Intelligent queuing assistance method, system, vehicle and device for vehicle

Abstract

The application discloses a kind of intelligent queuing assistance method, system, vehicle and equipment of vehicle.The intelligent queuing assistance method of vehicle includes: obtaining road condition information and obstacle information;According to the state information of ego vehicle and the obstacle information, the initial target obstacle that influences ego vehicle operation is screened;According to the initial target obstacle, target obstacle is obtained, and according to the type of the target obstacle, the driving trajectory of the ego vehicle is generated;According to the driving trajectory, the intelligent queuing control of ego vehicle is waited, followed or lane change.By using the application, the driver can effectively reduce the frequent operation of the vehicle when queuing, and relieve the driver's high concentration of monitoring the environment, improve the driving comfort when queuing, and avoid traffic creep and other problems caused by slow vehicle response due to conservative driving or slow reaction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to an intelligent queuing assistance method and system for a vehicle, a vehicle and a device. BACKGROUND

[0002] Currently, in many truck operation scenarios, the truck is sometimes queued at the factory gate for a long time to enter the factory for unloading or loading, and the time is as long as several hours or even several days. During this period, the driver needs to maintain attention at all times to follow the vehicle in front and move the vehicle forward at any time, and prevent other vehicles from cutting in line. The frequent acceleration, braking and other operations during the queuing process increase the operating burden of the driver, reduce the driving and riding comfort, cause driver fatigue, and long-term operating fatigue also increases the traffic safety hazard. Although the vehicle integrated cruise (ICA) system or traffic jam assist (TJA) system can play a certain role, but it is generally dependent on lane lines and has limited application scenarios. SUMMARY

[0003] Therefore, it is necessary to provide an intelligent queuing assistance method and system for a vehicle, a vehicle and a device to effectively reduce the frequent manipulation of the vehicle by the driver during queuing, and to alleviate the high concentration of the driver's monitoring of the environment, improve the driving comfort during queuing, and avoid traffic creep caused by slow response of the vehicle due to conservative driving or slow reaction of the driver.

[0004] In a first aspect, an intelligent queuing assistance method for a vehicle is provided, comprising:

[0005] obtaining road condition information and obstacle information;

[0006] screening an initial target obstacle affecting the operation of the ego vehicle according to the state information of the ego vehicle and the obstacle information;

[0007] obtaining a target obstacle according to the initial target obstacle, and generating a driving trajectory of the ego vehicle according to the type of the target obstacle;

[0008] controlling the ego vehicle to wait, follow or change lanes for intelligent queuing according to the driving trajectory.

[0009] In some examples, the screening of the initial target obstacle affecting the operation of the ego vehicle according to the state information of the ego vehicle and the obstacle information comprises:

[0010] virtually generating a driving trajectory of the ego vehicle according to the state information of the ego vehicle to obtain a virtual lane line;

[0011] Based on the virtual lane lines and obstacle information, initial target obstacles are selected, including the obstacles within the virtual lane lines that are closest to and second closest to the vehicle in longitudinal distance, and the obstacles that are closest to the virtual lane lines on the left and right.

[0012] In some examples, obtaining the target obstacle based on the initial target obstacle and generating the vehicle's trajectory based on the type of the target obstacle includes:

[0013] If the obstacle closest to the vehicle in the virtual lane line indicates a lane-changing intention;

[0014] If so, determine whether the nearest obstacle exists;

[0015] If the nearest obstacle exists, then the nearest obstacle is taken as the target obstacle; otherwise, follow the vehicle in front and perform a lane change operation.

[0016] In some examples, it also includes:

[0017] Determine whether the target obstacle is a vehicle;

[0018] If not, issue a takeover alert and initiate obstacle avoidance procedures.

[0019] In some examples, the process of virtually simulating the vehicle's driving trajectory and obtaining virtual lane lines based on the vehicle's state information includes:

[0020] The vehicle's lateral control is adjusted based on the location of the target obstacle; or,

[0021] Record the historical trajectory of the target obstacle and control the vehicle to follow the historical trajectory of the target obstacle.

[0022] In some examples, longitudinal control of the vehicle includes:

[0023] Based on the distance, speed, and acceleration of the target obstacle, and the anticipated lane-changing vehicles, the vehicle's acceleration and deceleration are calculated. Based on the calculated acceleration and deceleration, longitudinal control is applied to the vehicle. This longitudinal control includes at least longitudinal following control, stop-and-go control, targetless parking control, and wheel-end torque command limiting.

[0024] In intelligent queuing mode, EBS is prohibited from distributing torque, and braking energy is distributed through the braking energy distribution module to achieve efficient deceleration control.

[0025] In intelligent queuing mode, after the target ahead is lost, targetless parking control is executed to eventually stop the vehicle in place, then shift to neutral, engage the handbrake, and disengage the function.

[0026] In intelligent queuing mode, an improved IDM model is used to calculate the following control.

[0027] In some examples, lateral control of the vehicle includes:

[0028] Lateral control of the vehicle is achieved using pure tracking, LQR, or MPC methods.

[0029] Secondly, a vehicle intelligent queuing assistance system is provided, comprising:

[0030] The acquisition module is used to obtain road condition information and obstacle information;

[0031] The filtering module is used to filter out the initial target obstacles that affect the operation of the vehicle based on the vehicle's status information and the obstacle information.

[0032] The generation module is used to obtain the target obstacle based on the initial target obstacle, and generate the driving trajectory of the vehicle based on the type of the target obstacle;

[0033] The control module is used to perform intelligent queuing control of the vehicle, such as waiting, following, or changing lanes, based on the driving trajectory.

[0034] Thirdly, a vehicle is provided, comprising: an intelligent queuing assistance system for the vehicle according to the second aspect described above.

[0035] Fourthly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the intelligent queuing assistance method for vehicles described in the first aspect and any possible implementation thereof.

[0036] Fifthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the intelligent queuing assistance method for vehicles according to the first aspect and any possible implementation thereof.

[0037] In a sixth aspect, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the intelligent queuing assistance method for vehicles according to the first aspect and any possible implementation thereof.

[0038] In embodiments of this application, the intelligent queuing function integrates environmental information through sensory perception to perform vehicle-assisted behavior decision-making, trajectory planning, and control. It sends longitudinal and lateral control commands to the control unit to automatically control the vehicle's longitudinal and lateral movement in intelligent queuing situations. This effectively reduces the frequency of vehicle manipulation by the driver while queuing and alleviates the driver's need for highly concentrated environmental monitoring, improving driving comfort during queuing and avoiding traffic crawling problems caused by slow vehicle response due to overly conservative driving or sluggish reaction from the driver. Attached Figure Description

[0039] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0040] Figure 1 A flowchart of the intelligent queuing assistance method for vehicles provided in the embodiments of this application;

[0041] Figure 2 This is a schematic diagram of vehicle control provided in an embodiment of this application;

[0042] Figure 3 A structural block diagram of the intelligent queuing assistance system for vehicles provided in the embodiments of this application;

[0043] Figure 4 This is a structural block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0044] The present application will now be described in further detail with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the application. Furthermore, it should be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings.

[0045] It should be noted that, unless otherwise specified, the embodiments and features of the embodiments in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0046] The following describes in detail, with reference to the accompanying drawings, a method, system, vehicle, and device for intelligent queuing assistance for vehicles according to embodiments of this application.

[0047] Figure 1 This is a flowchart of a vehicle intelligent queuing assistance method according to an embodiment of this application. Figure 1 As shown, the intelligent queuing assistance method for vehicles according to an embodiment of this application includes the following steps:

[0048] S101: Obtain road condition information and obstacle information.

[0049] In one embodiment of this application, vehicle surrounding environment information such as road conditions and obstacles can be obtained through an environmental perception module such as an on-board sensor. Specifically, the vehicle surrounding environment, including road conditions and obstacles, is perceived through on-board sensors, including but not limited to a front-view camera, a front millimeter-wave radar, and a fisheye camera.

[0050] Road condition information includes, but is not limited to, curbs and guardrails on both sides of the vehicle (i.e., the vehicle itself), and road surface smoothness.

[0051] Obstacle information includes, but is not limited to, information such as the distance and speed of the target ahead, which is perceived by the forward-facing camera, the vehicle targets on both sides, which are perceived by the left and right fisheye cameras, and the target ahead distance and speed, which are perceived by the forward-facing millimeter-wave radar.

[0052] S102: Based on the vehicle's status information and the obstacle information, filter out the initial target obstacles that affect the vehicle's operation.

[0053] In one embodiment of this application, initial target obstacles affecting the operation of the vehicle are selected based on the vehicle's status information and the obstacle information, including: virtualizing the vehicle's driving trajectory based on the vehicle's status information to obtain virtual lane lines; and selecting initial target obstacles based on the virtual lane lines and obstacle information, wherein the initial target obstacles include the obstacles within the virtual lane lines that are closest to and second closest to the vehicle in longitudinal distance, and the obstacles that are closest to the virtual lane lines in the left and right directions.

[0054] S103: Obtain the target obstacle based on the initial target obstacle, and generate the vehicle's driving trajectory based on the type of the target obstacle.

[0055] In one embodiment of this application, obtaining a target obstacle based on the initial target obstacle and generating the vehicle's driving trajectory based on the type of the target obstacle includes: if the obstacle closest to the vehicle in the virtual lane line has a lane-changing intention; if so, determining whether there is a second closest obstacle; if there is a second closest obstacle, then taking the second closest obstacle as the target obstacle, otherwise following the vehicle in front to perform a lane-changing operation.

[0056] Furthermore, the intelligent queuing assistance method for vehicles also includes: determining whether the target obstacle is a vehicle; if not, issuing a takeover warning and performing obstacle avoidance operations.

[0057] S104: Based on the driving trajectory, perform intelligent queuing control for the vehicle to wait, follow other vehicles, or change lanes.

[0058] In one embodiment of this application, the virtual driving trajectory of the vehicle is generated based on the vehicle's state information to obtain a virtual lane line, including: performing lateral control of the vehicle based on the position of the target obstacle; or, recording the historical trajectory of the target obstacle and controlling the vehicle to follow the historical trajectory of the target obstacle.

[0059] The longitudinal control of the vehicle includes: calculating the vehicle's acceleration and deceleration based on the distance, speed, and acceleration of the target obstacle and the anticipated lane-changing vehicles, and performing longitudinal control on the vehicle based on the calculated acceleration and deceleration. This longitudinal control includes at least longitudinal following control, stop-and-go control, targetless parking control, and wheel-end torque command limitation. Specifically: in intelligent queuing mode, EBS torque distribution is disabled, and braking energy distribution is performed through the braking energy distribution module to achieve efficient deceleration control; in intelligent queuing mode, after the forward target is lost, targetless parking control is executed to eventually stop the vehicle in place, then shift to neutral, engage the handbrake, and disengage the function; in intelligent queuing mode, an improved IDM model is used for calculating the following control.

[0060] Lateral control of the vehicle includes: using pure tracking, LQR, or MPC methods to perform lateral control of the vehicle.

[0061] In practical applications, steps S102-S104 are actually divided into target selection, tracking and trajectory generation, and vehicle lateral and longitudinal control processes.

[0062] Specifically, the target screening module acquires obstacle perception information and vehicle status information, analyzes and predicts the vehicle's motion state and the obstacle's motion state, and filters out all target obstacles that affect the operation of the corresponding functions of the vehicle.

[0063] Vehicle trajectory prediction: By using the vehicle's state parameters, such as its longitudinal speed, lateral speed, and yaw rate, the virtual vehicle trajectory is generated, and lane lines are further virtualized.

[0064] The system filters target obstacles based on virtual lane lines, obstacle IDs, merging status, and type. Filtered obstacles include the closest obstacle in the current lane, the second closest obstacle in the current lane, and the closest obstacles in adjacent left and right virtual lanes. If the system determines that the closest obstacle in the current lane intends to change lanes, it checks the presence of the second closest obstacle to determine whether to switch obstacles. If the second closest obstacle exists, it reselects the second closest obstacle as the nearest obstacle; otherwise, the vehicle follows the vehicle in front in a lane change operation.

[0065] Non-vehicle target handling strategy: During function activation, if a newly selected target type is a non-vehicle target (pedestrian, two-wheeled vehicle, or other obstacles), the system immediately issues a takeover alert, calculates and issues a collision avoidance deceleration, and requests the EPB to be activated after the vehicle comes to a stop, keeping the vehicle stationary. It continues to follow other vehicles in the queue after the target disappears. If a newly selected target type is a vehicle target, the system recalculates the following trajectory and continues to follow other vehicles in the queue.

[0066] To perform lateral control of the vehicle, a lateral trajectory is generated. The lateral trajectory can be one or more trajectory points. Options include:

[0067] Directly following the vehicle in front: Lateral control can be performed by only needing the position of one point of the vehicle in front;

[0068] Preceding vehicle trajectory scheme: Record the historical trajectory of the preceding vehicle and allow your vehicle to follow that trajectory.

[0069] Horizontal and vertical control:

[0070] Lateral and longitudinal function configurations are based on vehicle type: The IPC determines whether to activate only the longitudinal following queuing function or both the longitudinal and lateral queuing functions based on the vehicle's EHPS type. Lateral control is only permitted if the vehicle is equipped with a drive-by-wire EHPS. A coupled lateral and longitudinal control or a separate lateral and longitudinal control strategy can be used. Optionally, separate lateral and longitudinal control is implemented as follows:

[0071] Vertical control:

[0072] Longitudinal control includes functions such as longitudinal following control, stop-and-go control, targetless parking control, and wheel-end torque command limitation.

[0073] The IPC function allows adjustment of the following distance (using the + / - buttons for multiplexing the distance). When the IPC operating conditions are met, and the driver activates IPC by pressing the button, the default following distance is set as follows:

[0074] 1. When the current driver activates IPC while the vehicle is stationary, and the distance to the vehicle in front is between the minimum safe distance and the maximum set following distance, the actual distance to the vehicle in front will be set as the current following distance.

[0075] 2. If the current driver activates IPC while the vehicle is in motion, the system sets Xm to the current default following distance.

[0076] 3. After the IPC function is activated, the driver can increase or decrease the following distance after stopping the vehicle in front by pressing the button (each increase / decrease increases / decreases the distance by 1m).

[0077] Stop-and-go distance memory: During the current power cycle, the stop-and-go distance set by the driver when the function was last activated is remembered. After power is restarted, the default stop-and-go distance is restored.

[0078] Longitudinal following: By acquiring the distance, speed, and acceleration of the obstacle vehicle ahead, and anticipating lane-changing vehicles, appropriate acceleration and deceleration control is calculated. An improved IDM model is used for following control calculations. The calculation formula is as follows:

[0079]

[0080]

[0081] The results were fused using the ACH constant acceleration model and the IDM model.

[0082]

[0083] Stop-and-go control: During the process of approaching a stop, stopping, or starting, ensure that the vehicle stops within a suitable range and triggers the start condition, and continue following normally.

[0084] 1. When the following stop time is less than T1, the vehicle will automatically follow and start moving after the vehicle in front starts moving;

[0085] 2. If T1 < following stop time < T2s, the system will issue a reminder that the vehicle in front has moved away after the vehicle in front starts moving. The driver needs to gently press the accelerator pedal to start moving again.

[0086] 3. If the downtime is ≥ T3, request EPB to be restarted. After EPB is restarted, the function does not exit, and the system remains active;

[0087] 4. After EPB is engaged, when the conditions for the preceding vehicle to start are met, the system issues a warning that the preceding vehicle has moved away. At this time, the driver lightly presses the accelerator pedal to confirm, and the system issues an EPB release request. After EPB is released, the system continues to perform following control.

[0088] Targetless Parking Control: When the vehicle is stationary, if the target ahead is lost or there is no target ahead for more than T4 hours, the system requests to shift to neutral (N) and engage the EPB (Electronic Power Buffer), ultimately disengaging the IPC (Interactive Parking) function. If the vehicle is following another vehicle and the vehicle ahead gradually moves away, the system requests driver intervention only when the target has reached its maximum distance or is lost, then gradually brings the vehicle to a stop. After stopping, it shifts to neutral (N) and engages the EPB, then disengages the IPC function.

[0089] Wheel-end torque command limit: Based on different scenario conditions (starting from a standstill, driving and following other vehicles, different vehicle weights, etc.), limit the target acceleration of the vehicle at different speeds, and calculate the wheel-end torque output limit based on the acceleration limit.

[0090] Inverse longitudinal dynamics model: The required target torque is calculated using the inverse longitudinal dynamics model.

[0091] Wheel end torque:

[0092] WheelTorque = F_{drive} * R; R is the radius of the vehicle's tires.

[0093] F_{drive}-F_{load}=m*a

[0094] Where F_{drive} is the driving force of the vehicle, used to calculate the wheel-end torque.

[0095] F_{load} represents the vehicle's drag force, including aerodynamic drag, rolling resistance, etc.

[0096] The results were obtained through theoretical calculations or real-vehicle experiments:

[0097] F_{load}=k1*v^2+K2*v+k3, where K1, K2, and K3 are the coefficients obtained from calibration.

[0098] m is the vehicle weight, and a is the expected acceleration.

[0099] Because new energy heavy-duty trucks can switch between motor negative torque braking and EBS braking during braking, the longitudinal control is responsible for distributing braking energy when it calculates negative deceleration. EBS torque distribution is disabled.

[0100] In autonomous driving mode, EBS torque distribution is disabled. Braking energy distribution is instead performed via the brake energy distribution module, achieving more efficient deceleration control. The specific strategy is as follows:

[0101] The required negative torque for braking is calculated based on the calculated braking deceleration. The maximum negative torque capacity of the motor is obtained from the motor control module. If the maximum negative torque capacity of the motor is greater than the required automatic negative torque, the motor will be used for braking. Otherwise, the difference between the two will be supplemented by the EBS module.

[0102] Lateral control:

[0103] Lateral control can employ methods such as pure tracking, LQR, and MPC. Optionally, the pure tracking method is as follows:

[0104] like Figure 2As shown, pure tracking is essentially a proportional controller, directly proportional to the lateral position deviation and inversely proportional to the square of the forward sight distance. It does not consider road curvature; it has good robustness; even on discontinuous roads, its robustness remains excellent. The algorithm's idea is to match a pre-aiming point based on the current rear wheel center position of the vehicle, moving forward a distance ld along the reference path. It assumes the rear wheel center point can reach this pre-aiming point by a certain turning radius R. Then, based on the geometric relationship between the pre-aiming distance ld, the turning radius R, and the orientation angle α of the pre-aiming point in the vehicle coordinate system, the front wheel steering angle is determined.

[0105] Lateral Hand Torque Takeover: When the IPC function is activated, after the driver applies steering wheel hand torque takeover, the function degrades to longitudinal following queuing mode and lateral control does not self-recover. At the same time, a prompt sound and pop-up request are sent to the instrument panel, with the pop-up message stating "The driver has taken over the steering wheel, currently in longitudinal following queuing mode."

[0106] Upgrade to lateral queuing: When the IPC function is downgraded to longitudinal following queuing mode due to the driver's lateral takeover, the driver needs to upgrade to lateral and longitudinal following queuing mode by clicking the IPC activation button again (press and hold the ICA button for more than 1000ms).

[0107] The steering wheel angle value is commanded based on the maximum lateral controllability limit (e.g., ±500°); if the calculated steering wheel angle value is continuously greater than the maximum limit for a set time, it is considered that the lateral control capability is insufficient, and the driver is reminded to take over the lateral control.

[0108] Hand-off detection and reminder:

[0109] When the vehicle is under lateral control, the system continuously monitors the driver's hands-off status. If the driver remains hands-off, the system will prompt the driver to return to the steering wheel via voice. The hands-off warning system has N levels, each corresponding to a different duration of hands-off status (e.g., 10s, 20s, 30s, etc.). If the driver continues to have their hands off the steering wheel even after reaching level N, the system will gradually bring the vehicle to a stop and deactivate the function.

[0110] Human-computer interaction interface:

[0111] The human-computer interaction of the intelligent queuing function is mainly reflected through the instrument panel, central control screen and steering wheel buttons, and the reminders are mainly in three forms: voice reminder, instrument interface reminder pop-up window and text reminder.

[0112] The intelligent queuing assistance method for vehicles in this application embodiment does not rely on lane lines, thus improving the applicability of the function compared to ICA and TJA, and avoiding the risk of lane line misdetection. Applicable scenarios include structured or unstructured roads inside and outside factory areas, with uneven surfaces, unpaved surfaces, no lane lines, but possibly grid lines, parking space lines, etc. A special working condition safety parking mechanism is included: for example, in situations where obstacles suddenly disappear, the system will first remind the driver to take over before exiting the function. If the driver does not take over, the system will execute a safety parking strategy, slowly braking to a stop, shifting to neutral, engaging the handbrake, and then exiting the function to ensure safety. The designed operating speed is within the queuing scenario range (0-30 kph). Within this speed range, lateral and longitudinal assistance are provided to the driver. During this process, the driver can take over the lateral function through the steering wheel, keeping the entire function solely longitudinal. When conditions are met, the system can switch from lateral-only control to lateral and longitudinal control. For some curves with large turning radii, the driver can pass by simply taking over the steering wheel, improving the overall usability of the function. The target type for queuing is vehicles. If the target is pedestrians, bicycles, or other non-vehicles, the driver is reminded to take over the vehicle. If the driver does not take over, a safe parking strategy is implemented. There must be an obstacle within a certain range in front of the vehicle when the queuing function is activated; otherwise, the function cannot be activated. If the lateral activation condition is not met when the queuing function is activated, only the longitudinal function can be activated. Intelligent queuing scenario reminder: The intelligent queuing scenario condition determination module reminds the driver that the automatic queuing function can be entered. The function is activated after the driver confirms by pressing the button.

[0113] According to the intelligent queuing assistance method for vehicles in this application embodiment, the intelligent queuing function inputs and integrates environmental information to perform vehicle-assisted behavior decision-making, trajectory planning and control, and sends longitudinal and lateral control commands to the control unit to achieve automatic control of the vehicle's longitudinal and lateral movement in intelligent queuing situations. This effectively reduces the frequency of vehicle manipulation by the driver while queuing, alleviates the driver's need for highly concentrated environmental monitoring, improves driving comfort while queuing, and avoids traffic creep problems caused by slow vehicle response due to overly conservative driving or sluggish reaction from the driver.

[0114] Figure 3 This is a structural block diagram of an intelligent queuing assistance system for vehicles according to an embodiment of this application. Figure 3 As shown, the intelligent queuing assistance system for vehicles according to an embodiment of this application includes: an acquisition module 310, a filtering module 320, a generation module 330, and a control module 340, wherein:

[0115] The acquisition module 310 is used to obtain road condition information and obstacle information;

[0116] The filtering module 320 is used to filter out the initial target obstacles that affect the operation of the vehicle based on the vehicle's status information and the obstacle information.

[0117] The generation module 330 is used to obtain the target obstacle based on the initial target obstacle, and generate the driving trajectory of the vehicle based on the type of the target obstacle;

[0118] The control module 340 is used to perform intelligent queuing control of the vehicle, such as waiting, following, or changing lanes, based on the driving trajectory.

[0119] According to the vehicle intelligent queuing assistance system of this application embodiment, the intelligent queuing function inputs and integrates environmental information to perform vehicle assisted behavior decision-making, trajectory planning and control, and sends longitudinal and lateral control commands to the control unit to realize automatic control of the vehicle's longitudinal and lateral movement in intelligent queuing situations. This effectively reduces the frequency of vehicle operation by the driver while queuing, alleviates the driver's need for highly concentrated environmental monitoring, improves driving comfort while queuing, and avoids traffic creep problems caused by slow vehicle response due to overly conservative driving or slow reaction by the driver.

[0120] Specific limitations regarding the intelligent queuing assistance system for vehicles can be found in the limitations of the intelligent queuing assistance method for vehicles described above, and will not be repeated here. Each module of the aforementioned intelligent queuing assistance system for vehicles can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0121] In one embodiment, a vehicle is provided, including: an intelligent queuing assistance system according to the above embodiment. The intelligent queuing function of this vehicle inputs and fuses environmental information to perform vehicle-assisted behavior decision-making, trajectory planning, and control. It sends longitudinal and lateral control commands to the control unit to automatically control the vehicle's longitudinal and lateral movement in intelligent queuing situations. This effectively reduces the frequency of vehicle manipulation by the driver while queuing and alleviates the driver's need for highly concentrated environmental monitoring, improving driving comfort while queuing and avoiding traffic crawling problems caused by slow vehicle response due to overly conservative driving or sluggish reaction from the driver.

[0122] The vehicle is, for example, a truck.

[0123] Furthermore, other components and functions of the vehicle according to the embodiments of this application are known to those skilled in the art and will not be described in detail.

[0124] In one embodiment, a computer device is provided.Figure 4 This is a structural block diagram of the computer device provided in the embodiments of this application, with reference to... Figure 4 The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned intelligent queuing assistance method embodiment for vehicles. For example, it executes: obtaining road condition information and obstacle information;

[0125] Based on the vehicle's status information and the obstacle information, the initial target obstacles affecting the vehicle's operation are selected;

[0126] The target obstacle is obtained based on the initial target obstacle, and the driving trajectory of the vehicle is generated based on the type of the target obstacle;

[0127] Based on the driving trajectory, the vehicle performs intelligent queuing control to wait, follow, or change lanes.

[0128] This application also provides a computer-readable storage medium storing a computer program. When the processor executes the computer program, it implements the aforementioned intelligent queuing assistance method embodiment for vehicles. For example, it executes: obtaining road condition information and obstacle information;

[0129] Based on the vehicle's status information and the obstacle information, the initial target obstacles affecting the vehicle's operation are selected;

[0130] The target obstacle is obtained based on the initial target obstacle, and the driving trajectory of the vehicle is generated based on the type of the target obstacle;

[0131] Based on the driving trajectory, the vehicle performs intelligent queuing control to wait, follow, or change lanes.

[0132] This application provides a computer program product including instructions that, when executed, cause the method described in this application embodiment to be performed. For example, it can execute... Figure 1 The steps of the intelligent queuing assistance method for vehicles shown include, for example, obtaining road condition information and obstacle information;

[0133] Based on the vehicle's status information and the obstacle information, the initial target obstacles affecting the vehicle's operation are selected;

[0134] The target obstacle is obtained based on the initial target obstacle, and the driving trajectory of the vehicle is generated based on the type of the target obstacle;

[0135] Based on the driving trajectory, the vehicle performs intelligent queuing control to wait, follow, or change lanes.

[0136] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for intelligent queuing assistance for vehicles, characterized in that, include: Obtain road condition and obstacle information; Based on the vehicle's status information and the obstacle information, the initial target obstacles affecting the vehicle's operation are selected; The target obstacle is obtained based on the initial target obstacle, and the driving trajectory of the vehicle is generated based on the type of the target obstacle; Based on the driving trajectory, the vehicle performs intelligent queuing control to wait, follow, or change lanes. The step of filtering out initial target obstacles affecting the operation of the vehicle based on the vehicle's status information and the obstacle information includes: Based on the vehicle's status information, the vehicle's driving trajectory is simulated to obtain virtual lane lines; Based on the virtual lane lines and obstacle information, initial target obstacles are selected, wherein the initial target obstacles include the obstacles within the virtual lane lines that are closest to and second closest to the vehicle in longitudinal distance, and the obstacles that are closest to the virtual lane lines in the left and right directions. The step of obtaining the target obstacle based on the initial target obstacle and generating the vehicle's driving trajectory based on the type of the target obstacle includes: If the obstacle closest to the vehicle in the virtual lane line indicates a lane-changing intention; If so, determine whether the nearest obstacle exists; If the nearest obstacle exists, then the nearest obstacle is taken as the target obstacle; otherwise, follow the vehicle in front and perform a lane change operation.

2. The intelligent queuing assistance method for vehicles according to claim 1, characterized in that, Also includes: Determine whether the target obstacle is a vehicle; If not, issue a takeover alert and initiate obstacle avoidance procedures.

3. The intelligent queuing assistance method for vehicles according to claim 1, characterized in that, The process of virtually tracing the vehicle's trajectory based on its status information to obtain virtual lane lines includes: The vehicle's lateral control is adjusted based on the location of the target obstacle; or, Record the historical trajectory of the target obstacle and control the vehicle to follow the historical trajectory of the target obstacle.

4. The intelligent queuing assistance method for vehicles according to claim 3, characterized in that, Longitudinal control of the vehicle includes: Based on the distance, speed, and acceleration of the target obstacle, and the anticipated lane-changing vehicles, the vehicle's acceleration and deceleration are calculated. Based on the calculated acceleration and deceleration, longitudinal control is applied to the vehicle. This longitudinal control includes at least longitudinal following control, stop-and-go control, targetless parking control, and wheel-end torque command limiting. Specifically: In intelligent queuing mode, EBS is prohibited from distributing torque, and braking energy is distributed through the braking energy distribution module to achieve efficient deceleration control; In intelligent queuing mode, after the target ahead is lost, targetless parking control is executed to eventually stop the vehicle in place, then shift to neutral, engage the handbrake, and disengage the function. In intelligent queuing mode, an improved IDM model is used to calculate the following control.

5. The intelligent queuing assistance method for vehicles according to claim 4, characterized in that, Lateral control of the vehicle includes: Lateral control of the vehicle is achieved using pure tracking, LQR, or MPC methods.

6. A vehicle intelligent queuing assistance system, characterized in that, A method for implementing intelligent queuing assistance for vehicles according to any one of claims 1-5, comprising: The acquisition module is used to obtain road condition information and obstacle information; The filtering module is used to filter out the initial target obstacles that affect the operation of the vehicle based on the vehicle's status information and the obstacle information. The generation module is used to obtain the target obstacle based on the initial target obstacle, and generate the driving trajectory of the vehicle based on the type of the target obstacle; The control module is used to perform intelligent queuing control of the vehicle, such as waiting, following, or changing lanes, based on the driving trajectory.

7. A vehicle, characterized in that, include: The intelligent queuing assistance system for vehicles according to claim 6.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the intelligent queuing assistance method for vehicles according to any one of claims 1-5.

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