Vehicle lane changing control method and device, electronic equipment, storage medium and vehicle

Abstract

The application discloses a vehicle lane changing control method and device, electronic equipment, storage medium and vehicle, and relates to the technical field of vehicle automatic driving. The first driving parameter, the second driving parameter and the road information of the vehicle are acquired, the second driving parameter comprises a plurality of driving parameters of each vehicle in a plurality of surrounding vehicles, the planning lane changing trajectory, the self-vehicle expected acceleration duration and the self-vehicle expected acceleration of the vehicle are determined according to the first driving parameter, the second driving parameter and the road information, the planning lane changing trajectory and the self-vehicle expected acceleration are optimized by a nonlinear programming method, the target lane changing trajectory and the target acceleration are obtained, the target lane changing trajectory and the target acceleration-time function are smooth curves, the vehicle is controlled to start acceleration, the vehicle drives from the original lane to the target lane according to the target lane changing trajectory, and since the lane changing trajectory and the acceleration-time function are both smooth curves, the acceleration of the vehicle is smoother, and the comfort of passengers is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle automatic driving, in particular to a vehicle lane changing control method and device, electronic equipment, storage medium and vehicle. BACKGROUND

[0002] At present, for the point-to-point automatic driving task of high-speed structured road, the full-process automatic driving from point to point can be realized. However, in addition to requiring the car to have the ability of adaptive cruise control, there are higher requirements for merging, converging, avoiding obstacles and the like, so as to complete the driving of the whole stage. Typical merging and converging scenarios include: vehicles entering the auxiliary road from the main road, or entering the main road from the auxiliary road; in the highway, vehicles enter the highway from the ramp, or vehicles drive on the high-grade highway with multiple lanes above and below, etc. In the scenarios of merging, converging, avoiding obstacles and the like, lane changing is the key to evaluating the level of automatic driving.

[0003] The existing lane changing scheme uses a larger acceleration when overtaking, and the constant acceleration will cause the vehicle to appear obvious acceleration oscillation, thereby causing poor passenger comfort.

[0004] Therefore, the existing lane changing method still needs to be improved in driving comfort. SUMMARY

[0005] One of the purposes of the present application is to provide a vehicle lane changing control method to solve the problem of acceleration oscillation when changing lanes, resulting in poor user experience when driving the vehicle; the second purpose is to provide a vehicle lane changing control device; the third purpose is to provide an electronic equipment; the fourth purpose is to provide a readable storage medium, and the fifth purpose is to provide a vehicle.

[0006] In order to achieve the above purposes, the technical scheme adopted by the present application is as follows:

[0007] A vehicle lane changing control method, the method comprising:

[0008] obtaining a first driving parameter, a second driving parameter and road information of a vehicle, the second driving parameter comprising a plurality of driving parameters of each vehicle in a plurality of surrounding vehicles;

[0009] determining a planned lane changing trajectory, a self-vehicle predicted acceleration duration and a self-vehicle predicted acceleration of the vehicle according to the first driving parameter, the second driving parameter and the road information;

[0010] optimizing the planned lane changing trajectory and the self-vehicle predicted acceleration by a nonlinear programming method to obtain a target lane changing trajectory and a target acceleration, the target lane changing trajectory and the target acceleration-time function being a smooth curve;

[0011] Within the expected acceleration period of the vehicle, the vehicle is controlled to accelerate according to the target acceleration, so that the vehicle travels from the original lane to the target lane according to the target lane-changing trajectory.

[0012] Based on the aforementioned technical means, by acquiring the vehicle's first driving parameters, second driving parameters, and road information, and then determining the vehicle's planned lane-changing trajectory, expected acceleration time, and expected acceleration based on these parameters, a nonlinear programming method is used to optimize the planned lane-changing trajectory and expected acceleration to obtain the target lane-changing trajectory and target acceleration. Finally, within the expected acceleration time, the vehicle is controlled to accelerate based on the target acceleration, allowing it to travel from the original lane to the target lane according to the target lane-changing trajectory. This approach achieves a higher probability of successful lane changes to ensure driving safety. Furthermore, it allows for appropriate adjustment of the lane-changing trajectory and acceleration. Since both the lane-changing trajectory and the acceleration-time function are smooth curves, meaning the target lane-changing trajectory and target acceleration have gentler slopes, smoother transitions, and no sharp points or abrupt changes, the vehicle's acceleration becomes smoother, avoiding sudden acceleration or deceleration and thus improving passenger comfort.

[0013] Furthermore, determining the vehicle's lane-changing trajectory, estimated acceleration time, and estimated acceleration based on the first driving parameters, the second driving parameters, and the road information includes:

[0014] Input the first driving parameter and the second driving parameter into the trajectory prediction model to obtain the vehicle's motion state and the motion state of multiple surrounding vehicles in the future first time period. The motion state of the multiple surrounding vehicles includes the motion state of each of the multiple surrounding vehicles.

[0015] The planned lane-changing trajectory is determined based on the vehicle's motion state, the motion states of the multiple surrounding vehicles, and the road information.

[0016] Based on the planned lane-changing trajectory, the first driving parameters, and the second driving parameters, the estimated acceleration time of the vehicle is determined;

[0017] The expected acceleration of the vehicle is determined based on the planned lane-changing trajectory, the first driving parameters, and the expected acceleration time of the vehicle.

[0018] Furthermore, determining the lane-changing trajectory based on the vehicle's motion state, the motion states of the multiple surrounding vehicles, and the road information includes:

[0019] Based on the road information, multiple lane-changing spaces are determined in the adjacent lanes of the original lane, and the lane-changing space is a gap in the adjacent lane that can be used for lane changing;

[0020] The target lane change space is determined from the multiple lane change spaces by considering the vehicle's motion state and the motion states of the surrounding vehicles.

[0021] The planned lane-changing trajectory is determined based on the vehicle's motion state and the target lane-changing space.

[0022] Furthermore, the second driving parameters include the driving parameters of the vehicle directly in front, the vehicle to the side front, and the vehicle to the side rear. Determining the vehicle's expected acceleration time based on the planned lane-changing trajectory, the first driving parameters, and the second driving parameters includes:

[0023] Based on the planned lane-changing trajectory, the first driving parameters, and the driving parameters of the vehicle directly ahead, the maximum lane-changing time is determined, where the maximum lane-changing time is the maximum time period during which the vehicle can accelerate.

[0024] The vehicle's motion state and the driving parameters of the vehicle to the side and in front are input into the lane-changing game model to obtain the first lane-changing time.

[0025] The vehicle's motion state and the driving parameters of the vehicles to the side and rear are input into the lane-changing game model to obtain the second lane-changing time.

[0026] If the first lane change time is less than or equal to the maximum lane change time, and the second lane change time is less than or equal to the maximum lane change time, the larger of the first lane change time and the second lane change time shall be used as the vehicle's expected acceleration time.

[0027] Furthermore, the optimization of the planned lane-changing trajectory and the predicted acceleration of the vehicle using a nonlinear programming method to obtain the target lane-changing trajectory and target acceleration includes:

[0028] The driving trajectories of the multiple surrounding vehicles are projected onto the displacement-time graph to obtain position constraints. The driving trajectories of the multiple surrounding vehicles include the driving trajectory of each of the multiple surrounding vehicles. The driving trajectories of the multiple surrounding vehicles are obtained by inputting the second driving parameters into the trajectory prediction model.

[0029] Determine the speed constraints based on the preset vehicle speed range;

[0030] The planned lane-changing trajectory is optimized using the objective cost function, the position constraint, and the velocity constraint to obtain the target lane-changing trajectory and the target acceleration.

[0031] Furthermore, the step of projecting the driving trajectories of the multiple surrounding vehicles onto a displacement-time graph to obtain positional constraints includes:

[0032] The upper boundary of the position constraint is obtained by projecting the driving trajectory of the vehicle directly in front during the first time period and the driving trajectory of the vehicle in front inside the expected acceleration time of the vehicle onto the displacement-time graph.

[0033] By projecting the driving trajectory of the vehicle behind the vehicle within the expected acceleration time of the self-vehicle onto the displacement-time graph, the lower boundary of the position constraint is obtained.

[0034] Furthermore, the method also includes:

[0035] When the vehicle starts accelerating, the vehicle's lane-changing time, current first driving parameters, and current second driving parameters are obtained.

[0036] The current first driving parameters and the current second driving parameters are input into the trajectory prediction model to obtain the first distance between the vehicle and the vehicle directly in front, and the second distance between the vehicle and the target vehicle directly behind in the future second time period. The future second time period is the difference between the vehicle's expected acceleration time and the start-up lane change time.

[0037] If the vehicle meets the lane change and reversal conditions, the vehicle is controlled to abandon the lane change and return to the original lane. The lane change and reversal conditions include: the first distance is less than the first safe distance, or the second distance is less than the second safe distance.

[0038] Furthermore, the method also includes:

[0039] If the third distance between the vehicle and a target vehicle to the side and rear in the original lane is less than the third safe distance, or if there is a vehicle that wants to change lanes to the original lane, the vehicle is controlled to travel in the target lane, and a prompt message is output to alert the driver of the potential collision risk.

[0040] A vehicle lane-changing device, the device comprising:

[0041] The acquisition module is used to acquire the vehicle's first driving parameters, second driving parameters, and road information. The second driving parameters include multiple driving parameters for each of the multiple surrounding vehicles.

[0042] The determination module is used to determine the planned lane-changing trajectory, the expected acceleration time, and the expected acceleration of the vehicle based on the first driving parameters, the second driving parameters, and the road information.

[0043] The calculation module is used to optimize the planned lane-changing trajectory and the predicted acceleration of the vehicle using a nonlinear programming method to obtain the target lane-changing trajectory and the target acceleration. The target lane-changing trajectory and the target acceleration-time function are smooth curves.

[0044] The control module is used to control the vehicle to start acceleration according to the target acceleration within the expected acceleration time of the vehicle, so that the vehicle travels from the original lane to the target lane according to the target lane change trajectory.

[0045] An electronic device includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the vehicle lane change control method described above.

[0046] A readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the vehicle lane-changing control method described above.

[0047] A vehicle includes a vehicle lane change control device as described above.

[0048] The beneficial effects of this invention are:

[0049] In this embodiment, by acquiring the vehicle's first driving parameters, second driving parameters, and road information (the second driving parameters include multiple driving parameters for each of the surrounding vehicles), and then determining the vehicle's planned lane-changing trajectory, expected acceleration time, and expected acceleration based on the first driving parameters, second driving parameters, and road information, the vehicle's planned lane-changing trajectory, expected acceleration time, and expected acceleration are determined. Then, a nonlinear programming method is used to optimize the planned lane-changing trajectory and expected acceleration to obtain the target lane-changing trajectory and target acceleration. Finally, within the expected acceleration time, the vehicle is controlled to accelerate based on the target acceleration, allowing the vehicle to travel from the original lane to the target lane according to the target lane-changing trajectory. This achieves a higher probability of successful lane changes to ensure driving safety. Furthermore, it allows for appropriate adjustment of the lane-changing trajectory and acceleration. Since both the lane-changing trajectory and the acceleration-time function are smooth curves, meaning the target lane-changing trajectory and target acceleration have gentler slopes, smoother transitions, no sharp points, and no abrupt changes, the vehicle's acceleration is smoother, avoiding sudden acceleration or deceleration and thus improving passenger comfort. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the steps of a vehicle lane-changing control method provided in an embodiment of this application;

[0051] Figure 2 This is a flowchart illustrating the specific steps of vehicle lane-changing control provided in an embodiment of this application;

[0052] Figure 3A This is a schematic diagram of a lane-changing space provided in an embodiment of this application;

[0053] Figure 3BThis is a schematic diagram of another lane-changing space provided in an embodiment of this application;

[0054] Figure 4 This is a flowchart illustrating the specific steps of vehicle lane-changing control provided in an embodiment of this application;

[0055] Figure 5A This is a schematic diagram of the lane-changing trajectory of a vehicle provided in an embodiment of this application;

[0056] Figure 5B This is a schematic diagram of the vehicle acceleration provided in an embodiment of this application;

[0057] Figure 6 This is a block diagram of a vehicle lane-changing control device provided in an embodiment of this application;

[0058] Figure 7 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0059] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0060] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0061] During driving, drivers may intend to change lanes (hereinafter referred to as "lane changing") for various reasons, such as pursuing higher driving efficiency or being constrained by road markings. Since lane changing involves the interaction of vehicles in multiple lanes, improper planning can negatively impact traffic efficiency and, in severe cases, even lead to traffic accidents. Generally, the lane changing process can be divided into three stages: intention generation, execution, and adjustment. The intention generation stage aims to create a lane change opportunity, selecting a target lane and finding a safe space to change lanes. If no suitable space is found, the lane change is abandoned. The execution stage initiates acceleration to complete the lane change. During this stage, trajectory planning is performed in real-time based on the movement of surrounding vehicles, and the planned trajectory is optimized. The adjustment stage determines whether to return to the original lane. During this stage, the vehicle should dynamically adjust its speed to follow the vehicle in front in the target lane.

[0062] In summary, both the intention generation and execution phases require reasonable judgment of the motion states of multiple surrounding vehicles to ensure the safety requirements of lane-changing behavior. In existing technologies, the large and constant acceleration used during overtaking leads to significant acceleration oscillations in the vehicle, resulting in poor passenger comfort. Furthermore, existing technologies do not consider scenarios where vehicles return to their original lane after initiating a lane change, nor do they address scenarios where vehicles in other lanes are simultaneously changing lanes to the target lane, prohibiting them from returning to their original lane. This could potentially cause traffic accidents.

[0063] The vehicle lane-changing control method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0064] Figure 1 This is a flowchart illustrating the steps of a vehicle lane-changing control method provided in an embodiment of this application, as follows: Figure 1 As shown, the method may include:

[0065] Step 101: Obtain the vehicle's first driving parameters, second driving parameters, and road information.

[0066] In this embodiment of the application, the second driving parameter includes multiple driving parameters for each of the multiple surrounding vehicles.

[0067] Optionally, the multiple surrounding vehicles may include: vehicles directly in front, vehicles directly behind, vehicles to the side front, and vehicles to the side rear; the driving parameters may include: position, speed, acceleration, etc., wherein the acceleration may include lateral acceleration and / or longitudinal acceleration, and the acceleration has magnitude and direction; the second driving parameters may include: the driving parameters of the vehicles directly in front, the vehicles directly behind, the vehicles to the side front, and the vehicles to the side rear, that is, the second driving parameters may include: the position, speed, acceleration, etc. of the vehicles directly in front, the position, speed, acceleration, etc. of the vehicles directly behind, the position, speed, acceleration, etc. of the vehicles to the side front, and the position, speed, acceleration, etc. of the vehicles to the side rear.

[0068] It is understood that the embodiments of this application can collect second driving parameters in real time through vehicle-mounted sensors. The second driving parameters are dynamic and can reflect the speed changes of multiple surrounding vehicles and their distance from the vehicle, which facilitates subsequent prediction of the future motion status of multiple surrounding vehicles and changes in their distance from the vehicle.

[0069] In some embodiments, before step 101, the above-mentioned vehicle lane change control method may further include: activating the vehicle's lane change creation mode when the triggering conditions are met, wherein the lane change creation mode may include: lane change with turn signal, lane change with voice request, automatic overtaking lane change, lane change on / off ramps, lane change during merging, lane change to avoid obstacles, etc.

[0070] Step 102: Based on the first driving parameters, the second driving parameters, and road information, determine the vehicle's planned lane-changing trajectory, the vehicle's expected acceleration time, and the vehicle's expected acceleration.

[0071] In this embodiment, road information may include lane line information, which includes the current lane, the left adjacent lane, and the right adjacent lane. Lane line information may also include lane line type, such as dashed line, solid line, dashed line changing to solid line, solid line changing to dashed line, etc. The current lane refers to the lane the vehicle is currently in, also known as the "original lane." The target lane refers to the lane the vehicle wants to change lanes to reach, and the target lane may be the left adjacent lane or the right adjacent lane of the current lane.

[0072] In some embodiments, road information may also include obstacle information, which includes the location and size of the obstacles.

[0073] For example, in step 102, road information can be identified by the vehicle's current location using the onboard vision sensor; or in step 102, road information can be obtained by sending a road information acquisition request to the cloud server and processing the road information request returned by the cloud server.

[0074] In some embodiments, the expected acceleration time and expected acceleration of the vehicle can be comfort calibration parameters obtained after multiple lane change tests, with different coefficients corresponding to different lane change creation modes. For example, in special scenarios such as merging, obstacle avoidance, and exiting ramps, the lane change creation completion time is limited to 4 seconds (s), the minimum deceleration coefficient is calibrated to -3, and the maximum acceleration coefficient is calibrated to +0.8.

[0075] Step 103: Optimize the planned lane-changing trajectory and the vehicle's expected acceleration using a nonlinear programming method to obtain the target lane-changing trajectory and target acceleration.

[0076] Among them, the target lane-changing trajectory and the target acceleration-time function are smooth curves.

[0077] In some embodiments, time-dependent polynomial equations can be used to establish a lane-changing trajectory optimization model, thereby ensuring that the lane-changing trajectory and its derivative function (velocity, acceleration) are continuous and smooth.

[0078] Understandably, nonlinear programming is a method for solving optimization problems where one or more nonlinear functions are present in the objective function or constraints. Quadratic programming is a special type of mathematical programming problem within nonlinear programming, where the objective function is a quadratic function and the constraints are linear; it is relatively easy to solve.

[0079] For example, when solving for the target lane-changing trajectory and target acceleration, an objective function is determined based on a preset optimization objective. Then, based on the first driving parameters and the target driving parameters, multiple constraints need to be followed when solving for each parameter in the cost function. These constraints can be linear or nonlinear, thus obtaining a lane-changing trajectory optimization model based on quadratic programming. Inputting the planned lane-changing trajectory and the vehicle's predicted acceleration into this model yields the output target lane-changing trajectory and target acceleration. Due to the characteristics of quadratic programming, the target lane-changing trajectory and target acceleration-time function are smooth curves with gentler slopes. Furthermore, the curves of the target lane-changing trajectory and target acceleration exhibit smooth transitions without sharp points or abrupt changes. This results in smoother vehicle acceleration, avoiding sudden acceleration or deceleration, thereby improving passenger comfort.

[0080] In some embodiments, step 103 may include: substituting the constraints into a quadratic programming (QP) solver to optimize the objective function and obtain the optimization result, thereby effectively improving the smoothness of the vehicle when changing lanes.

[0081] Step 104: Within the expected acceleration time of the vehicle, control the vehicle to start acceleration according to the target acceleration, so that the vehicle travels from the original lane to the target lane according to the target lane change trajectory.

[0082] In some embodiments, before step 104, the vehicle lane-changing control method further includes: if the vehicle has not started accelerating, determining whether the vehicle meets the lane-changing suppression conditions based on the current first driving parameters and the current second driving parameters; if the vehicle meets the lane-changing suppression conditions, controlling the vehicle to abandon the lane change; if the vehicle does not meet the lane-changing suppression conditions, controlling the vehicle to start accelerating, so that the vehicle travels from the original lane to the target lane according to the target lane-changing trajectory. The lane-changing suppression conditions may include: the distance between the vehicle and the vehicle to its side or rear is less than the minimum safe distance, or the speed difference between the vehicle and the vehicle to its side or rear is less than the minimum safe speed. Optionally, the minimum safe distance and the minimum safe speed are determined based on the vehicle's speed and the relative distance between the vehicle and the vehicle directly in front.

[0083] In summary, in this embodiment, by acquiring the vehicle's first driving parameters, second driving parameters, and road information (the second driving parameters include multiple driving parameters for each of the surrounding vehicles), and then determining the vehicle's planned lane-changing trajectory, expected acceleration time, and expected acceleration based on the first driving parameters, second driving parameters, and road information, the planned lane-changing trajectory, expected acceleration time, and expected acceleration are determined. Then, a nonlinear programming method is used to optimize the planned lane-changing trajectory and expected acceleration to obtain the target lane-changing trajectory and target acceleration. Finally, within the expected acceleration time, the vehicle is controlled to accelerate based on the target acceleration. This allows the vehicle to travel from the original lane to the target lane according to the target lane-changing trajectory. On the one hand, this achieves a higher probability of successful lane changing to ensure driving safety. On the other hand, it allows for appropriate adjustment of the lane-changing trajectory and acceleration. Since both the lane-changing trajectory and the acceleration-time function are smooth curves, meaning the target lane-changing trajectory and target acceleration have a gentler slope, smoother transition, no sharp points, and no abrupt changes, the vehicle's acceleration is smoother, avoiding sudden acceleration or deceleration, thereby improving passenger comfort.

[0084] In some embodiments, see Figure 2 Step 102 may include the following steps.

[0085] Step 201: Input the first driving parameter and the second driving parameter into the trajectory prediction model to obtain the vehicle's motion state and the motion state of multiple surrounding vehicles in the first time period in the future.

[0086] In this embodiment of the application, the motion state of the multiple surrounding vehicles includes the motion state of each of the multiple surrounding vehicles.

[0087] Optionally, the motion state may include acceleration and trajectory.

[0088] In this embodiment, the trajectory prediction model can be a neural network model, which is trained using real traffic data to obtain a trained trajectory prediction model.

[0089] For example, a trajectory prediction model can be a mathematical model or algorithm that can be used to describe and predict the behavior of a vehicle following the vehicle in front on a road, such as a safe distance model; a neural network model can be a machine learning model based on artificial neural networks that can handle complex traffic situations, such as a Long Short-Term Memory (LSTM) neural network; real traffic data can be data collected in the actual road traffic environment, such as information on vehicle speed, acceleration, location, and time.

[0090] It is understood that the embodiments of this application can establish a vehicle trajectory prediction model by combining a large amount of real traffic data with a neural network, which can predict the dynamic behavior of vehicles on the road. The first driving parameter and the second driving parameter are input into the trajectory prediction model to predict the acceleration of multiple surrounding vehicles, thereby providing the driver with more accurate and timely driving assistance information, and thus the vehicle's motion state and the motion state of multiple surrounding vehicles in the first time period in the future.

[0091] For example, an LSTM neural network builds a trajectory prediction model for a vehicle, with input features including the vehicle's speed of 30 m / s, the relative speed between the vehicle and the vehicle in front of it of 10 m / s, and the relative acceleration between the vehicle and the vehicle in front of it of 5 m / s². 2 The output is the acceleration of the vehicle and the vehicle directly in front within the next 8 seconds. Optionally, the trajectory prediction model can also output the distance between the vehicle and the vehicle directly in front within the next 8 seconds, the vehicle's trajectory, the vehicle's trajectory, or other driving parameters. This embodiment of the application does not limit this.

[0092] Step 202: Determine the planned lane-changing trajectory based on the vehicle's motion status, the motion status of multiple surrounding vehicles, and road information.

[0093] In some embodiments, step 1022 may include the following steps:

[0094] Based on road information, determine multiple lane-changing spaces in the adjacent lanes of the original lane. The lane-changing space is the gap in the adjacent lane that can be used for lane changing.

[0095] The target lane change space is determined from multiple lane change spaces by considering the vehicle's own motion status and the motion status of multiple surrounding vehicles.

[0096] The planned lane-changing trajectory is determined based on the vehicle's motion status and the target lane-changing space.

[0097] In this embodiment of the application, the lane-changing trajectory can be calculated based on the target lane-changing space, the vehicle's motion state, and the motion states of multiple surrounding vehicles to create a driving trajectory that safely transitions from the current lane to the target lane.

[0098] For example, see Figure 3A and Figure 3B The lane-changing spaces in this application embodiment can be diverse. When the current vehicle needs to change lanes to an adjacent lane, and the target lane is the left lane, multiple lane-changing spaces are available, such as... Figure 3A As shown, when the target lane is the right lane, there are multiple lane-changing spaces as follows: Figure 3B As shown.

[0099] In one possible implementation, determining the target lane-changing space from multiple lane-changing spaces by considering the vehicle's motion state and the motion states of multiple surrounding vehicles may include: establishing a cost calculation function to predict the real-time state of the vehicle at a preset acceleration or preset deceleration within a preset lane-changing time period; generating constraints based on the motion states of multiple surrounding vehicles; scoring the multiple lane-changing spaces based on the cost calculation function and the constraints; and determining the lane-changing space with the highest score among the multiple lane-changing spaces as the target lane-changing space.

[0100] For example, the specific method for establishing the cost calculation function is as follows: obtain the preset acceleration unit and cost calculation function formula;

[0101] Initialize the cost calculation function formula corresponding to each acceleration unit; set a preset lane-changing time period according to the trajectory prediction model to obtain the speed and position state of the vehicle directly in front; use a preset evaluation function to determine whether the vehicle and the vehicle directly in front meet the safe distance to support lane changing at every moment during the lane-changing time period, and obtain the first score value of multiple lane-changing spaces; similarly, use the evaluation function to calculate the second score value of the vehicle to the side and the third score value of the vehicle to the side and rear; take the lane-changing space with the highest score as the target lane-changing space.

[0102] In one possible implementation, before determining the planned lane-changing trajectory based on the vehicle's motion state and the target lane-changing space, the vehicle lane-changing control method may further include: determining whether the vehicle's lane-changing sign meets the activation conditions; if the lane-changing sign meets the activation conditions, determining the planned lane-changing trajectory based on the vehicle's motion state and the target lane-changing space, and recording the acceleration time; if the lane-changing sign does not meet the activation conditions, abandoning the lane change. Optionally, the activation conditions for the lane-changing sign include: within a preset time period, a second score value continuously greater than or equal to a preset second scoring threshold, and a third score value continuously greater than or equal to a preset third scoring threshold.

[0103] In another possible implementation, determining the target lane change space from multiple lane change spaces by considering the vehicle's motion state and the motion states of multiple surrounding vehicles may further include: calculating the optimal lane change speed for each lane change space, which is the speed with the minimum lane change cost; determining the evaluation score for each lane change space based on the optimal lane change speed, the available acceleration time, and a preset weighting formula; and selecting the lane change space with the highest score as the target lane change space.

[0104] In one possible implementation, determining the planned lane-changing trajectory based on the vehicle's motion state and the target lane-changing space may include: sending a lane-changing request to a cloud server, the request including the vehicle's motion state and the target lane-changing space; and receiving the planned lane-changing trajectory returned by the cloud server. Optionally, the cloud server may be the server corresponding to the navigation application software or the map application software.

[0105] In another possible implementation, the above-mentioned vehicle lane change control method may further include: if the number of determined lane change spaces is one, using that lane change space as the target lane change space.

[0106] In some embodiments, the above-described vehicle lane-changing control method may further include: exiting the lane-changing mode when no lane-changing space can be determined in the adjacent lane. For example, in congested scenarios, before acceleration is initiated, it can be determined whether to exit the lane-creating mode based on whether the lane-changing space meets the minimum safe distance, thus avoiding overall following unevenness caused by acceleration switching between creating and straight-ahead driving.

[0107] By employing the aforementioned technical means, the optimal target lane-changing space is determined based on the predicted motion state of the vehicle and the motion states of multiple surrounding vehicles. This accurately assesses whether there is a collision risk during the lane-changing process, improving driving safety. Furthermore, the current vehicle's lane-changing trajectory is planned based on the target lane-changing space, avoiding unnecessary waiting and tentative lane changes. This significantly improves lane-changing efficiency and road capacity, ensuring a smooth lane-changing process and enhancing passenger comfort.

[0108] Step 203: Determine the estimated acceleration time of the vehicle based on the planned lane-changing trajectory, the first driving parameter, and the second driving parameter.

[0109] In some embodiments, the second driving parameters include the driving parameters of the vehicle directly in front, the driving parameters of the vehicle to the side front, and the driving parameters of the vehicle to the side rear. Step 203 may include the following steps:

[0110] Based on the planned lane-changing trajectory, the initial driving parameters, and the driving parameters of the vehicle directly ahead, the maximum lane-changing time is determined. The maximum lane-changing time is the maximum time period during which the vehicle can accelerate.

[0111] The vehicle's motion state and the driving parameters of the vehicles to the side and in front are input into the lane-changing game model to obtain the first lane-changing time.

[0112] The vehicle's motion state and the driving parameters of vehicles to the side and rear are input into the lane-changing game model to obtain the second lane-changing time.

[0113] If the first lane change time is less than or equal to the maximum lane change time, and the second lane change time is less than or equal to the maximum lane change time, the larger of the first and second lane change times will be used as the vehicle's expected acceleration time.

[0114] In one possible implementation, determining the limit lane change duration based on the planned lane change trajectory, the first driving parameters, and the driving parameters of the vehicle directly ahead may include: obtaining a preset creation acceleration time; within this creation acceleration time, performing a limited number of loops under the constraint of confirming a suitable lane change, gradually increasing the creation acceleration time until the limit lane change duration is found.

[0115] In this embodiment of the application, the lane-changing game model can be a neural network model, which is trained using real traffic data to obtain a trained lane-changing game model.

[0116] For example, a lane-changing game model can be a mathematical model or algorithm that can be used to describe and predict whether a vehicle's lane-changing behavior on the road can meet the safe distance requirement, such as the Bayesian Stackelberg Game model; the optimal solution obtained by solving the payoff function of the Bayesian Stackelberg Game model is either the first lane-changing time or the second lane-changing time.

[0117] Understandably, the maximum lane change time is the duration required to maintain a safe distance between the vehicle and the vehicle directly in front; the first lane change time is the duration required to maintain a safe distance between the vehicle and the vehicle to the side; and the second lane change time is the duration required to maintain a safe distance between the vehicle and the vehicle to the side and behind. When both the first and second lane change times are less than or equal to the maximum lane change time, it indicates that the vehicle maintains safe distances from the vehicles directly in front, to the side, and to the side and behind, and there is no risk of collision. To ensure the safety of lane changes, the larger of the first and second lane change times is used as the vehicle's expected acceleration time.

[0118] In some embodiments, the vehicle lane change control model described above may further include: abandoning lane change when the first lane change duration is greater than the maximum lane change duration, or the second lane change duration is greater than the maximum lane change duration.

[0119] By incorporating game theory into the lane-changing trajectory planning method for autonomous vehicles using the aforementioned technical means, autonomous vehicles can determine whether to continue changing lanes based on their own and multiple surrounding vehicles' motion states. This effectively considers the impact of multiple targets during the lane-changing process, reduces the possibility of vehicle collisions, and improves lane-changing safety.

[0120] Step 204: Determine the vehicle's expected acceleration based on the planned lane-changing trajectory, the first driving parameters, and the vehicle's expected acceleration time.

[0121] In some embodiments, step 204 may include: sending a lane-change request to a cloud server, the lane-change request including a planned lane-change trajectory, first driving parameters, and the vehicle's expected acceleration time; and receiving the vehicle's expected acceleration returned by the cloud server. Optionally, the cloud server may be a server corresponding to a navigation application or a map application.

[0122] In summary, the embodiments of this application predict the motion state of the vehicle and the motion state of multiple surrounding vehicles based on the trajectory prediction model, then combine road information to determine the planned lane-changing trajectory, and finally determine the expected acceleration time and expected acceleration of the vehicle based on the obtained planned lane-changing trajectory. This can improve the consistency of autonomous driving decisions and the smoothness of control.

[0123] In some embodiments, see Figure 4 Step 103 may include the following steps.

[0124] Step 301: Project the driving trajectories of multiple surrounding vehicles onto the displacement-time graph to obtain position constraints.

[0125] In this embodiment of the application, the driving trajectory of the multiple surrounding vehicles includes the driving trajectory of each of the multiple surrounding vehicles.

[0126] In this embodiment of the application, the driving trajectories of multiple surrounding vehicles are obtained by inputting the second driving parameters into the trajectory prediction model.

[0127] In this embodiment of the application, the method of obtaining the driving trajectories of multiple surrounding vehicles is similar to step 201, and will not be repeated here.

[0128] In some embodiments, step 301 may include: projecting the driving trajectory of the vehicle directly in front during the first time period and the driving trajectory of the vehicle in front inside the expected acceleration time of the vehicle onto the displacement-time graph to obtain the upper boundary of the position constraint.

[0129] Projecting the trajectory of the vehicle behind the vehicle within the expected acceleration time of the vehicle onto the displacement-time graph yields the lower boundary of the position constraint.

[0130] For example, the first time period can be 8 seconds.

[0131] In some embodiments, since the vehicle accelerates within a time period after a successful lane change, the difference between the first time period and the vehicle's expected acceleration time is also referred to as the lane change completion time. This lane change completion time is calibrated, and different lane change modes correspond to different lane change completion times. In other words, the boundary line projected onto the displacement-time diagram can be the lane change completion time.

[0132] For example, see Figure 5A The lane change creation completion time is 4 seconds. Within this time, only the vehicle directly in front is used as the upper boundary of the vehicle's trajectory. After the lane change completion time, the vehicle needs to form final upper and lower boundary constraints for three targets: the vehicle directly in front, the vehicle to the side front, and the vehicle to the side rear. First, the 8-second trajectory of the nearest vehicle directly in front (the following target) is mapped into the ST graph. Starting from the 5th second, the trajectories of the vehicles to the side front and the vehicles to the side rear are mapped into the ST graph for processing. For vehicles in front of the lane change space, the vehicle can start moving after 4 seconds, which is the estimated time for the vehicle to create its own lane. In this way, the number of surrounding vehicles entering the ST graph ultimately does not exceed 3. The sampling period is 0.1 seconds, traversing from 8 seconds to 0 seconds to form a monotonic upper and lower boundary. This upper and lower boundary is used as the inequality position constraint of QP.

[0133] By using the above-mentioned technical means, the process of a vehicle changing lanes is divided into an intention generation stage and an actual execution stage. In other words, it takes a certain amount of time for a vehicle to create a lane change. By projecting multiple surrounding vehicles from different directions in different time periods, the accuracy of lane change trajectory optimization can be improved and the amount of computation required to solve the problem can be reduced.

[0134] Step 302: Determine the speed constraint conditions based on the preset vehicle speed range.

[0135] In the embodiments of this application, different lane-changing modes correspond to different preset vehicle speed ranges, and comfortable acceleration / deceleration limits and slope limits are calibrated in different scenarios to form corresponding inequality constraints.

[0136] In one possible implementation, the vehicle speed range can also be obtained based on the vehicle's speed before the lane change. For example, in a merging lane-changing scenario, the vehicle speed range is less than or equal to 115% of the set speed and greater than or equal to 40% of the vehicle's speed before the lane change.

[0137] Step 303: Optimize the planned lane-changing trajectory using the objective cost function, position constraints, and velocity constraints to obtain the target lane-changing trajectory and target acceleration.

[0138] In one possible implementation, the target cost function is a fitted functional expression of the current lane and the target lane's planar curves. Optionally, road planar information is collected, and curved road information is collected using high-precision maps, sensors, and Global Positioning System (GPS) devices to obtain the x and y coordinates of the planar sample points of the curved road segment and the curve length s between adjacent sample points. The functional expression of the current lane and the target lane's planar curves is then fitted using Chebyshev interpolation.

[0139] In this embodiment of the application, since the position, speed and acceleration of the lane change trajectory need to be seamlessly connected, the constraints may also include equality constraints for the starting point and the connection point.

[0140] For example, the expression for the target cost function is as follows:

[0141] (1)

[0142] In formula (1), H represents the matrix obtained by converting the optimization objective function into the QP formula form, and x and f both represent vectors in the feasible region of the objective cost function.

[0143] The constraints are as follows:

[0144] (2)

[0145] (3)

[0146] (4)

[0147] Wherein, formula (2) represents the upper and lower boundaries of S, st represents the effective set, LB represents the upper boundary of S, and UB represents the lower boundary of S; formula (3) represents the equality constraint between the starting point and the connection point. Indicates the starting point. The connection point is indicated by equation (4); the velocity constraint condition is indicated by equation (4). Indicates the speed range. Indicates the velocity boundary.

[0148] In some embodiments, step 303 may include: solving the target cost function to obtain the planned velocity, planned acceleration, and derivative of the planned acceleration at each sampling point of the vehicle in the trajectory to be traveled, thereby obtaining the target lane-changing trajectory and the target acceleration. The algorithm used to solve the target cost function may include: Lagrange method, interior point method, effective set method, ellipsoidal algorithm, etc.

[0149] Optionally, the above-mentioned vehicle lane-changing control method may further include: performing a secondary verification of the target acceleration obtained by QP within the vehicle's expected acceleration time. For example, if the lane-changing completion time is 3s and the first time period is 7s, then the vehicle's expected acceleration time is 4s. Within the 4th to 7th second, the target lane-changing trajectory and the upper and lower boundaries of the actual S are verified for a safe distance.

[0150] In some other embodiments, step 303 may further include: solving the target cost function that satisfies the constraints, and using the optimal solution as the target lane-changing trajectory; and determining the target acceleration based on the target lane-changing trajectory using the anchor point method.

[0151] In another possible implementation, see Figure 5B If multiple surrounding vehicles are only vehicles to the side and rear, and the speed of these vehicles exceeds a preset speed threshold; step 303 may further include: constructing the vehicle's expected acceleration trajectory and expected deceleration trajectory within a first time period, wherein the expected acceleration trajectory is generated based on the maximum acceleration value, and the expected deceleration trajectory is generated based on the minimum acceleration value; and correcting the original acceleration trajectory using an acceleration correction reference trajectory. Optionally, the maximum acceleration value can be obtained by looking up a table based on the speed difference between the vehicle and the vehicles to the side and rear. For example, when the vehicles to the side and rear are 3 m / s faster than the vehicle, the maximum acceleration value is +130% of the calibrated acceleration value, and the minimum acceleration value is -50% of the calibrated acceleration value. This can generate unnecessary false acceleration or deceleration experiences, keeping the vehicle within a small range of fluctuation along the uniform speed reference line.

[0152] In summary, in this embodiment of the application, since the mathematical model based on quadratic programming is easier to solve, the amount of computation during lane change planning is reduced. This not only reduces the consumption of computing resources but also improves computational efficiency, thereby enhancing the real-time performance and applicability of vehicle position trajectory and acceleration control during lane changes and improving the user's driving experience.

[0153] In some embodiments, the above-described vehicle control lane changing method may further include the following steps:

[0154] When the vehicle starts and accelerates, obtain the vehicle's start-up lane change duration, current first driving parameter, and current second driving parameter;

[0155] Input the current first driving parameters and the current second driving parameters into the trajectory prediction model to obtain the first distance between the vehicle and the vehicle directly in front, and the second distance between the vehicle and the target vehicle directly behind in the second time period in the future. The second time period in the future is the difference between the vehicle's expected acceleration time and the start-up lane change time.

[0156] If the conditions for lane changing and reversing are met, the vehicle is controlled to abandon the lane change and return to the original lane. The conditions for lane changing and reversing include: the first distance is less than the first safe distance, or the second distance is less than the second safe distance.

[0157] The first safe distance can be the minimum safe distance that needs to be maintained between the vehicle and the vehicle directly in front, and the second safe distance can be the minimum safe distance that needs to be maintained between the vehicle and the vehicle directly behind.

[0158] For example, the first safety distance is 6m and the second safety distance is 3m.

[0159] In one possible implementation, the first safe distance is determined based on the speed difference between the vehicle and the vehicle directly in front, and the relative distance between the vehicle and the vehicle directly in front. The second safe distance is determined based on the speed difference between the vehicle and the vehicle directly in front, and the relative distance between the vehicle and the vehicle directly behind.

[0160] For example, after the vehicle initiates a lane change, the decision-making process will uniformly schedule the lateral lane change time. Assuming that it takes 6 seconds to complete the lane change alignment, the timer will start from the start of the lane change. If it is 2 seconds after the start, it is only necessary to calculate in real time that the vehicle will not collide with the vehicle directly behind it in the longitudinal position in the next 4 seconds. By analogy, the longer the vehicle takes to initiate a lane change, the lower the possibility of backtracking. This strategy can effectively avoid the poor overall lane change experience caused by accidental backtracking.

[0161] In another possible implementation, the above-mentioned vehicle lane change control method may further include: when the speed of the vehicle directly behind is greater than the speed of the vehicle itself and the speed difference between the vehicle and the vehicle directly behind is less than a preset speed difference threshold, or when the lane change mode is created as an on-ramp lane change, reducing the first safety distance and the second safety distance and increasing the current acceleration of the vehicle itself.

[0162] For example, if the vehicle directly behind is traveling much faster than the vehicle itself, the estimated travel distance L1 for the vehicle in the second time period is calculated based on the vehicle's real-time acceleration and speed from the previous cycle. Simultaneously, the actual relative distance L2 between the vehicle and the vehicle directly behind is calculated. A preset minimum safe distance judgment method is used to calculate the minimum safe distance L3 that the vehicle and the vehicle need to maintain. It can be seen that L1 + L2 - L3 represents the maximum distance the vehicle directly behind will travel in the second time period. Based on the vehicle's speed and the minimum safe distance, the minimum acceleration difference that the vehicle needs to reduce when braking is calculated. If the reduced acceleration difference is less than a preset acceleration threshold, the vehicle meets the lane-changing and reversing conditions; that is, the lane change is safe, reversing is not necessary, and the faster vehicle behind can yield. Otherwise, the vehicle abandons the lane change and returns to its original lane.

[0163] In another possible implementation, the above-mentioned vehicle lane change control method may also include: when the lane change mode is overtaking lane change, navigation lane change, or lane change to avoid large vehicles, the vehicle uses a preset cruise acceleration to change lanes.

[0164] In another possible implementation, the above-mentioned vehicle lane change control method may further include: when the speed difference between the vehicle and the vehicle directly behind is greater than or equal to a preset speed difference threshold, or when the lane change mode is created as an off-ramp lane change, the vehicle uses a preset gentle acceleration to change lanes.

[0165] Through the above-mentioned technical means, when a vehicle meets the conditions for lane changing and reversing, it can promptly return to its original lane, replan the current vehicle's lane changing trajectory, and re-make a lane changing decision while maintaining a safe distance between the current vehicle and the vehicles in front and behind, ensuring that the current vehicle's actions are both safe and effective, and can smoothly complete the lane changing operation without interfering with other vehicles.

[0166] In some embodiments, the above-described vehicle control lane changing method may further include the following steps:

[0167] If the third distance between the vehicle and a target vehicle to the side and rear in the original lane is less than the third safe distance, or if there is a vehicle that wants to change lanes to the original lane, the vehicle is controlled to drive in the target lane and a prompt message is output to alert the driver of the potential collision risk.

[0168] For example, the third safety distance can be 2.5m.

[0169] In one possible implementation, the vehicle control lane-changing method described above may further include: identifying whether there are lane-changing vehicles within a preset target range that intend to change lanes back to their original lane. Optionally, identifying whether there are lane-changing vehicles within the preset target range that intend to change lanes back to their original lane may include: identifying other vehicles within the target range that have crossed the lane line of their original lane; and determining that other vehicles are lane-changing vehicles intending to change lanes back to their original lane if the proportion of other vehicles crossing the lane line is greater than a lane-changing threshold. Crossing the lane line may be a part of the vehicle, such as the tires or body, that has crossed the lane boundary line of the original lane. The lane-changing threshold may be a preset value, such as 50% of the vehicle's width, used to determine whether the vehicle is actually performing a lane-changing behavior. For example, an onboard vision sensor can be used to identify whether there are lane-changing vehicles intending to change lanes back to their original lane.

[0170] By using the aforementioned technical means, the vehicle can determine whether other vehicles are performing or preparing to perform lane-changing operations by using the third distance between the vehicle and the target vehicle to the side and rear in the original lane, as well as the relative position relationship of other vehicles in the surrounding area. This allows the vehicle to adjust its driving strategy to ensure safety and output prompt information, thereby improving driving safety and reducing the occurrence of accidents.

[0171] Figure 6 This is a block diagram of a vehicle lane-changing control device provided in an embodiment of this application, such as... Figure 6 As shown, the device 30 includes:

[0172] The acquisition module 31 is used to acquire the vehicle's first driving parameters, second driving parameters, and road information. The second driving parameters include multiple driving parameters of each of the multiple surrounding vehicles.

[0173] The determination module 32 is used to determine the vehicle's planned lane-changing trajectory, the vehicle's expected acceleration time, and the vehicle's expected acceleration based on the first driving parameters, the second driving parameters, and road information.

[0174] The calculation module 33 is used to optimize the planned lane-changing trajectory and the expected acceleration of the vehicle through a nonlinear programming method to obtain the target lane-changing trajectory and the target acceleration. The target lane-changing trajectory and the target acceleration-time function are smooth curves.

[0175] The control module 34 is used to control the vehicle to start acceleration according to the target acceleration within the expected acceleration time of the vehicle, so that the vehicle can travel from the original lane to the target lane according to the target lane change trajectory.

[0176] Optionally, module 32 is defined, specifically for:

[0177] Input the first driving parameter and the second driving parameter into the trajectory prediction model to obtain the vehicle's motion state and the motion state of multiple surrounding vehicles in the first time period in the future. The motion state of multiple surrounding vehicles includes the motion state of each of the multiple surrounding vehicles.

[0178] Based on the vehicle's movement status, the movement status of multiple surrounding vehicles, and road information, the planned lane-changing trajectory is determined;

[0179] Based on the planned lane-changing trajectory, the first driving parameters, and the second driving parameters, the estimated acceleration time of the vehicle is determined;

[0180] Based on the planned lane-changing trajectory, the initial driving parameters, and the vehicle's expected acceleration time, the vehicle's expected acceleration is determined.

[0181] Optionally, module 32 is defined, specifically for:

[0182] Based on road information, determine multiple lane-changing spaces in the adjacent lanes of the original lane. The lane-changing space is the gap in the adjacent lane that can be used for lane changing.

[0183] The target lane change space is determined from multiple lane change spaces by considering the vehicle's own motion status and the motion status of multiple surrounding vehicles.

[0184] The planned lane-changing trajectory is determined based on the vehicle's motion status and the target lane-changing space.

[0185] Optionally, the second driving parameters include the driving parameters of the vehicle directly in front, the driving parameters of the vehicle to the side front, and the driving parameters of the vehicle to the side rear. The determining module 32 is specifically used for:

[0186] Based on the planned lane-changing trajectory, the initial driving parameters, and the driving parameters of the vehicle directly ahead, the maximum lane-changing time is determined. The maximum lane-changing time is the maximum time period during which the vehicle can accelerate.

[0187] The vehicle's motion state and the driving parameters of the vehicles to the side and in front are input into the lane-changing game model to obtain the first lane-changing time.

[0188] The vehicle's motion state and the driving parameters of vehicles to the side and rear are input into the lane-changing game model to obtain the second lane-changing time.

[0189] If the first lane change time is less than or equal to the maximum lane change time, and the second lane change time is less than or equal to the maximum lane change time, the larger of the first and second lane change times will be used as the vehicle's expected acceleration time.

[0190] Optionally, the calculation module 33 is specifically used for:

[0191] The driving trajectories of multiple surrounding vehicles are projected onto the displacement-time graph to obtain position constraints; the driving trajectories of multiple surrounding vehicles include the driving trajectory of each of the multiple surrounding vehicles, and the driving trajectories of multiple surrounding vehicles are obtained by inputting the second driving parameter into the trajectory prediction model.

[0192] Determine the speed constraints based on the preset vehicle speed range;

[0193] The planned lane-changing trajectory is optimized using the objective cost function, position constraints, and velocity constraints to obtain the target lane-changing trajectory and target acceleration.

[0194] Optionally, the calculation module 33 is also used for:

[0195] Project the driving trajectories of the vehicles directly in front during the first time period and the driving trajectories of the vehicles in front of the vehicle within the expected acceleration time of the vehicle onto the displacement-time graph to obtain the upper boundary of the position constraint.

[0196] Projecting the trajectory of the vehicle behind the vehicle within the expected acceleration time of the vehicle onto the displacement-time graph yields the lower boundary of the position constraint.

[0197] Optionally, the acquisition module 31 is also used to acquire the vehicle's start-up lane-changing duration, current first driving parameters, and current second driving parameters when the vehicle starts accelerating.

[0198] The determination module 32 is also used to input the current first driving parameters and the current second driving parameters into the trajectory prediction model to obtain the first distance between the vehicle and the vehicle in front in the future second time period, and the second distance between the vehicle and the target vehicle behind. The future second time period is the difference between the vehicle's expected acceleration time and the start-up lane change time.

[0199] The control module 34 is also used to control the vehicle to abandon lane changing and return to the original lane when the vehicle meets the lane changing and reversing conditions. The lane changing and reversing conditions include: a first distance is less than a first safe distance, or a second distance is less than a second safe distance.

[0200] Optionally, the control module 34 is specifically used for:

[0201] If the third distance between the vehicle and a target vehicle to the side and rear in the original lane is less than the third safe distance, or if there is a vehicle that wants to change lanes to the original lane, the vehicle is controlled to drive in the target lane and a prompt message is output to alert the driver of the potential collision risk.

[0202] The vehicle lane-changing control device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0203] The vehicle lane-changing control device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0204] The vehicle lane-changing control device provided in this application embodiment can achieve... Figure 1 The various processes implemented by the vehicle lane-changing control device in the method embodiment will not be described again here to avoid repetition.

[0205] This application also provides a vehicle including the vehicle lane change control device as described above.

[0206] The specific implementation process of the vehicle lane change control device in the vehicle provided in this application embodiment is similar to that of the aforementioned vehicle lane change control device, and will not be repeated here.

[0207] In this embodiment, by acquiring the vehicle's first driving parameters, second driving parameters, and road information (the second driving parameters include multiple driving parameters for each of the surrounding vehicles), and then determining the vehicle's planned lane-changing trajectory, expected acceleration time, and expected acceleration based on the first driving parameters, second driving parameters, and road information, the vehicle's planned lane-changing trajectory, expected acceleration time, and expected acceleration are determined. Then, a nonlinear programming method is used to optimize the planned lane-changing trajectory and expected acceleration to obtain the target lane-changing trajectory and target acceleration. Finally, within the expected acceleration time, the vehicle is controlled to accelerate based on the target acceleration, allowing the vehicle to travel from the original lane to the target lane according to the target lane-changing trajectory. This achieves a higher probability of successful lane changes to ensure driving safety. Furthermore, it allows for appropriate adjustment of the lane-changing trajectory and acceleration. Since both the lane-changing trajectory and the acceleration-time function are smooth curves, meaning the target lane-changing trajectory and target acceleration have gentler slopes, smoother transitions, no sharp points, and no abrupt changes, the vehicle's acceleration is smoother, avoiding sudden acceleration or deceleration and thus improving passenger comfort.

[0208] Optionally, embodiments of this application also provide an electronic device, including a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the various processes of the above-described vehicle lane change control method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0209] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0210] Figure 7 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0211] The electronic device 400 includes, but is not limited to, components such as: radio frequency unit 401, network module 402, audio output unit 403, input unit 404, sensor 405, display unit 406, user input unit 407, interface unit 408, memory 409, and processor 410.

[0212] Those skilled in the art will understand that the electronic device 400 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 410 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 7The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0213] The processor 410 is used to acquire the vehicle's first driving parameters, second driving parameters, and road information. The second driving parameters include multiple driving parameters for each of the multiple surrounding vehicles.

[0214] Based on the first driving parameters, the second driving parameters, and road information, determine the vehicle's planned lane-changing trajectory, the vehicle's expected acceleration time, and the vehicle's expected acceleration.

[0215] The planned lane-changing trajectory and the predicted acceleration of the vehicle are optimized by nonlinear quadratic programming to obtain the target lane-changing trajectory and target acceleration. The target lane-changing trajectory and target acceleration-time function are smooth curves.

[0216] Within the expected acceleration time of the vehicle, the vehicle is controlled to start acceleration based on the target acceleration, so that the vehicle travels from the original lane to the target lane according to the target lane change trajectory.

[0217] It should be understood that, in this embodiment, the input unit 404 may include a graphics processing unit (GPU) 4041 and a microphone 4042. The GPU 4041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 407 includes at least one of a touch panel 4071 and other input devices 4072. The touch panel 4071 is also called a touch screen. The touch panel 4071 may include a touch detection device and a touch controller. Other input devices 4072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0218] The memory 409 can be used to store software programs and various data. The memory 409 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 409 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 409 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0219] Processor 410 may include one or more processing units; optionally, processor 410 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 410.

[0220] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described vehicle lane change control method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0221] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0222] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described vehicle lane change control method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0223] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0224] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0225] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0226] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

Claims

1. A vehicle lane-changing control method, characterized in that, The method includes: Acquire the vehicle's first driving parameters, second driving parameters, and road information; the second driving parameters include the driving parameters of the vehicle directly in front, the vehicle to the side front, and the vehicle to the side rear. Based on the first driving parameters, the second driving parameters, and the road information, the planned lane-changing trajectory, the vehicle's expected acceleration time, and the vehicle's expected acceleration are determined. The planned lane-changing trajectory and the predicted acceleration of the vehicle are optimized by nonlinear programming to obtain the target lane-changing trajectory and the target acceleration. Both the target lane-changing trajectory and the target acceleration-time function are smooth curves. Within the expected acceleration time of the vehicle, the vehicle is controlled to start acceleration according to the target acceleration, so that the vehicle travels from the original lane to the target lane according to the target lane change trajectory; The step of determining the planned lane-changing trajectory, the estimated acceleration time, and the estimated acceleration of the vehicle based on the first driving parameters, the second driving parameters, and the road information includes: Input the first driving parameter and the second driving parameter into the trajectory prediction model to obtain the vehicle's motion state and the motion state of multiple surrounding vehicles in the future first time period. The motion state of the multiple surrounding vehicles includes the motion state of each of the multiple surrounding vehicles. The planned lane-changing trajectory is determined based on the vehicle's motion state, the motion states of the multiple surrounding vehicles, and the road information. Based on the planned lane-changing trajectory, the first driving parameters, and the driving parameters of the vehicle directly ahead, the maximum lane-changing time is determined, where the maximum lane-changing time is the maximum time period during which the vehicle can accelerate. The vehicle's motion state and the driving parameters of the vehicle to the side and in front are input into the lane-changing game model to obtain the first lane-changing time. The vehicle's motion state and the driving parameters of the vehicles to the side and rear are input into the lane-changing game model to obtain the second lane-changing time. If the first lane change time is less than or equal to the maximum lane change time, and the second lane change time is less than or equal to the maximum lane change time, the larger of the first lane change time and the second lane change time shall be used as the vehicle's expected acceleration time. The expected acceleration of the vehicle is determined based on the planned lane-changing trajectory, the first driving parameters, and the expected acceleration time of the vehicle.

2. The method according to claim 1, characterized in that, Determining the planned lane-changing trajectory based on the vehicle's motion state, the motion states of the surrounding vehicles, and the road information includes: Based on the road information, multiple lane-changing spaces are determined in the adjacent lanes of the original lane, and the lane-changing space is the gap in the adjacent lane that can be used for lane changing; The target lane change space is determined from the multiple lane change spaces by considering the vehicle's motion state and the motion states of the surrounding vehicles. The planned lane-changing trajectory is determined based on the vehicle's motion state and the target lane-changing space.

3. The method according to claim 1, characterized in that, The optimization of the planned lane-changing trajectory and the predicted acceleration of the vehicle using a nonlinear programming method to obtain the target lane-changing trajectory and target acceleration includes: The driving trajectories of the multiple surrounding vehicles are projected onto the displacement-time graph to obtain position constraints; the driving trajectories of the multiple surrounding vehicles include the driving trajectory of each of the multiple surrounding vehicles, and the driving trajectories of the multiple surrounding vehicles are obtained by inputting the second driving parameters into the trajectory prediction model. Determine the speed constraints based on the preset vehicle speed range; The planned lane-changing trajectory is optimized using the objective cost function, the position constraint, and the velocity constraint to obtain the target lane-changing trajectory and the target acceleration.

4. The method according to claim 3, characterized in that, The step of projecting the driving trajectories of the multiple surrounding vehicles onto a displacement-time graph to obtain positional constraints includes: The upper boundary of the position constraint is obtained by projecting the driving trajectory of the vehicle directly in front during the first time period and the driving trajectory of the vehicle in front inside the expected acceleration time of the vehicle onto the displacement-time graph. By projecting the driving trajectory of the vehicle behind the vehicle within the expected acceleration time of the self-vehicle onto the displacement-time graph, the lower boundary of the position constraint is obtained.

5. The method according to claim 1, characterized in that, The method further includes: When the vehicle starts accelerating, the vehicle's lane-changing time, current first driving parameters, and current second driving parameters are obtained. The current first driving parameters and the current second driving parameters are input into the trajectory prediction model to obtain the first distance between the vehicle and the vehicle directly in front, and the second distance between the vehicle and the target vehicle directly behind in the future second time period. The future second time period is the difference between the vehicle's expected acceleration time and the start-up lane change time. If the vehicle meets the lane change and reversal conditions, the vehicle is controlled to abandon the lane change and return to the original lane. The lane change and reversal conditions include: the first distance is less than the first safe distance, or the second distance is less than the second safe distance.

6. The method according to claim 5, characterized in that, The method further includes: If the third distance between the vehicle and a target vehicle to the side and rear in the original lane is less than the third safe distance, or if there is a vehicle that wants to change lanes to the original lane, the vehicle is controlled to travel in the target lane, and a prompt message is output to alert the driver of the potential collision risk.

7. A vehicle lane-changing control device, characterized in that, The device includes: The acquisition module is used to acquire the vehicle's first driving parameters, second driving parameters, and road information. The second driving parameters include the driving parameters of the vehicle directly in front, the vehicle to the side front, and the vehicle to the side rear. The determination module is used to determine the planned lane-changing trajectory, the expected acceleration time, and the expected acceleration of the vehicle based on the first driving parameters, the second driving parameters, and the road information. The calculation module is used to optimize the planned lane-changing trajectory and the predicted acceleration of the vehicle using a nonlinear programming method to obtain the target lane-changing trajectory and the target acceleration. The target lane-changing trajectory and the target acceleration-time function are both smooth curves. The control module is used to control the vehicle to start acceleration according to the target acceleration within the expected acceleration time of the vehicle, so that the vehicle travels from the original lane to the target lane according to the target lane change trajectory; The determining module is specifically used for: Input the first driving parameter and the second driving parameter into the trajectory prediction model to obtain the vehicle's motion state and the motion state of multiple surrounding vehicles in the future first time period. The motion state of the multiple surrounding vehicles includes the motion state of each of the multiple surrounding vehicles. The planned lane-changing trajectory is determined based on the vehicle's motion state, the motion states of the multiple surrounding vehicles, and the road information. Based on the planned lane-changing trajectory, the first driving parameters, and the driving parameters of the vehicle directly ahead, the maximum lane-changing time is determined, where the maximum lane-changing time is the maximum time period during which the vehicle can accelerate. The vehicle's motion state and the driving parameters of the vehicle to the side and in front are input into the lane-changing game model to obtain the first lane-changing time. The vehicle's motion state and the driving parameters of the vehicles to the side and rear are input into the lane-changing game model to obtain the second lane-changing time. If the first lane change time is less than or equal to the maximum lane change time, and the second lane change time is less than or equal to the maximum lane change time, the larger of the first lane change time and the second lane change time shall be used as the vehicle's expected acceleration time. The expected acceleration of the vehicle is determined based on the planned lane-changing trajectory, the first driving parameters, and the expected acceleration time of the vehicle.

8. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the vehicle lane change control method as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the vehicle lane change control method as described in any one of claims 1 to 6.

10. A vehicle, characterized in that, Includes the vehicle lane change control device as described in claim 7.

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