Control method, device and equipment of autonomous vehicle, medium and program product

Abstract

The embodiment of the invention provides a control method and device of an automatic driving vehicle, equipment, a medium and a program product, and relates to the field of automatic driving. In the vehicle driving process, the driving domain controller controls the vehicle to drive according to the driving track of the target vehicle according to the target vehicle selected by the user on the human-computer interaction interface HMI, and therefore personalized driving experience is provided for the user.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a control method, device, equipment, medium and program product for an autonomous driving vehicle. Background Art

[0002] With the development of autonomous driving technology, the control method of autonomous driving vehicles has become particularly critical.

[0003] In existing technical solutions, autonomous vehicles usually control their own driving trajectory by locking onto the driving trajectory of a target vehicle. Specifically, when the end-to-end (E2E) function in the vehicle is successfully activated, the control module will control the vehicle to always follow the vehicle directly in front and adjacent to it as the target, thereby achieving control of the autonomous vehicle.

[0004] However, the human-computer interaction functions of current autonomous driving vehicles during driving are relatively simple, making it difficult to provide a personalized driving experience. Summary of the invention

[0005] The embodiments of the present application provide a control method, device, equipment, medium and program product for an autonomous driving vehicle to provide a personalized driving experience.

[0006] In a first aspect, the present application provides a control method for an autonomous driving vehicle, the method comprising:

[0007] During the driving process of the vehicle, based on the road data and driving parameters of other vehicles, multiple vehicles traveling within a preset range of the vehicle are displayed on the human-machine interaction interface HMI;

[0008] In response to a selection operation by a user, determining a target vehicle for tracking driving from the plurality of vehicles;

[0009] The vehicle is controlled to travel according to the travel trajectory of the target vehicle.

[0010] In a possible design of the first aspect, controlling the vehicle to travel according to the driving trajectory of the target vehicle includes:

[0011] Controlling the vehicle to drive to the lane where the target vehicle is located and to be in an adjacent position directly behind the target vehicle;

[0012] According to the driving parameters of the target vehicle, the driving parameters of the vehicle are adjusted to follow the driving trajectory of the target vehicle.

[0013] In a possible design of the first aspect, adjusting the driving parameters of the vehicle according to the driving parameters of the target vehicle includes:

[0014] Determining at least one adjustment scheme from preset adjustment strategies according to the driving parameters of the target vehicle;

[0015] According to the control scheme, a driving parameter of the vehicle is adjusted.

[0016] In a possible design of the first aspect, the adjustment strategy includes at least one of the following adjustment schemes:

[0017] If the driving parameter of the target vehicle indicates that the target vehicle is accelerating or decelerating, controlling the vehicle to adjust the time headway between vehicles;

[0018] If the driving parameter of the target vehicle indicates that the target vehicle changes lanes laterally, the vehicle is controlled to change lanes laterally.

[0019] In a possible design of the first aspect, controlling the vehicle to adjust the time headway between vehicles includes:

[0020] According to the mapping relationship between the preset vehicle speed range and the preset vehicle headway, a target vehicle headway corresponding to the vehicle's driving speed is obtained;

[0021] The distance between the vehicle and the target vehicle is adjusted according to the driving speed of the vehicle and the target time headway between the vehicles.

[0022] In a possible design of the first aspect, the method further includes:

[0023] If the driving speed of the vehicle is the minimum driving speed preset in the end-to-end E2E function, a collision will occur between the vehicle and the target vehicle, then the vehicle is controlled to trigger a forward collision warning function or an automatic emergency braking function, and the user is prompted to intervene with the brake pedal through the HMI to exit the end-to-end E2E function.

[0024] In a possible design of the first aspect, controlling the vehicle to drive to the lane where the target vehicle is located and to be located at an adjacent position directly behind the target vehicle includes:

[0025] Determining whether the vehicle is in the lane where the target vehicle is located and is in an adjacent position directly behind the target vehicle;

[0026] If the vehicle is not in the lane where the target vehicle is located, or the vehicle is not in an adjacent position directly behind the target vehicle, the traffic efficiency of the traffic flow in different lanes is determined according to the road data within a preset range;

[0027] determining a lane changing trajectory of the vehicle according to the traffic efficiency;

[0028] The vehicle is controlled to perform a lane change operation according to the lane change trajectory, so that the vehicle travels to the lane where the target vehicle is located and is located at an adjacent position directly behind the target vehicle.

[0029] In a possible design of the first aspect, the method further includes:

[0030] If the vehicle has not completed the lane change operation at the end of the first preset time, or the vehicle has not achieved being in the same lane as the target vehicle and in an adjacent position directly behind the target vehicle at the end of the second preset time, or the distance between the vehicle and the target vehicle is greater than a preset distance at the end of the second preset time, the HMI pushes information indicating that the tracking driving function of the vehicle is not achieved, and the first preset time is less than the second preset time.

[0031] In a second aspect, the present application provides a control device for an autonomous driving vehicle, comprising:

[0032] A display module is used to display multiple vehicles traveling within a preset range of the vehicle on a human-machine interaction interface HMI based on road data and driving parameters of other vehicles during the vehicle's driving process;

[0033] A processing module, configured to determine a target vehicle for tracking driving from the plurality of vehicles in response to a selection operation by a user;

[0034] A control module is used to control the vehicle to travel according to the driving trajectory of the target vehicle.

[0035] In a third aspect, the present application provides a driving controller, comprising: a transceiver, a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions;

[0036] The processor executes the computer-executable instructions stored in the memory to implement the control method of the autonomous driving vehicle as described in any one of the first aspects.

[0037] In a fourth aspect, the present application provides a vehicle, comprising: a vehicle body and the driving controller described in the third aspect.

[0038] In a fifth aspect, the present application provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the control method of the autonomous driving vehicle as described in any one of the first aspects.

[0039] In a sixth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, is used to implement a control method for an autonomous driving vehicle as described in any one of the first aspects.

[0040] The control method, device, equipment, medium and program product of the autonomous driving vehicle provided in the embodiments of the present application relate to the field of autonomous driving technology. During the driving process of the vehicle, in order to display multiple vehicles traveling within the preset range of the vehicle to the user in real time on the human-machine interface (Human Machine Interface, referred to as: HMI), the method will obtain road data and driving parameters of other vehicles in real time. In order to enhance the user's personalized driving experience, the method will respond to the user's selection operation on the HMI and control the vehicle to travel according to the driving trajectory of the target vehicle selected by the user. Through this method, the user can obtain a more intuitive and interactive driving experience and enjoy personalized vehicle control. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] Figure 1 A schematic diagram of a control method scenario for an autonomous driving vehicle provided in this application;

[0043] Figure 2 A schematic diagram of a flow chart of a first embodiment of a control method for an autonomous driving vehicle provided in the present application;

[0044] Figure 3 A schematic diagram of a flow chart of a second embodiment of a control method for an autonomous driving vehicle provided in the present application;

[0045] Figure 4 A flowchart of a third embodiment of a control method for an autonomous driving vehicle provided in the present application;

[0046] Figure 5 A schematic diagram of a flow chart of a fourth embodiment of a control method for an autonomous driving vehicle provided in the present application;

[0047] Figure 6 A flowchart of a fifth embodiment of a control method for an autonomous driving vehicle provided in the present application;

[0048] Figure 7 A flowchart of a sixth embodiment of a control method for an autonomous driving vehicle provided in the present application;

[0049] Figure 8 A schematic diagram of a control method for an autonomous driving vehicle provided in this application;

[0050] Fig. 9 A schematic diagram of a process for adjusting the time interval between vehicles provided in this application;

[0051] Fig.10 A schematic diagram of a tracking control process for an autonomous driving vehicle provided in this application;

[0052] Fig.11 A schematic diagram of a control flow of lateral lane change provided in this application;

[0053] Fig.12 A schematic diagram of a flow chart of a control method for an autonomous driving vehicle provided in the present application;

[0054] Fig.13 A schematic diagram of the structure of a control device for an autonomous driving vehicle according to a first embodiment of the present application;

[0055] Fig.14 A schematic diagram of the structure of a driving controller provided for the application.

[0056] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0057] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0058] First, the terms involved in this application are explained:

[0059] Automated Driving Control Unit (ADCU): The core unit of the vehicle's autonomous driving system, responsible for processing sensor data, performing environmental perception and decision-making, and controlling the vehicle's execution system to achieve safe and reliable autonomous driving functions;

[0060] Human Machine Interface (HMI): refers to the medium for interaction and information exchange between vehicle systems and users;

[0061] End-to-End (E2E): A point-to-point automated driving assistance system based on high-precision map navigation;

[0062] Time interval between two vehicles: refers to the time interval between two vehicles traveling consecutively in the same direction, usually expressed in seconds.

[0063] With the continuous advancement of autonomous driving technology, the control method of autonomous driving vehicles has become one of the core issues in technology research and development and application.

[0064] Existing technical solutions usually use a predefined algorithm to lock the target vehicle for tracking driving to achieve autonomous navigation and control of the autonomous driving vehicle. Specifically, when the E2E function in the vehicle is successfully activated, the vehicle's control module will always lock the vehicle directly in front of and adjacent to the current driving vehicle as the target vehicle for tracking driving. This method achieves automatic control of the vehicle by monitoring the movement trajectory of the vehicle in front in real time and adjusting its own driving path.

[0065] However, the existing technical solutions have certain deficiencies in terms of human-computer interaction, road traffic rate and vehicle driving safety. Specifically, when the E2E function in the vehicle is successfully activated, the human-computer interaction is relatively simple, and the driver cannot adjust the vehicle's driving style according to personal preferences, which limits the diversity and personalization of the driving experience and makes it difficult to meet the needs of different users. At the same time, the method of determining the target vehicle in the existing technical solution does not take into account the current traffic conditions of the road. If the vehicle directly in front of and adjacent to the vehicle is always used as the target vehicle for tracking driving, it is easy to reduce the traffic efficiency of the road. In addition, the existing technical solution does not take into account the driving level of the target vehicle, which is easy to cause a collision between the vehicle and the target vehicle, thereby reducing the safety of vehicle driving.

[0066] In response to the above problems, the inventor discovered during the research on the control method of the autonomous driving vehicle that the existing technical solutions only use other vehicles directly in front of and adjacent to the current autonomous driving vehicle as target vehicles when realizing the tracking driving of the autonomous driving vehicle, and the interaction method provided to the driver and the vehicle is relatively simple. Based on this, the inventor considered whether it is possible to use the vehicle selected by the driver as the target vehicle for tracking driving. Specifically, during the driving process of the vehicle, based on the road data and the driving parameters of other vehicles, multiple vehicles traveling within the preset range of the vehicle are displayed in real time on the HMI, and the vehicle is controlled to travel along the driving trajectory of the target vehicle determined by the user. In this way, users can independently select the most suitable target vehicle according to personal preferences and real-time traffic conditions, thereby achieving a more personalized and flexible driving experience.

[0067] Figure 1 A schematic diagram of a control method scenario for an autonomous driving vehicle provided in this application. Figure 1As shown, the application scenario of the control method of the autonomous driving vehicle provided by the present application is a scenario of human-machine co-driving. In the scenario of human-machine co-driving, a vehicle 100 is included. Among them, HMI 101, ADCU 102 and vehicle-mounted sensor 103 are deployed in the vehicle 100. Although Figure 1 Only one vehicle 100 is shown in FIG. 1 , but it should be understood that two or more vehicles 100 may be present.

[0068] The vehicle 100 is primarily an autonomous driving vehicle deployed with an ADCU 102 .

[0069] The vehicle-mounted sensor 103 includes, but is not limited to, any one or any combination of a laser radar, a camera, and an ultrasonic radar, and this application does not make any specific limitation on this.

[0070] During the driving process of the vehicle 100, the HMI 101 displays multiple vehicles traveling within a preset range of the vehicle 100 in real time based on the road data and driving parameters of other vehicles acquired in real time by the sensor 103. At the same time, the ADCU 102 controls the vehicle 100 to travel according to the driving trajectory of the target vehicle selected by the user in response to the user's selection operation on the HMI 101.

[0071] It should be noted that the above scenario is only an example of an application scenario provided by an embodiment of the present application. The embodiment of the present application does not limit the actual form of the various devices included in the scenario. In the specific application of the solution, it can be set according to actual needs.

[0072] It should be noted that the term "self-vehicle" refers to a vehicle that follows a target vehicle and the terms "target vehicle" and "target vehicle" are interchangeable in this article.

[0073] For ease of description, the following describes the implementation of the control method for an autonomous driving vehicle by taking the execution subject ADCU 102 as an example. It can be understood that the execution subject of the method is ADCU 102 is only an exemplary description and should not be understood as a limitation on the method.

[0074] The technical solutions shown in the present application are described in detail below through specific embodiments. It should be noted that the following embodiments can exist independently or in combination with each other, and the same or similar contents will not be described repeatedly in different embodiments.

[0075] Figure 2 This is a flow chart of a control method for an autonomous driving vehicle provided in this application. Figure 2 As shown, the process of the control method of the automatic driving vehicle may include:

[0076] S201: During vehicle driving, based on road data and driving parameters of other vehicles, multiple vehicles traveling within a preset range of the vehicle are displayed on the HMI.

[0077] In this step, when the vehicle is in the state where the E2E function is activated, the vehicle travels to the end position according to the starting position defined by the user. In order to enhance the user's personalized driving experience, this application displays multiple vehicles traveling within a preset range during the driving process to the user in real time in the form of HMI to ensure that the user can intuitively obtain the required information.

[0078] Specifically, during vehicle driving, the perception module configured in the ADCU provides real-time road data and other vehicle driving parameters within a preset range, providing data support for the HMI to display information in real time.

[0079] In a possible implementation manner, the preset range is determined according to a detection range of an onboard sensor configured in the vehicle.

[0080] Road data includes but is not limited to any one or any combination of the lane where the vehicle is located, road type, road width, lane lines, guide lines, etc., and this application does not make any specific limitations on this.

[0081] The driving parameters of other vehicles include but are not limited to any one or any combination of the position, speed, acceleration, driving direction, driving trajectory, etc. of other vehicles, and this application does not make any specific limitation on this.

[0082] It is worth noting that the road data and other vehicle driving parameters provided by the perception module can be obtained through any one or any combination of cloud servers, vehicle-mounted sensors, or navigation services such as Amap, and this application does not make any specific limitations on this.

[0083] Among them, the cloud server can receive driving parameters and real-time road data from other vehicles, and the vehicle communicates with the cloud server through the on-board system to download the driving parameter data and road data of other vehicles, thereby obtaining the driving parameters and road data of other vehicles.

[0084] The vehicle-mounted sensors include but are not limited to any one or any combination of cameras, lidar, radar sensors, and inertial measurement units, which are not specifically limited in this application. The vehicle-mounted sensors are the core components for realizing autonomous driving and advanced driver assistance systems, and can directly collect road data and other vehicle driving parameters within a preset range.

[0085] Navigation services such as height maps can collect and provide basic road information, real-time traffic flow, congestion conditions, etc.

[0086] The flexibility and diversity of ways to obtain road data and other vehicle driving parameters allow vehicles to select the optimal data source according to different driving scenarios, thereby improving the safety of autonomous driving.

[0087] S202: In response to a selection operation by a user, a target vehicle for tracking driving is determined from a plurality of vehicles.

[0088] In this step, according to step S201, based on a plurality of vehicles traveling within a preset range of vehicles displayed on the HMI, the decision module configured in the ADCU responds to the user's selection operation.

[0089] The user's selection operation refers to the user's selection of a target vehicle from the displayed multiple vehicles as the target vehicle for tracking. This process is designed to enhance the user's sense of participation and control, allowing the user to select the most appropriate target vehicle based on their own judgment to improve driving safety and efficiency.

[0090] In one possible implementation, the user can double-click the target vehicle through the HMI to make a selection, and then perform a secondary confirmation to ensure the accuracy of the selected vehicle. This double-click selection and secondary confirmation mechanism not only provides a user-friendly interaction method, but also reduces the risk of misoperation, ensuring that the user can fully understand and confirm their decision during the selection process. At the same time, it is worth noting that the specific method for the user to perform the selection operation is not specifically limited in this application, that is, it supports multiple selection methods, including but not limited to touch, drag, voice commands, etc., to adapt to the usage habits and needs of different users, thereby improving the overall user experience.

[0091] In a possible implementation, before the user selects a target vehicle, the perception module configured in the ADCU provides data support for the information displayed by the HMI, and simultaneously sends the acquired road data and driving parameters of other vehicles to the prediction module configured in the ADCU. The prediction module predicts the traffic efficiency of different lanes based on the data transmitted by the perception module, and sends the prediction result and the data transmitted by the perception module to the decision module configured in the ADCU. The decision module plans the vehicle's travel path, and the control module controls the vehicle's acceleration and deceleration, steering wheel angle, headlights and other information according to the planned vehicle's travel path to dynamically adjust the travel path.

[0092] S203: Control the vehicle to travel according to the driving trajectory of the target vehicle.

[0093] In this step, according to step S202, when the decision module responds to the user's selection operation, the control module controls the vehicle to travel according to the driving trajectory of the target vehicle.

[0094] Specifically, the control module accurately controls the driving trajectory of the vehicle based on the driving trajectory of the target vehicle selected by the user in step S202. The driving trajectory of the target vehicle is used as a guidance reference, and the control module adjusts the acceleration, braking, steering and other operations of the vehicle in real time to ensure that the vehicle can accurately move along the driving path of the target vehicle.

[0095] It is worth noting that the control module will continuously monitor the behavior dynamics of the target vehicle to ensure that the vehicle's driving trajectory can be consistent with the target vehicle under different road conditions and traffic conditions to avoid the risk of deviation or collision. The specific implementation of this step can refer to the following steps S301 to S302, which will not be repeated here.

[0096] The control method of the autonomous driving vehicle provided in the embodiment of the present application displays multiple vehicles traveling within the preset range of the vehicle to the user in the form of HMI, ensuring the user's intuitiveness. At the same time, compared with the existing technical solution that always takes the vehicle directly in front of the vehicle and the adjacent vehicle as the target vehicle, the present application controls the vehicle to travel along the driving trajectory of the target vehicle determined by the user based on the user's selection operation, thereby improving the driving safety and enhancing the user's personalized driving experience.

[0097] Next, the specific implementation process of step S203 will be discussed in detail. Figure 3 This is a flow chart of a second embodiment of a control method for an autonomous driving vehicle provided in this application. Figure 3 As shown, based on the above embodiment, the vehicle is controlled to travel according to the driving trajectory of the target vehicle. The process of the control method of the automatic driving vehicle may include:

[0098] S301: Control the vehicle to drive to the lane where the target vehicle is located and to be in an adjacent position directly behind the target vehicle.

[0099] In this step, according to step S202, when the decision module responds to the user's selection operation, the control module controls the vehicle to travel according to the driving trajectory of the target vehicle.

[0100] Specifically, the premise for controlling the vehicle to travel according to the driving trajectory of the target vehicle is to ensure that the vehicle and the target vehicle are in the same lane and the vehicle is in an adjacent position directly behind the target vehicle.

[0101] In a possible implementation, if the vehicle and the target vehicle are not in the same lane or the vehicle is not directly behind and adjacent to the target vehicle, the decision module configured in the ADCU plans the vehicle to travel to the lane where the target vehicle is located and to be located in an adjacent position directly behind the target vehicle based on the road data provided by the perception module and the driving parameters of other vehicles and the traffic efficiency of the lane provided by the prediction module in advance, and the control module controls the vehicle to travel to the lane where the target vehicle is located and to be located in an adjacent position directly behind the target vehicle according to the planned driving trajectory. The specific control process can refer to steps S401 to S404, which will not be repeated here.

[0102] S302: According to the driving parameters of the target vehicle, adjusting the driving parameters of the vehicle to follow the driving trajectory of the target vehicle.

[0103] In this step, when the vehicle and the target vehicle are in the same lane and the vehicle is in an adjacent position directly behind the target vehicle, the decision module configured in the ADCU will start to dynamically adjust the vehicle's driving parameters.

[0104] Specifically, the perception module configured in the ADCU will actively monitor the driving status of the target vehicle, including its speed, acceleration, steering angle and other driving parameters, and send the monitored data to the decision module in real time. In order to ensure safe and stable following driving, the decision module will adjust the driving parameters of the vehicle according to the driving parameters of the target vehicle.

[0105] In one possible implementation, when the target vehicle accelerates, the decision module will instruct the vehicle to increase the throttle appropriately to maintain a safe distance from the target vehicle. If the target vehicle decelerates or brakes, the decision module will issue a braking command in time to avoid a rear-end collision. At the same time, the decision module will also monitor the distance to the target vehicle in real time to ensure that it remains within a preset safety range, further improving driving safety.

[0106] It is worth noting that the decision module not only relies on the driving parameters of the target vehicle, but also comprehensively considers the current road conditions, traffic signals and the dynamic behaviors of other surrounding vehicles. Through this multi-dimensional data analysis, the decision module can more intelligently determine when to accelerate, decelerate or change lanes, so that the vehicle can travel smoothly in a complex traffic environment, effectively improving the vehicle's following performance, so that it can follow the target vehicle's driving trajectory more safely and smoothly. The specific implementation method of adjusting the vehicle's driving parameters can refer to the following steps S601 to S602, which will not be repeated here.

[0107] The control method of the autonomous driving vehicle provided in the embodiment of the present application provides the premise and implementation method for the vehicle to follow the target vehicle in tracking driving. Specifically, the premise of tracking driving is that the vehicle must be in the same lane as the target vehicle, and the vehicle is in an adjacent position directly behind the target vehicle to ensure the safety and accuracy of tracking driving. At the same time, according to the driving parameters of the target vehicle, the driving parameters of the vehicle are dynamically adjusted to ensure that the vehicle can smoothly follow the driving trajectory of the target vehicle and avoid safety hazards caused by sudden acceleration or deceleration.

[0108] Next, the specific implementation process of step S301 will be discussed in detail. Figure 4 This is a flow chart of a control method for an autonomous driving vehicle provided in this application. Figure 4 As shown, based on the above embodiment, the vehicle is controlled to drive to the lane where the target vehicle is located and is located in an adjacent position directly behind the target vehicle. The process of the control method of the automatic driving vehicle may include:

[0109] S401: Determine whether the vehicle is in the lane where the target vehicle is located and is in an adjacent position directly behind the target vehicle.

[0110] In this step, according to step S202, after the decision module responds to the user's selection operation, it is necessary to pre-determine whether the vehicle is in the lane where the target vehicle is located and is in an adjacent position directly behind the target vehicle.

[0111] In a possible implementation, when the decision module responds to the user's selection operation, the perception module first obtains the driving parameters of the vehicle and the target vehicle, including position, speed, and lane information, based on the cloud server, vehicle-mounted sensors, or navigation services such as AutoNavi Maps. Then, the current lane of the vehicle is confirmed using the map matching algorithm and lane recognition technology. By calculating the relative position of the vehicle and the target vehicle, combined with the speed and acceleration information, it is determined whether the vehicle is behind and adjacent to the target vehicle. Finally, the decision module analyzes these data and outputs the judgment result for subsequent operations.

[0112] If the vehicle is in the lane where the target vehicle is located and is in an adjacent position directly behind the target vehicle, step S302 is executed and will not be repeated here.

[0113] If the vehicle is not in the lane where the target vehicle is located, or the vehicle is not in an adjacent position directly behind the target vehicle, step S402 is executed, which will not be repeated here.

[0114] S402: If the vehicle is not in the lane where the target vehicle is located, or the vehicle is not in an adjacent position directly behind the target vehicle, the traffic efficiency of the traffic flow in different lanes is determined based on the road data within a preset range.

[0115] In this step, according to the judgment result of step S401, if the vehicle is not in the lane where the target vehicle is located, or the vehicle is not in the adjacent position directly behind the target vehicle, the traffic efficiency of the traffic flow in different lanes is determined based on the road data within a preset range.

[0116] Specifically, the road data within the preset range is the basis for determining the traffic efficiency of different lanes. Among them, based on the road data, the decision module can determine any one or any combination of traffic density, average speed, and traffic light cycle. Traffic density refers to the number of vehicles per unit length on each lane. High density is used to indicate lane congestion, and low density is used to indicate unobstructed lanes. Average speed refers to the average speed of vehicles on each lane. A higher average speed usually indicates that the lane has a higher traffic efficiency. The traffic light cycle refers to the time arrangement of the traffic lights, which will affect the lane capacity to a certain extent.

[0117] The decision module can calculate the traffic efficiency of each lane by analyzing the road data within a preset range. Traffic efficiency is a comprehensive indicator that reflects the number of vehicles that can pass through a lane in a unit of time.

[0118] In a possible implementation, after the decision module obtains the road data within a preset range, the obtained road data is input into a preset mathematical model to obtain the traffic efficiency of each lane. The preset mathematical model will comprehensively consider the data with different attributes in the road data, and assign corresponding weights to the data with different attributes according to actual needs, so as to calculate the traffic efficiency of each lane. It is worth noting that the preset mathematical model includes but is not limited to any one or any combination of macro traffic flow models, machine learning models, etc., and this application does not make specific limitations on this.

[0119] S403: Determine the lane change trajectory of the vehicle according to the traffic efficiency.

[0120] In this step, according to step S402, after the traffic efficiency of the traffic flow in different lanes is determined, the lane change trajectory of the vehicle is determined according to the traffic efficiency.

[0121] The lane change trajectory of the vehicle is determined in order to enable the vehicle to travel to the lane where the target vehicle is located and to be in an adjacent position directly behind the target vehicle.

[0122] In one possible implementation, the decision module analyzes the traffic efficiency of each lane and identifies the efficiency difference between the current lane and the target lane. If the traffic efficiency of the target lane is higher and changing lanes helps improve the overall driving efficiency, the decision module will consider changing lanes. Specifically, the decision module uses preset path planning algorithms, such as the shortest path algorithm and Bezier curves, to design smooth and efficient lane change trajectories, while combining dynamic planning to adapt to real-time traffic changes. In addition, the decision module will comprehensively consider safety factors, monitor the surrounding environment through on-board sensors, and ensure that lane changes are performed under safe conditions.

[0123] S404: Control the vehicle to perform a lane change operation according to the lane change trajectory, so that the vehicle drives to the lane where the target vehicle is located and is located in an adjacent position directly behind the target vehicle.

[0124] In this step, the control module controls the vehicle to perform a lane change operation according to the lane change trajectory obtained in step S403 to ensure that the vehicle can smoothly enter the lane where the target vehicle is located and accurately locate in the adjacent position directly behind the target vehicle.

[0125] In a possible implementation, the perception module first monitors the surrounding traffic environment in real time based on vehicle-mounted sensors, cloud servers, or the navigation service of Amap to ensure that there are no obstacles or other vehicles in adjacent lanes that interfere with the lane change operation.

[0126] If the lane change conditions are confirmed to be safe, the control module will accurately adjust the vehicle's steering, speed and acceleration based on the generated lane change trajectory. The steering system will gradually adjust the steering wheel angle to allow the vehicle to move smoothly along the calculated trajectory. At the same time, the power system will accelerate or decelerate as needed to maintain a safe distance from the target vehicle and ensure a smooth lane change process.

[0127] During the lane change process, the decision module continuously monitors the actual position and speed of the vehicle and compares it with the lane change trajectory obtained in step S503. If a deviation is detected, the decision module will dynamically adjust the control parameters to ensure that the vehicle accurately follows the predetermined trajectory. In addition, the decision module will also make adjustments based on real-time traffic conditions. For example, if the traffic conditions of the target lane change, the decision module may re-plan the lane change strategy.

[0128] When the vehicle successfully enters the target lane and is located directly behind the target vehicle, the decision module confirms the completion of the lane change operation. At this time, the vehicle's sensors scan the surrounding environment again to ensure that the vehicle is in a safe position and then proceed with the subsequent tracking.

[0129] The control method of the autonomous driving vehicle provided in the embodiment of the present application performs a lane change operation by using a control module based on a driving trajectory determined by a decision module according to the traffic efficiency of different lanes when the vehicle is not in the lane where the target vehicle is located or is not in an adjacent position directly behind the target vehicle. This method can dynamically optimize the vehicle's driving path, improve driving efficiency and road resource utilization, and ensure that the vehicle can quickly and safely adjust to the target position. At the same time, through intelligent lane change decision-making and execution, the vehicle can not only adapt to complex traffic environments more effectively, reduce traffic congestion, and improve the smoothness and safety of the overall traffic flow, but also make personalized adjustments based on real-time traffic conditions and passenger preferences, thereby providing passengers with a more comfortable, efficient and personalized driving experience.

[0130] Figure 5 This is a flow chart of a fourth embodiment of a control method for an autonomous driving vehicle provided by the present application. Figure 5 As shown, based on the above embodiment, the process of the control method of the automatic driving vehicle also includes:

[0131] S501: If the vehicle has not completed the lane change operation at the end of the first preset time, or the vehicle has not achieved being in the same lane as the target vehicle and in an adjacent position directly behind the target vehicle at the end of the second preset time, or the distance between the vehicle and the target vehicle is greater than the preset distance at the end of the second preset time, the HMI pushes information indicating that the tracking driving function of the vehicle is not achieved, and the first preset time is less than the second preset time.

[0132] In this step, in order to ensure driving safety, there are preset time limits and distance limits when controlling the vehicle to perform lane change operations according to the lane change trajectory so that the vehicle drives to the lane where the target vehicle is located and is located in an adjacent position directly behind the target vehicle.

[0133] Specifically, the duration set in this application includes a first preset duration and a second preset duration, and the first preset duration is less than the second preset duration. The first preset duration and the second preset duration are mainly used to monitor the progress of the lane change operation and vehicle positioning.

[0134] The first preset time is a short time window specifically designed to ensure that the vehicle can complete the lane change within a reasonable time. If the vehicle fails to complete the lane change at the end of the first preset time, the HMI pushes a message indicating that the vehicle's tracking driving function is not implemented, and the vehicle continues to perform basic E2E operations such as adaptive cruise control and lane keeping assist.

[0135] The second preset time is a longer time window, which is mainly used to determine whether the vehicle can achieve the same lane and adjacent position behind the target vehicle within a longer time range after completing the lane change. If the vehicle does not achieve the same lane and adjacent position behind the target vehicle at the end of the second preset time, the HMI pushes a message indicating that the vehicle's tracking driving function is not realized, and the vehicle continues to perform basic E2E operations, such as adaptive cruise control and lane keeping assist.

[0136] At the same time, in the process of achieving the vehicle and the target vehicle being in the same lane and in an adjacent position directly behind the target vehicle, the decision module continuously monitors the vehicle's position and the relative distance to the target vehicle. If the distance between the vehicle and the target vehicle is greater than the preset distance at the end of the second preset time period, the HMI pushes information indicating that the vehicle's tracking driving function is not implemented, and the vehicle continues to perform basic E2E operations such as adaptive cruise control and lane keeping assist.

[0137] It is worth noting that if any of the above conditions are not met, the vehicle's HMI will actively push information to the user, indicating that the vehicle's tracking driving function has not been realized. This information feedback mechanism improves the transparency of the system, allowing users to understand the vehicle's operating status in a timely manner and perform manual intervention when necessary.

[0138] The control method of the autonomous driving vehicle provided in the embodiment of the present application determines whether the vehicle is in the same lane as the target vehicle and in an adjacent position directly behind the target vehicle by setting a preset time and a preset distance, thereby optimizing traffic flow, reducing unnecessary lane occupancy, and providing real-time feedback through the HMI to enhance the user's driving experience.

[0139] Next, the specific implementation process of step S302 will be discussed in detail. Figure 6 This is a flow chart of a fifth embodiment of a control method for an autonomous driving vehicle provided in this application. Figure 6 As shown, based on the above embodiment, according to the driving parameters of the target vehicle, the driving parameters of the vehicle are adjusted. The process of the control method of the automatic driving vehicle may include:

[0140] S601: Determine at least one adjustment scheme from preset adjustment strategies according to the driving parameters of the target vehicle.

[0141] In this step, when the vehicle and the target vehicle are in the same lane and the vehicle is in an adjacent position directly behind the target vehicle, the decision module configured in the ADCU will start to dynamically adjust the vehicle's driving parameters.

[0142] Specifically, at least one adjustment scheme is determined from the preset adjustment strategies according to the driving parameters of the target vehicle. The driving parameters of the target vehicle can indicate the driving state of the target vehicle, such as accelerating, decelerating, driving at a constant speed, lateral lane change, etc. In order to ensure that the vehicle follows the target vehicle, the decision module will determine at least one adjustment scheme from the preset adjustment strategies according to the driving state of the target vehicle.

[0143] Optionally, the adjustment scheme includes but is not limited to any one or any combination of increasing the vehicle's driving speed, reducing the vehicle's driving speed, and controlling the vehicle's lateral lane change.

[0144] S602: Adjust the driving parameters of the vehicle according to the adjustment plan.

[0145] In this step, according to step S601, after at least one adjustment scheme is determined from preset adjustment strategies according to the driving parameters of the target vehicle, the driving parameters of the vehicle are adjusted according to the adjustment scheme.

[0146] In a possible implementation, the adjustment strategy includes at least one of the following adjustment schemes:

[0147] If the driving parameters of the target vehicle indicate that the target vehicle is accelerating or decelerating, the vehicle is controlled to adjust the time distance between vehicles;

[0148] If the driving parameters of the target vehicle indicate that the target vehicle changes lanes laterally, the vehicle is controlled to change lanes laterally.

[0149] Specifically, the perception module acquires the target vehicle's driving parameters in real time, including the target vehicle's position, speed, acceleration, etc. The decision module calculates the speed difference based on the continuously measured speed of the target vehicle. If the speed difference is positive, it is determined that the target vehicle is accelerating. If the speed difference is negative, it is determined that the target vehicle is decelerating. Based on whether the target vehicle is accelerating or decelerating, the control module controls the vehicle to adjust the inter-vehicle time interval. The inter-vehicle time interval refers to the time interval between the vehicle and the target vehicle, usually expressed in seconds.

[0150] In a possible implementation, if the target vehicle is accelerating, the control module appropriately increases the vehicle's driving speed to shorten the inter-vehicle time interval. It is worth noting that the maximum driving speed of the vehicle cannot exceed the maximum driving speed within the E2E function.

[0151] In another possible implementation, if the target vehicle is decelerating, the control module appropriately reduces the vehicle's speed to increase the headway. It is worth noting that the vehicle's minimum speed cannot be lower than the minimum speed within the E2E function.

[0152] The specific method of controlling the vehicle to adjust the time headway between vehicles can be referred to the following steps S701 to S702, which will not be described in detail here.

[0153] If the decision module determines that the target vehicle changes lanes laterally based on the driving parameters of the target vehicle, compared with the prior art solution, when the target vehicle changes lanes laterally, the vehicle does not follow the lateral lane change. The present application controls the vehicle to follow the lateral lane change of the target vehicle.

[0154] Specifically, if the decision module determines that the target vehicle's steering is turned on based on the turn signal data of the target vehicle obtained in real time by the perception module, and identifies that the target vehicle is moving to the adjacent lane based on the lateral position change of the target vehicle obtained in real time by the perception module, then the optimal lane change path and timing are calculated based on the real-time lane conditions to ensure that the vehicle does not collide with other vehicles during the lane change process. After determining the safety and necessity of the lane change, the decision module formulates a specific lane change strategy, including the direction and speed of the lane change. Based on the lane change strategy, the control module accurately controls the steering angle and accelerator pedal to achieve a smooth lateral lane change. At the same time, during the execution of the specific lane change operation, the decision module monitors the surrounding environment in real time and adjusts the speed and direction of the vehicle in real time to deal with possible emergencies.

[0155] It is worth noting that during the lane change process, the decision module can also adjust the lane change behavior in real time based on the user's driving preferences and driving strategies. After the vehicle successfully changes lanes laterally, the HMI prompts the user that the lane change is successful and continues to execute the tracking driving function.

[0156] The adjustment scheme is not limited to any one or combination of the above two schemes. Optionally, the adjustment scheme may also include any one or combination of the following schemes.

[0157] If the driving parameters of the target vehicle indicate that the target vehicle is unstable within the lane, the vehicle is controlled to perform lane keeping.

[0158] If the driving parameters of the target vehicle indicate that there is an obstacle or potential collision risk ahead, the control vehicle triggers emergency braking.

[0159] If the target vehicle's driving parameters indicate a change in traffic flow, the vehicle is controlled to adjust its speed to maintain a safe distance.

[0160] If the driving parameters of the target vehicle indicate that the target vehicle is changing lanes and there is an obstacle in the blind spot, the vehicle is controlled to issue a warning or assist in changing lanes.

[0161] If the driving parameters of the target vehicle indicate that a traffic sign has been recognized, the vehicle is controlled to adjust its speed or driving strategy in response to the traffic sign.

[0162] If the driving parameters of the target vehicle indicate that visibility is low, the vehicle is controlled to use night vision or low visibility assistance functions.

[0163] If the driving parameters of the target vehicle indicate that the path needs to be optimized, the vehicle is controlled to perform path planning and optimization.

[0164] If the driving parameters of the target vehicle indicate that there are obstacles in the surrounding environment, the vehicle is controlled to perform dynamic obstacle avoidance.

[0165] The control method of the autonomous driving vehicle provided in this embodiment provides an adjustment scheme during the tracking driving process. Specifically, the vehicle can flexibly respond to the dynamic behavior of the target vehicle to ensure safety and following efficiency in various traffic scenarios. This adjustment mechanism not only reduces the risk of rear-end collisions, but also optimizes the vehicle's driving path and improves the smoothness and efficiency of the overall traffic flow.

[0166] The control method of the autonomous driving vehicle provided in the embodiment of the present application mainly describes how to adjust the driving parameters of the vehicle according to the driving parameters of the target vehicle. Specifically, the adjustment of the vehicle driving parameters is mainly based on at least one determined adjustment scheme. Through this precise parameter adjustment, the vehicle can maintain the optimal vehicle headway and driving path in various traffic scenarios, significantly reducing the risk of rear-end and side collisions, and improving driving safety. At the same time, the vehicle can more effectively adapt to changes in traffic flow, reduce unnecessary braking and acceleration, thereby improving fuel efficiency and user comfort. In addition, the control method also promotes the smoothness of traffic flow, reduces the possibility of traffic congestion, and thereby improves the overall road network capacity.

[0167] Figure 7 This is a flow chart of a sixth embodiment of a control method for an autonomous driving vehicle provided in this application. Figure 7 As shown, based on the above embodiment, the vehicle is controlled to adjust the time headway between vehicles. The process of the control method of the automatic driving vehicle may include:

[0168] S701: Obtain a target time headway between vehicles corresponding to a vehicle's travel speed according to a mapping relationship between a preset vehicle speed range and a preset time headway between vehicles.

[0169] In this step, if the driving parameters of the target vehicle indicate that the target vehicle accelerates or decelerates, the vehicle is controlled to adjust the time distance between vehicles. When the vehicle is controlled to adjust the time distance between vehicles, it is necessary to obtain in advance the mapping relationship between the preset vehicle speed range and the preset time distance between vehicles.

[0170] Specifically, a set of mapping relationships are pre-set in the vehicle, which link different vehicle speed ranges with corresponding vehicle headway distances. These mapping relationships are formulated based on traffic safety standards, vehicle dynamics characteristics, and a large amount of road test data. For example, when the vehicle's driving speed is greater than 0km / h and less than or equal to 30km / h, the vehicle headway distance is 2 seconds. When the vehicle's driving speed is greater than 30km and less than or equal to 60km / h, the vehicle headway distance is 3 seconds. When the vehicle's driving speed is greater than 60km / h, the vehicle headway distance is 4 seconds or longer. It is worth noting that the number of mapping relationships is determined according to the adaptability of different vehicle types, and this application does not make specific limitations on this.

[0171] When controlling the vehicle to adjust the headway, the decision module determines the speed range based on the current vehicle speed, and then obtains the target headway corresponding to the vehicle speed based on the mapping relationship between the speed range and the preset headway. The target headway is a key parameter to ensure that the vehicle can safely follow the vehicle ahead at the current speed.

[0172] S702: Adjust the distance between the vehicle and the target vehicle according to the driving speed of the vehicle and the target inter-vehicle time distance.

[0173] In this step, according to step S701, after the target inter-vehicle time distance corresponding to the driving speed of the completion vehicle is obtained, the distance between the vehicle and the target vehicle is adjusted according to the driving speed of the vehicle and the target inter-vehicle time distance.

[0174] Specifically, the perception module continuously monitors the actual inter-vehicle distance between the vehicle and the target vehicle. The decision module determines the actual inter-vehicle time distance based on the actual inter-vehicle distance and the vehicle's driving speed, and compares the actual inter-vehicle time distance with the target inter-vehicle time distance.

[0175] If the actual inter-vehicle time distance is less than the target inter-vehicle time distance, it means that the vehicle is too close to the vehicle in front and there is a safety risk. The decision module will take measures to increase the inter-vehicle distance, such as by decelerating or slightly braking. If the actual inter-vehicle time distance is greater than the target inter-vehicle time distance, it means that the vehicle is far away from the vehicle in front. The decision module may allow the vehicle to accelerate slightly to approach the vehicle in front to optimize traffic flow and improve road traffic efficiency. In an emergency, the decision module will quickly apply braking force to avoid a collision.

[0176] The control method of the autonomous driving vehicle provided in the embodiment of the present application determines the target inter-vehicle time distance according to the mapping relationship between the preset vehicle speed range and the inter-vehicle time distance, thereby accurately adjusting the distance between the vehicle and the target vehicle in front. Through this precise distance control, the risk of rear-end collision can be effectively reduced, ensuring the safety of the vehicle under various driving conditions. At the same time, this optimized distance management strategy also improves the road's traffic capacity, making the traffic flow smoother.

[0177] In a possible implementation, if the vehicle's driving speed reaches the minimum driving speed preset in the E2E function, a collision will occur between the vehicle and the target vehicle, the vehicle is controlled to trigger the forward collision warning function or the automatic emergency braking function, and the user is prompted to intervene with the brake pedal through the HMI to exit the E2E function.

[0178] In this step, when the vehicle is following the target vehicle, the decision module will monitor and adjust the vehicle's driving parameters in real time to adapt to the dynamic changes of the target vehicle. This adaptive adjustment includes precise control of key parameters such as vehicle speed, acceleration and distance, aiming to ensure that the vehicle can smoothly and safely follow the target vehicle.

[0179] However, in certain complex or sudden traffic conditions, even if the vehicle's speed has been adjusted to the minimum speed preset in the E2E function, there may still be a risk of collision between the vehicle and the target vehicle.

[0180] To effectively deal with this potential danger, the decision module will respond quickly and take a series of preventive measures. First, the decision module will trigger the forward collision warning function, which will promptly remind the user of the impending danger through sound and light signals or vibration. This warning function is designed to buy precious reaction time for the user to take appropriate measures to avoid accidents.

[0181] If the risk of collision persists, the decision module will further activate the automatic emergency braking function. This function can apply braking force to the vehicle in a very short time, quickly reduce the speed of the vehicle, or even stop the vehicle completely, to minimize collision or reduce the damage caused by the collision. At the same time, the decision module will issue a clear prompt to the user through the HMI, suggesting that the user immediately intervene in the brake pedal and automatically exit the E2E function. This prompt is not only a reminder to the user, but also a transfer of system control, ensuring that in extreme cases, the user can take over the control of the vehicle in time and intervene manually.

[0182] In the control method of the autonomous driving vehicle provided in this embodiment, when the vehicle's driving speed has been adjusted to the minimum driving speed preset by the E2E function, but there is still a risk of collision with the target vehicle, the decision module will quickly take a series of safety measures. These measures are intended to provide multi-level security in a complex and changing traffic environment. Through such a design, not only the safety and reliability of the autonomous driving system are significantly improved, but also the user's trust and acceptance of autonomous driving technology are enhanced.

[0183] Figure 8 This is a schematic diagram of a control method for an autonomous driving vehicle provided in this application. Figure 8As shown, the framework of the control method of the autonomous driving vehicle may include: intelligent driving function activation 801, perception module 802, prediction module 803, decision module 804, path planning 805, control module 806, dynamic adjustment 807, HMI information display 808, manual selection of target vehicle 809, locking target vehicle 8010, and tracking driving 8011. Among them, the intelligent driving system based on E2E function includes modules such as perception, fusion, positioning, map, prediction, decision, and control.

[0184] When the intelligent driving function activation 801 is activated, the perception module 802 in the ADCU collects the road data and driving parameters of other vehicles within the preset range of the vehicle in real time, and transmits the road data and driving parameters of other vehicles to the prediction module 803 in real time, and displays them to the user in the form of HMI information display 808. The user manually selects the target vehicle 809 in the HMI according to the information displayed by the HMI and his own wishes. If the target vehicle is directly in front of the vehicle and adjacent to the vehicle, the prediction module 803 predicts the driving trajectory of the target vehicle based on the road data and driving parameters of other vehicles, and the decision module 804 calculates the acceleration, speed and other information of the vehicle based on the driving trajectory of the target vehicle, and controls the vehicle to follow the track. If the target vehicle is not directly in front of the vehicle and is adjacent, the prediction module 803 predicts the traffic efficiency of the traffic flow of different lanes and the driving trajectory of other vehicles according to the road data and the driving parameters of other vehicles. The decision module 804 determines the expected target angle, target acceleration and target speed of the steering wheel of the vehicle according to the traffic efficiency of the traffic flow of different lanes and the driving trajectory of other vehicles, and controls the vehicle to enter the rear of the target vehicle and be adjacent. If the selected target vehicle is directly in front of the vehicle and is adjacent, the vehicle directly selects the target vehicle to follow the track 8011. If the selected target vehicle is directly in front but not adjacent, the vehicle actively changes lanes to overtake under safe conditions until the target vehicle is directly in front of the vehicle, and then follows the track 8011. If the selected target vehicle is on the side and the adjacent lane line is a dotted line, the vehicle actively accelerates or decelerates, and changes lanes until the target vehicle is directly in front of the vehicle, and then follows the track 8011. If the target vehicle is on the side, but the adjacent lane line is a solid line, the HMI prompts that the solid line cannot be changed, and the lane change is in progress. The waiting time is 30s. If the lane change conditions are met within the timing period, the lane change is made. If the lane change conditions are not met within the timing period, the HMI prompts that the target vehicle lock function has timed out and is not activated. If the ego vehicle has not reached the rear of the target vehicle within 3 minutes of locking the target vehicle or the distance between the target vehicle and the ego vehicle exceeds 120m, the HMI prompts that the target vehicle lock has failed. At this time, the function is downgraded to the basic functions of E2E, such as adaptive cruise control and lane keeping assist.

[0185] In one possible implementation, the control method of an autonomous driving vehicle may specifically include target vehicle locking, autonomous vehicle control, tracking driving, human-computer interaction, safe driving, and traffic efficiency improvement.

[0186] Target vehicle locking refers to manually selecting a target vehicle 809 through the HMI according to the user's wishes. This process includes real-time monitoring and analysis of the speed, acceleration, position, driving direction and other parameters of the target vehicle by the perception module 802 and the control algorithm, and displaying a virtual model on the HMI.

[0187] Autonomously controlling a vehicle refers to autonomously controlling the steering, acceleration and deceleration of the vehicle to follow the locked target vehicle. This process includes the control module 806 intelligently controlling the vehicle power system, steering system and braking system to achieve smooth following of the vehicle.

[0188] Tracking driving means that when the vehicle is following a target vehicle, the perception module 802 and the intelligent algorithm are used to monitor and analyze the road information and the driving trajectory of the target vehicle in real time, so that the vehicle can accurately track the driving trajectory of the target vehicle.

[0189] Safe driving means that when the target vehicle to be followed is known to be relatively safe, the driving status of the target vehicle is monitored and analyzed in real time through the perception module 802 and the intelligent algorithm, and the driving strategy of the vehicle is adjusted according to the driving status of the target vehicle to improve the safety of the vehicle following the vehicle.

[0190] Human-computer interaction refers to real-time interaction with users through devices such as on-board displays and voice systems, adjusting the vehicle's driving strategy according to user needs and instructions, and enhancing the pleasure of intelligent driving through human-computer co-driving.

[0191] Traffic efficiency improvement refers to the real-time monitoring and analysis of road information and the driving status of the vehicle ahead through the perception module 802 and the intelligent algorithm in the case of multi-vehicle cooperative driving, and the adjustment of the driving strategy of the vehicle in different lanes according to the road information and the driving status of the vehicle ahead, so as to improve the traffic efficiency of vehicles on the road and reduce traffic congestion.

[0192] This application can achieve a more intelligent and personalized driving experience and enhance the driving pleasure of human-machine co-driving. In the intelligent driving domain control, the prediction module predicts the traffic efficiency of the vehicle in different lanes and displays it to the user in the form of HMI, so that the user can manually lock the target vehicle through the central control screen, and autonomously control the vehicle to change lanes or overtake, accelerate and decelerate to switch different lanes, and finally achieve the tracking and following driving state, which can improve the traffic efficiency of vehicles on the road and reduce traffic congestion. At the same time, this application can realize the intelligence and personalization of human-computer interaction. By integrating human driving experience and autonomous driving technology, a more intelligent and personalized driving experience can be achieved according to the driver's wishes and real-time road conditions. In addition, this application can achieve a more natural and intuitive interaction method. By locking the target vehicle according to the user's wishes, autonomously controlling the vehicle to change lanes or overtake, accelerate and decelerate, etc., the user's interaction with the autonomous driving system can be made more natural and intuitive, reducing misunderstandings and conflicts between the user and the autonomous driving system.

[0193] Fig. 9 A schematic diagram of a process for adjusting the time interval between vehicles provided in this application. Fig. 9 As shown, the process of adjusting the time headway between workshops may include:

[0194] S901: Follow the track.

[0195] In this step, the current vehicle has traveled to the lane where the target vehicle is located and is located in an adjacent position directly behind the target vehicle.

[0196] S902: The target vehicle accelerates and decelerates longitudinally.

[0197] In this step, during the vehicle tracking driving, based on the acquired driving parameters of the target vehicle, it can be determined whether the target vehicle is accelerating or decelerating longitudinally. If so, step S903 is executed.

[0198] S903: The vehicle actively accelerates or decelerates.

[0199] In this step, according to step S902, if it is determined that the target vehicle is accelerating or decelerating longitudinally, the self-vehicle will actively accelerate or decelerate, and dynamically adjust the distance between the self-vehicle and the target vehicle. It is worth noting that different vehicle speed segments have different vehicle headroom. Generally, the lower the speed of the self-vehicle, the shorter the distance between the two vehicles, and the higher the speed of the self-vehicle, the longer the distance between the two vehicles. In addition, the self-vehicle perception module senses the surrounding vehicles in real time. When there are other vehicles in the adjacent lanes on the left and right of the self-vehicle, the vehicle control module actively shortens the headroom between the self-vehicle and the target vehicle to prevent other vehicles in other lanes from changing lanes and affecting the traffic efficiency of the self-vehicle. When there are no other vehicles in the adjacent lanes on the left and right of the self-vehicle, the vehicle control module actively increases the headroom between the self-vehicle and the target vehicle to prevent the self-vehicle and the target vehicle from being too close and there is a risk of collision. It is worth noting that the maximum driving speed of the vehicle cannot exceed the maximum driving speed within the E2E function.

[0200] At the same time, it is worth noting that the present application provides a mapping relationship between a preset vehicle speed range and a preset vehicle headway. Fig. 9 As shown, if the speed of the vehicle is less than or equal to the preset first speed, the time headway 1 can be determined. If the speed of the vehicle is greater than the preset first speed and less than the preset second speed, the time headway 2 can be determined. Similarly, if the speed of the vehicle is greater than the preset (n-1)th speed and less than the preset nth speed, the time headway n can be determined. Wherein, n is a positive integer greater than or equal to 2, and the specific value of n is determined according to the type of vehicle, and this application does not make specific restrictions on this.

[0201] Fig.10 This is a schematic diagram of the control process of an autonomous driving vehicle following the track provided in this application. Fig.10 As shown, the autonomous driving vehicle tracking control process may include:

[0202] S1001: Follow the track.

[0203] In this step, the current vehicle has traveled to the lane where the target vehicle is located and is located in an adjacent position directly behind the target vehicle.

[0204] S1002: The target vehicle decelerates rapidly in the longitudinal direction.

[0205] In this step, during the vehicle tracking driving, if it is determined based on the driving parameters of the target vehicle that the target vehicle is rapidly decelerating longitudinally, step S1003 is executed.

[0206] S1003: The vehicle follows and decelerates.

[0207] In this step, if the target vehicle decelerates suddenly in the longitudinal direction, the own vehicle will follow and decelerate.

[0208] S1004: Is there a risk of collision?

[0209] In this step, if the vehicle decelerates to the minimum driving speed within the E2E function and there is still a risk of collision, step S1005 is executed, otherwise step S1001 is executed.

[0210] S1005: Forward collision warning function or automatic emergency braking function.

[0211] In this step, if the vehicle decelerates to the minimum driving speed within the E2E function and there is still a risk of collision, the vehicle will trigger the forward collision warning function or the automatic emergency braking function, and synchronize the HMI display to remind the user to actively intervene with the brake pedal and exit the E2E function.

[0212] Fig.11 A schematic diagram of a control flow of lateral lane change provided in this application. Fig.11 As shown, the control process of the lateral lane change may include:

[0213] S1101: Follow the track.

[0214] In this step, the current vehicle has traveled to the lane where the target vehicle is located and is located in an adjacent position directly behind the target vehicle.

[0215] S1102: The target vehicle changes lanes laterally.

[0216] In this step, during the vehicle tracking driving, if it is determined based on the driving parameters of the target vehicle that the target vehicle changes lanes laterally, step S1103 is executed.

[0217] S1103: The vehicle follows the path.

[0218] In this step, if the target vehicle changes lanes laterally, the ego vehicle will change lanes synchronously with the target vehicle and follow the track based on the prediction algorithm and the perception and recognition turn signal algorithm.

[0219] S1104: Trigger the lever lane change function.

[0220] In this step, the vehicle will trigger the lever lane change function to achieve vehicle tracking driving.

[0221] Fig.12 This is a flow chart of a control method for an autonomous driving vehicle provided in this application. Fig.12 As shown, the process of the control method of the automatic driving vehicle may include:

[0222] S1201: Follow the track.

[0223] In this step, the current vehicle has traveled to the lane where the target vehicle is located and is located in an adjacent position directly behind the target vehicle.

[0224] S1202: The target vehicle is lost or a new target enters.

[0225] In this step, during the vehicle tracking driving, if it is determined based on the driving parameters of the target vehicle that the target vehicle is lost, or it is detected that another vehicle has entered between the vehicle and the target vehicle, step S1203 is executed.

[0226] S1203: HMI prompts the user to take over.

[0227] In this step, if the target vehicle is lost or a new target is cut in, the HMI prompts the user to take over.

[0228] S1204: Exit the E2E function.

[0229] In this step, if the target vehicle is lost or a new target is cut in, the E2E function is exited while the HMI prompts the user to take over.

[0230] The control method of the autonomous driving vehicle provided in this application can effectively enhance the intelligent driving pleasure of human-machine co-driving, improve the traffic efficiency of vehicles on the road, and improve the safety of self-vehicle following vehicle driving. Specifically, the existing autonomous driving system has a single function and lacks a personalized driving experience in realizing human-machine co-driving. This application manually locks the target vehicle on the central control screen according to the user's wishes, and the intelligent driving domain control autonomously controls the vehicle to complete left and right lane changes or overtaking, acceleration and deceleration, and finally follows the locked target vehicle for tracking driving, thereby enhancing the intelligent driving pleasure of human-machine co-driving. The existing autonomous driving system has difficulties in handling multi-vehicle collaborative driving, especially in following vehicle driving on highways. If the driving state of the front vehicle is not good, the following vehicle driving safety of the rear vehicle will be affected. This application predicts the traffic efficiency of different lanes through the prediction module in the intelligent driving domain controller, and displays it to the user in the form of HMI, so that the user can switch different lanes by manually locking the target vehicle on the central control screen, and finally achieves the tracking and following vehicle driving state, which can improve the traffic efficiency of vehicles on the road and reduce traffic congestion. The target vehicle to be followed by the existing automatic driving system is mainly the target in front of the lane. The target vehicle is a random target without selectivity and cannot be switched according to the user's wishes. At this time, when the target vehicle's driving level is insufficient and there is an emergency brake, the self-vehicle is prone to collision risks. This application can improve the safety of the self-vehicle following the vehicle and reduce the occurrence of traffic accidents by locking the target vehicle to be a known relatively safe and experienced vehicle.

[0231] Fig.13 This is a schematic diagram of the structure of a control device for an autonomous driving vehicle according to an embodiment of the present application. Fig.13 As shown, the control device 1300 of the automatic driving vehicle includes:

[0232] The display module 1301 is used to display multiple vehicles traveling within a preset range of the vehicle on the human-machine interaction interface HMI based on the road data and driving parameters of other vehicles during the vehicle driving process;

[0233] The processing module 1302 is used to determine a target vehicle for tracking driving from a plurality of vehicles in response to a selection operation of a user;

[0234] The control module 1303 is used to control the vehicle to travel according to the driving trajectory of the target vehicle.

[0235] Optionally, the control module 1303 is further used for:

[0236] The vehicle is controlled to move to the lane where the target vehicle is located and to be in an adjacent position directly behind the target vehicle.

[0237] Optionally, the processing module 1302 is further configured to:

[0238] According to the driving parameters of the target vehicle, the driving parameters of the vehicle are adjusted to follow the driving trajectory of the target vehicle.

[0239] Optionally, the processing module 1302 is further configured to:

[0240] Determining at least one adjustment scheme from preset adjustment strategies according to the driving parameters of the target vehicle;

[0241] According to the adjustment plan, the driving parameters of the vehicle are adjusted.

[0242] Optionally, the processing module 1302 is further configured to:

[0243] If the driving parameters of the target vehicle indicate that the target vehicle is accelerating or decelerating, the vehicle is controlled to adjust the time distance between vehicles;

[0244] If the driving parameters of the target vehicle indicate that the target vehicle changes lanes laterally, the vehicle is controlled to change lanes laterally.

[0245] Optionally, the processing module 1302 is further configured to:

[0246] According to the mapping relationship between the preset vehicle speed range and the preset vehicle headway, a target vehicle headway corresponding to the vehicle's driving speed is obtained;

[0247] Adjust the distance between the vehicle and the target vehicle according to the vehicle's driving speed and the target vehicle-to-vehicle time interval.

[0248] Optionally, the control module 1303 is further used for:

[0249] If the vehicle's driving speed reaches the minimum driving speed preset in the end-to-end E2E function, a collision will occur between the vehicle and the target vehicle, then the vehicle will be controlled to trigger the forward collision warning function or the automatic emergency braking function, and the user will be prompted to intervene with the brake pedal through the HMI to exit the end-to-end E2E function.

[0250] Optionally, the processing module 1302 is further configured to:

[0251] Determine whether the vehicle is in the lane where the target vehicle is located and is in an adjacent position directly behind the target vehicle;

[0252] If the vehicle is not in the lane where the target vehicle is located, or the vehicle is not in an adjacent position directly behind the target vehicle, the traffic efficiency of the traffic flow in different lanes is determined based on the road data within a preset range;

[0253] Determine the vehicle's lane-changing trajectory based on traffic efficiency.

[0254] Optionally, the control module 1303 is further used for:

[0255] The vehicle is controlled to perform a lane change operation according to the lane change trajectory so that the vehicle drives to the lane where the target vehicle is located and is in an adjacent position directly behind the target vehicle.

[0256] Optionally, the display module 1301 is further used for:

[0257] If the vehicle has not completed the lane change operation at the end of the first preset time, or the vehicle has not achieved being in the same lane as the target vehicle and in an adjacent position directly behind the target vehicle at the end of the second preset time, or the distance between the vehicle and the target vehicle is greater than the preset distance at the end of the second preset time, the HMI pushes information indicating that the vehicle's tracking driving function has not been achieved, and the first preset time is less than the second preset time.

[0258] The control device for an autonomous driving vehicle provided in an embodiment of the present application can be used to execute a control method for an autonomous driving vehicle in any of the aforementioned method embodiments. The implementation principles and technical effects are similar and will not be described in detail here.

[0259] Fig.14 A schematic diagram of the structure of a driving controller provided for the application. Fig.14 As shown, the driving controller 1400 may specifically include a transceiver 1401, a processor 1402, and a memory 1403. The transceiver 1401 is used to implement data transmission with other systems in the vehicle, the memory 1403 stores computer-executable instructions, and the processor 1402 executes the computer-executable instructions stored in the memory 1403 to implement the control method of the autonomous driving vehicle in the above embodiment.

[0260] This embodiment provides a vehicle, including a vehicle body and Fig.14 Steering controller 1401 is shown.

[0261] This embodiment provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the control method of the autonomous driving vehicle in the above embodiment.

[0262] This embodiment also provides a computer program product, including a computer program, which, when executed by a processor, implements the control method of the autonomous driving vehicle provided in any one of the above embodiments.

[0263] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0264] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A control method for an autonomous driving vehicle, characterized in that: The method comprises: During the driving process of the vehicle, based on the road data and driving parameters of other vehicles, multiple vehicles traveling within a preset range of the vehicle are displayed on the human-machine interaction interface HMI; In response to a selection operation by a user, determining a target vehicle for tracking driving from the plurality of vehicles; The vehicle is controlled to travel according to the travel trajectory of the target vehicle.

2. The method according to claim 1, characterized in that The controlling the vehicle to travel according to the travel trajectory of the target vehicle comprises: Controlling the vehicle to drive to the lane where the target vehicle is located and to be in an adjacent position directly behind the target vehicle; According to the driving parameters of the target vehicle, the driving parameters of the vehicle are adjusted to follow the driving trajectory of the target vehicle.

3. The method according to claim 2, characterized in that The step of adjusting the driving parameters of the vehicle according to the driving parameters of the target vehicle includes: Determining at least one adjustment scheme from preset adjustment strategies according to the driving parameters of the target vehicle; According to the control scheme, a driving parameter of the vehicle is adjusted.

4. The method according to claim 3, characterized in that The adjustment strategy includes at least one of the following adjustment schemes: If the driving parameter of the target vehicle indicates that the target vehicle is accelerating or decelerating, controlling the vehicle to adjust the time headway between vehicles; If the driving parameter of the target vehicle indicates that the target vehicle changes lanes laterally, the vehicle is controlled to change lanes laterally.

5. The method according to claim 4, characterized in that The controlling the vehicle to adjust the time headway between vehicles comprises: According to the mapping relationship between the preset vehicle speed range and the preset vehicle headway, a target vehicle headway corresponding to the vehicle's driving speed is obtained; The distance between the vehicle and the target vehicle is adjusted according to the driving speed of the vehicle and the target time headway between the vehicles.

6. The method according to claim 5, characterized in that The method further comprises: If the driving speed of the vehicle is the minimum driving speed preset in the end-to-end E2E function, a collision will occur between the vehicle and the target vehicle, then the vehicle is controlled to trigger a forward collision warning function or an automatic emergency braking function, and the user is prompted to intervene with the brake pedal through the HMI to exit the end-to-end E2E function.

7. The method according to claim 2, characterized in that The controlling the vehicle to drive to the lane where the target vehicle is located and to be located at an adjacent position directly behind the target vehicle includes: Determining whether the vehicle is in the lane where the target vehicle is located and is in an adjacent position directly behind the target vehicle; If the vehicle is not in the lane where the target vehicle is located, or the vehicle is not in an adjacent position directly behind the target vehicle, the traffic efficiency of the traffic flow in different lanes is determined according to the road data within a preset range; determining a lane changing trajectory of the vehicle according to the traffic efficiency; The vehicle is controlled to perform a lane change operation according to the lane change trajectory, so that the vehicle travels to the lane where the target vehicle is located and is located at an adjacent position directly behind the target vehicle.

8. The method according to claim 7, characterized in that The method further comprises: If the vehicle has not completed the lane change operation at the end of the first preset time, or the vehicle has not achieved being in the same lane as the target vehicle and in an adjacent position directly behind the target vehicle at the end of the second preset time, or the distance between the vehicle and the target vehicle is greater than a preset distance at the end of the second preset time, the HMI pushes information indicating that the tracking driving function of the vehicle is not achieved, and the first preset time is less than the second preset time.

9. A control device for an autonomous driving vehicle, characterized in that: include: A display module is used to display multiple vehicles traveling within a preset range of the vehicle on a human-machine interaction interface HMI based on road data and driving parameters of other vehicles during the vehicle's driving process; A processing module, configured to determine a target vehicle for tracking driving from the plurality of vehicles in response to a selection operation by a user; A control module is used to control the vehicle to travel according to the driving trajectory of the target vehicle.

10. A driving controller, characterized in that: include: A transceiver, a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the control method of the autonomous driving vehicle as described in any one of claims 1 to 8.

11. A vehicle, characterized in that: include: A vehicle body and a driving controller as claimed in claim 10.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the control method of the autonomous driving vehicle as described in any one of claims 1 to 8.

13. A computer program product, characterized in that It includes a computer program, which, when executed by a processor, is used to implement the control method of the automatic driving vehicle as described in any one of claims 1 to 8.

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