving vehicle in a specified future time period, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle.

4. The driving control method according to claim 1, characterized in that: Based on the trajectory points of the intelligent driving vehicle and the target that triggers the emergency braking decision in a specified time period in the future, determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: Determine whether the trajectory point of the target that triggers the emergency braking decision is within the vehicle frame of the trajectory point of the intelligent driving vehicle at any time within a specified future time period; If the judgment result is yes, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle; If the judgment result is no, determine whether the line connecting the various trajectory points of the target that triggers the emergency braking decision in the specified future time period intersects with the line connecting the various trajectory points of the intelligent driving vehicle in the specified future time period. If the judgment result is intersecting, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle.

5. The driving control method according to any one of claims 1 to 4, characterized in that: Based on the trajectory points of the intelligent driving vehicle and the target that triggers the emergency braking decision in a specified time period in the future, determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: When it is determined that there is no conflict risk based on the trajectory points, the emergency braking decision is made again based on the motion state information of the last trajectory point of the intelligent driving vehicle and the target that triggered the emergency braking decision within a specified time period in the future; If the result of the second judgment is that emergency braking is still required, it is determined that there is a risk of conflict between the target and the intelligent driving vehicle. If the result of the second judgment is that emergency braking is not required, it is determined that there is no risk of conflict between the target and the intelligent driving vehicle.

6. The driving control method according to claim 5, characterized in that: The decision to perform emergency braking again includes: 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 at the last trajectory point and the speed and braking properties of the intelligent driving vehicle; In the event that a risk of conflict is determined, emergency braking is determined to be necessary.

7. The driving control method according to claim 6, 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 relative to the intelligent driving vehicle at the last trajectory point, 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; In the case where the target is a candidate target for triggering emergency braking, it is determined 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 intelligent driving vehicle coordinate system at the last trajectory point and the velocity and braking properties of the intelligent driving vehicle.

8. The driving control method according to claim 6, wherein: Determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: Determine the braking distance of the target and the braking distance of the intelligent driving vehicle according to the radial component of the velocity and the radial component of the acceleration of the target at the last trajectory point and the speed and braking properties of the intelligent driving vehicle; 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, it is determined whether there is a risk of conflict between the target and the intelligent driving vehicle in the radial direction, wherein the radial distance between the target and 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.

9. The driving control method according to claim 6, wherein: Determining whether there is a risk of conflict between the target and the intelligent driving vehicle includes: Determine whether the target will move within the width of the intelligent driving vehicle in the lateral direction according to the position, velocity, and lateral component of acceleration of the target in the intelligent driving vehicle coordinate system at the last trajectory point and the speed of the intelligent driving vehicle; Whether the target moves into the width range of the intelligent driving vehicle in the lateral direction is determined to determine whether there is a risk of conflict between the target and the intelligent driving vehicle in the lateral direction.

10. 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 to 9 based on instructions stored in the memory.

11. 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 9.

12. 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 9 are implemented.

13. 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 9 are implemented.

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