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

Abstract

The application discloses a vehicle lane changing control method, device, equipment, medium and vehicle. The method comprises the following steps: in the case that a lane changing behavior of a second vehicle located in front of a first vehicle is detected, target scene features are acquired; according to the target scene features, target artificial driving experience information matched with a lane changing scene in which the second vehicle is located is determined; according to the target artificial driving experience information, a lane changing decision result is generated; and according to the lane changing decision result, lane changing control of the first vehicle is performed. The application can improve the experience of autonomous lane changing control of the vehicle and reduce the probability of invalid lane changing of the vehicle.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a method, device, equipment, medium and vehicle for controlling vehicle lane change. Background Art

[0002] Advanced Driver Assistance Systems (ADAS) refer to a range of vehicle systems designed to improve driving safety, reduce driver workload, and enhance the overall driving experience. These systems utilize onboard sensors, cameras, radar, LiDAR, and other sensor devices to gather information about the vehicle's surroundings and take appropriate actions to help the driver better control the vehicle.

[0003] Autonomous lane change is a fundamental feature of assisted driving. It allows a vehicle to change lanes without driver intervention, supported by the assisted driving system. Among related technologies, the Level 2+ assisted driving feature, Navigation on Highway Assist (NOA), enables autonomous lane changes under certain road conditions.

[0004] Although current assisted driving technology can enable vehicles to change lanes autonomously, it is limited by the system's intelligence level. The autonomous lane change control experience of existing vehicles is poor, which can easily lead to a negative experience of ineffective lane changes. Summary of the Invention

[0005] This application aims to propose a vehicle lane change control method, device, equipment, medium and vehicle, which can improve the experience of vehicle autonomous lane change control and reduce the probability of invalid lane changes.

[0006] In a first aspect, an embodiment of the present application provides a method for controlling a vehicle lane change, comprising the following steps:

[0007] When a second vehicle ahead of a first vehicle is detected to have changed lanes, a target scene feature is acquired, wherein the second vehicle is the vehicle ahead of the first vehicle that has changed lanes, and the target scene feature is used to indicate the lane change scene in which the second vehicle is located;

[0008] Determining target human driving experience information that matches the lane change scenario in which the second vehicle is located based on the target scenario characteristics, wherein the target human driving experience information is used to describe the target human driving experience that conforms to a preset driving logic;

[0009] generating a lane change decision result based on the target person's driving experience information, wherein the lane change decision result is used to indicate whether the first vehicle is to change lanes and the lane change direction if the lane change occurs, based on the target person's driving experience;

[0010] Execute lane change control of the first vehicle according to the lane change decision result.

[0011] According to some embodiments of the present application, obtaining target scene features includes:

[0012] Acquiring scene recognition information associated with the first vehicle and the second vehicle, the scene recognition information including at least one of traffic regulation information and map information;

[0013] Determine the target scene characteristics based on the scene cognition information.

[0014] According to some embodiments of the present application, the second vehicle is a target type vehicle, and the scene recognition information includes traffic rule information for describing lane change rules for the target type vehicle;

[0015] The determining the target scene feature according to the scene recognition information includes:

[0016] When the speed of the second vehicle during the lane change is lower than the speed of the first vehicle, obtaining lane information of the lane in which the second vehicle was located before the lane change;

[0017] According to the lane information and the traffic rule information, a lane-changing scene feature is determined, and the target scene feature includes the lane-changing scene feature.

[0018] According to some embodiments of the present application, the second vehicle is a cargo vehicle;

[0019] The performing lane change control of the first vehicle according to the lane change decision result includes:

[0020] In the lane-changing scenario, if the truck is located in the rightmost lane before changing lanes, according to the lane-changing decision result, preventing the first vehicle from changing lanes to the lane before the truck changes lanes; or

[0021] In the lane-changing scenario, when the truck is not located in the rightmost lane of the road before changing lanes, the first vehicle is controlled to change lanes to the lane before the truck changes lanes according to the lane-changing decision result.

[0022] According to some embodiments of the present application, obtaining scene recognition information associated with the first vehicle and the second vehicle includes:

[0023] When detecting that the second vehicle changes lanes multiple times in a row toward a target direction, obtaining map information associated with the first vehicle and the second vehicle;

[0024] The determining the target scene feature according to the scene recognition information includes:

[0025] In a case where it is determined based on the map information that the second vehicle's lane change behavior is to enter a ramp on the road side corresponding to the target direction, an off-ramp lane change scenario is determined, wherein the target scenario feature includes the off-ramp lane change scenario.

[0026] The lane change decision result is used to instruct the first vehicle to change lanes in a direction opposite to the target direction based on the target human driving experience.

[0027] According to some embodiments of the present application, determining target human driving experience information that matches the lane change scenario in which the second vehicle is located based on the target scenario characteristics includes:

[0028] Inputting the target scene feature into a preset driving experience database to obtain human driving experience information corresponding to the target scene feature from the driving experience database, wherein the driving experience database is a database including correspondences between different scene features and human driving experience information;

[0029] When human driving experience information having a corresponding relationship with the target scene feature is obtained from the driving experience database, the obtained human driving experience information is determined as target human driving experience information.

[0030] In a second aspect, an embodiment of the present application provides a vehicle lane change control device, comprising:

[0031] a scene feature acquisition module, configured to acquire a target scene feature when a second vehicle located ahead of a first vehicle is detected to have changed lanes, wherein the second vehicle is the vehicle ahead of the first vehicle that has changed lanes, and the target scene feature is used to indicate the lane change scene in which the second vehicle is located;

[0032] a driving experience determination module, configured to determine, based on the target scene characteristics, target human driving experience information that matches the lane change scene in which the second vehicle is located, wherein the target human driving experience information is used to describe the target human driving experience that conforms to a preset driving logic;

[0033] a lane change decision generating module, configured to generate a lane change decision result based on the target person's driving experience information, wherein the lane change decision result is used to indicate whether the first vehicle is to change lanes and the lane change direction if the lane change occurs, based on the target person's driving experience;

[0034] A lane change control execution module is used to execute lane change control of the first vehicle according to the lane change decision result.

[0035] In a third aspect, an embodiment of the present application provides an electronic device, the device comprising: a processor and a memory storing computer program instructions;

[0036] When the processor executes the computer program instructions, the vehicle lane change control method as described in the first aspect is implemented.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the vehicle lane change control method as described in the first aspect is implemented.

[0038] In a fifth aspect, an embodiment of the present application provides a vehicle, which performs lane change control using the vehicle lane change control method as described in the first aspect.

[0039] The vehicle lane change control method, device, equipment, medium, and vehicle of the embodiments of the present application have at least the following beneficial effects:

[0040] This application first detects a lane change by a second vehicle ahead of a first vehicle and obtains target scene features. Based on these target scene features, it then determines target driver driving experience information that matches the lane change scenario of the second vehicle. It then generates a lane change decision based on the target driver driving experience information. Finally, it executes lane change control for the first vehicle based on the lane change decision. This application can improve the experience of autonomous lane change control and reduce the likelihood of ineffective lane changes.

[0041] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present application is further described below with reference to the accompanying drawings and embodiments, wherein:

[0043] Figure 1 A schematic flow chart of an embodiment of a vehicle lane change control method provided by the present application;

[0044] Figure 2A schematic diagram of a first cut-in lane change scenario in a vehicle lane change control method;

[0045] Figure 3 A schematic diagram of a second cut-in lane change scenario in a vehicle lane change control method;

[0046] Figure 4 A schematic diagram of an off-ramp lane change scenario in a vehicle lane change control method;

[0047] Figure 5 A structural schematic diagram of an embodiment of a vehicle lane change control device provided by the present application;

[0048] Figure 6 A structural schematic diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0049] Features and exemplary embodiments of various aspects of the present application will be described in detail below, in order to make the purposes, technical solutions and advantages of the present application more clear and apparent, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. The present application can be implemented without some of these specific details by those skilled in the art. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0050] In this document, relational terms such as first and second and the like can only be used to distinguish one entity or action from another entity or action, and do not necessarily require or imply that these entities or actions exist in any such actual relationship or order. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements does not only include those elements, but also includes other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, an element defined by the statement "comprising" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0051] To solve the problems in the prior art, the embodiments of the present application provide a vehicle lane change control method, device, equipment, medium and vehicle. First, the vehicle lane change control method provided by the embodiments of the present application will be introduced.

[0052] Figure 1 A flowchart of the vehicle lane change control method provided by the embodiments of the present application is shown. The method is applied to an electronic device.

[0053] As Figure 1 As shown, the above-mentioned vehicle lane change control method includes the following steps:

[0054] S101, when a second vehicle ahead of a first vehicle is detected to have changed lanes, obtaining a target scene feature, wherein the second vehicle is the vehicle ahead of the first vehicle that has changed lanes, and the target scene feature is used to indicate a lane change scene in which the second vehicle is located;

[0055] S102: Determine, based on the target scene characteristics, target driver driving experience information that matches the lane change scene in which the second vehicle is located, where the target driver driving experience information is used to describe the target driver driving experience that conforms to a preset driving logic;

[0056] S103: Generate a lane change decision result based on the target person's driving experience information, where the lane change decision result is used to indicate whether the first vehicle is to change lanes and the lane change direction if the lane change occurs, based on the target person's driving experience;

[0057] S104. Execute lane change control of the first vehicle according to the lane change decision result.

[0058] In this embodiment, upon detecting a lane change by a second vehicle ahead of a first vehicle, target scene features are first acquired. Based on the target scene features, target driver driving experience information matching the lane change scenario of the second vehicle is determined. A lane change decision result is then generated based on the target driver driving experience information. Finally, lane change control of the first vehicle is executed based on the lane change decision result. This application can enhance the experience of autonomous lane change control and reduce the likelihood of ineffective lane changes.

[0059] In the above step S101, when it is detected that a second vehicle located in front of a first vehicle changes lanes, a target scene feature is obtained, wherein the second vehicle is the vehicle that changes lanes in front of the first vehicle, and the target scene feature is used to indicate the lane change scene in which the second vehicle is located.

[0060] The first vehicle mentioned above refers to this vehicle, which is equipped with an assisted driving system and can change lanes autonomously, and the second vehicle refers to other vehicles that change lanes in front of this vehicle.

[0061] The above-mentioned detection of the lane change of the second vehicle located in front of the first vehicle refers to the surrounding environment information of the vehicle obtained by the vehicle through one or more of the front-view camera, side-view camera, lidar, millimeter-wave radar and other vehicle-mounted sensors. The surrounding environment information includes but is not limited to lane information, as well as vehicle information in lanes such as the own lane, adjacent lane, and adjacent lane. It also includes the vehicle's target type, target position, lateral and longitudinal speeds, lateral and longitudinal accelerations, collision time (Time To Collision-TTC), etc. This information can be obtained directly through the sensor or calculated based on the sensor data. The movement trajectory of nearby vehicles is then predicted based on this information, and whether lane change behaviors such as cutting in and out occur are predicted based on the movement trajectory.

[0062] As an example, an implementation method of lane change behavior prediction is described below, specifically as follows:

[0063] First, the sensor acquires the characteristics of the target vehicle, such as the initial distance to the lane line, speed, and acceleration. The target vehicle is any vehicle near the host vehicle.

[0064] The above features are detected once every cycle;

[0065] The trajectory prediction method is used to calculate the motion trajectory of the target vehicle. The trajectory prediction method can adopt the Kalman filter algorithm or other trajectory prediction algorithms;

[0066] The lane change behavior of cutting in and out is determined by the relationship between the predicted motion trajectory and the lane line, such as the TTC and angle relationship with the lane line.

[0067] The following introduces a target trajectory calculation method to predict the trajectory of the target vehicle. The specific calculation process is as follows:

[0068] First, the target position and lateral and longitudinal speed of nearby vehicles are obtained through on-board sensors;

[0069] Then according to the kinematic calculation formula, we can get:

[0070] At time T0, X0=[Px0 Py0 Vx0 Vy0];

[0071] At time T1, X1=[Px1 Py1 Vx1 Vy1];

[0072] At time T2, X2=[Px2 Py2 Vx2 Vy2];

[0073] …

[0074] Furthermore, according to the kinematic calculation formula, we can get:

[0075] Xn=[Px(n-1)+Vx(n-1)* Py(n-1)+Vy(n-1)* Vx(n-1) Vy(n-1)]+[1 / 2 2 1 / 2 2 ].

[0076] Where X0 represents the position and velocity variables of the vehicle at time T0, with the lateral position relative to the vehicle coordinate system being Px0, the longitudinal position being Py0, the lateral velocity being Vx0, and the longitudinal velocity being Vy0; the same applies to X1 and X2; Xn represents the position and velocity of the vehicle at time Tn, Px(n-1) is the lateral position of the vehicle at time T(n-1), ax is the lateral acceleration of the vehicle, ay is the longitudinal acceleration of the vehicle, and Δt is the system operation period.

[0077] The current position, lateral and longitudinal velocities, and acceleration of the target vehicle can be obtained based on the sensor data at each moment. The position of the next point is estimated based on the current lateral and longitudinal velocities and acceleration. The calculation is performed every 10ms to finally obtain the motion trajectory of the target vehicle.

[0078] The target scene features described above refer to the lane change scenario of the second vehicle. The second vehicle's lane change behavior can be predicted based on the target vehicle's trajectory and surrounding environment information. This determines which lane change scenario the second vehicle's lane change behavior falls into, and then generates target scene features for the corresponding scenario. Lane change scenarios include, but are not limited to, lane change cut-in, lane change cut-out, lane change on an off-ramp, lane change onto an auxiliary lane, and lane change on a turn.

[0079] In the above step S102, target human driving experience information matching the lane change scenario of the second vehicle is determined based on the target scenario characteristics. The target human driving experience information is used to describe the target human driving experience that conforms to the preset driving logic.

[0080] The target human driving experience information mentioned above refers to experience information that conforms to driving logic in a specific driving scenario. It is equivalent to the driving maneuvers that an experienced virtual driver might predict based on the scenario, such as controlling the vehicle to slow down or change lanes in a lane change scenario, or changing lanes to avoid an obstacle ahead. The target human driving experience information that matches the lane change scenario refers to the driving maneuvers that an experienced virtual driver might predict in that lane change scenario.

[0081] The above-mentioned target human driving experience information can be obtained through a pre-built driving experience library, or through a driving experience model constructed by a neural network model, or through other artificial intelligence methods such as AI.

[0082] In the above step S103, a lane change decision result is generated according to the target person's driving experience information. The lane change decision result is used to indicate whether the first vehicle has changed lanes and the lane change direction when the lane change occurs based on the target person's driving experience.

[0083] The above-mentioned generation of the lane change decision result based on the target person's driving experience information refers to generating the lane change decision result in the current lane change scenario based on the target person's driving experience information obtained in step S102. The lane change decision result may indicate that the first vehicle changes lanes, or may indicate that the first vehicle does not change lanes, depending on whether an effective lane change is achieved. If it is determined based on the target person's driving experience information that the first vehicle changes lanes to improve traffic efficiency, the lane change is performed. If it is determined based on the target person's driving experience information that the first vehicle changes lanes to reduce traffic efficiency, the lane change is not performed.

[0084] The lane change decision results mentioned above include not only the decision results on whether to change lanes, but also the decision results on how to change lanes, such as cutting out to the left, cutting out to the right, continuous lane changes, etc.

[0085] In the above step S104 , lane change control of the first vehicle is executed according to the lane change decision result.

[0086] Lane change control, as described above, refers to the vehicle's execution of lane change control based on the lane change decision. This is achieved by invoking the autonomous lane change state machine, which describes the state transitions, vehicle control, and interaction processes that the driver assistance system automatically triggers when a lane change is initiated. This is a system-defined autonomous lane change process. When the lane change decision triggers the lane change indicator in the autonomous lane change state machine, the vehicle automatically moves laterally, activates the turn signal, and begins the lane change. The vehicle continuously monitors the surrounding environment and target status during the lane change process, performs risk assessments, and makes decisions and actions to continue or cancel the lane change.

[0087] In some implementations, obtaining target scene features may include:

[0088] Acquiring scene recognition information associated with the first vehicle and the second vehicle, the scene recognition information including at least one of traffic regulation information and map information;

[0089] Determine the target scene characteristics based on scene cognition information.

[0090] In this embodiment, scene recognition information associated with the first and second vehicles is first acquired, and then target scene features are determined based on the scene recognition information. This allows for more accurate acquisition of the target scene features, improving the match between the lane change scenario indicated by the target scene features and the actual lane change scenario.

[0091] The above-mentioned acquisition of scene recognition information associated with the first vehicle and the second vehicle refers to acquiring traffic rule information and / or map information associated with the first vehicle and the second vehicle, and then more accurately acquiring the target scene features of the second vehicle based on the traffic rule information and / or map information.

[0092] The scene recognition information mentioned above includes at least one of traffic rule information and map information. Traffic rule information is used to indicate the traffic rules that the vehicle should follow during driving, such as the maximum and minimum speeds of each lane, lane type, traffic instructions corresponding to different lane lines and signs, and different traffic rules corresponding to different vehicles. For example, special vehicles such as ambulances and fire trucks need to be given priority. Traffic rule information can help more accurately determine the characteristics of the target scene. Map information refers to a priori map information, which refers to pre-loaded map information, including road layout, traffic signals, service areas, ramps, road signs, etc. On the one hand, map information provides the basic information required for the vehicle to locate and navigate in its environment. On the other hand, map information can help more accurately determine the characteristics of the target scene. For example, if the map information indicates that there is an intersection ahead, and the second vehicle on the left ahead makes a continuous right lane change, it may be a right lane change scenario. If the second vehicle on the right ahead makes a continuous left lane change, it may be a left lane change scenario.

[0093] In some embodiments, the second vehicle is a target type vehicle, and the scene awareness information includes traffic rule information describing lane change rules for the target type vehicle;

[0094] Based on the scene cognition information, determine the characteristics of the target scene, including:

[0095] When the speed of the second vehicle during the lane change is lower than the speed of the first vehicle, obtaining lane information of the lane in which the second vehicle was located before the lane change;

[0096] According to the lane information and traffic rule information, a lane change scenario is determined, and the target scenario characteristics include the lane change scenario.

[0097] In this embodiment, when the second vehicle is a target type vehicle, lane information of the lane the second vehicle was in before the lane change is obtained, provided that the second vehicle's speed during the lane change is lower than that of the first vehicle. Based on this lane information and traffic regulation information, a lane change scenario is determined, with the target scenario features including the lane change scenario. This improves the match between the lane change scenario indicated by the target scenario features and the actual lane change scenario.

[0098] The target type of vehicles mentioned above refers to vehicles with specific traffic rules, such as cargo vehicles, large vehicles, and special vehicles such as ambulances and fire trucks.

[0099] In the case where the second vehicle's speed during the lane change is lower than that of the first vehicle, obtaining lane information for the second vehicle's lane prior to the lane change involves first calculating the first vehicle's speed and the second vehicle's speed during the lane change using onboard sensors. If the second vehicle's speed during the lane change is determined to be lower than that of the first vehicle, it is assumed that the second vehicle may be speeding down the first vehicle, thereby impacting the first vehicle's traffic efficiency. If it is determined that the second vehicle is speeding down the first vehicle, lane information for the second vehicle's lane prior to the lane change is obtained, and then, based on this lane information and traffic rule information, a lane change scenario is determined.

[0100] In the above lane change scenario determined based on lane information and traffic rule information, the lane change scenario is equivalent to the second vehicle cutting into the lane where the first vehicle is located and speeding up. Usually, the logic judgment of the assisted driving is to directly request a lane change to the left or right to avoid the speeding up behavior of the second vehicle. However, when the second vehicle is a cargo vehicle, a large vehicle, or a special vehicle such as an ambulance or a fire truck, it has specific traffic rules. For example, cargo vehicles and large vehicles need to drive on the right according to traffic rules. For example, special vehicles such as ambulances and fire trucks can drive in the emergency lane in an emergency. Therefore, simply requesting a lane change in the lane change scenario may result in an invalid lane change. In this application, the lane change scenario is determined based on lane information and traffic rule information, and the appropriate lane change scenario is determined based on the different types of second vehicles, lane information and traffic rules, so as to further improve the matching degree between the lane change scenario indicated by the target scene feature and the actual lane change scenario.

[0101] It should be understood that the above-mentioned lane change scenarios include many different types of situations. Depending on the different second vehicles, different lane information and different traffic rules of the corresponding vehicles, different lane change sub-scenarios can be obtained. Different target human driving experience information and lane change decision results can be obtained according to the lane change scenarios in different situations.

[0102] In some embodiments, the second vehicle is a cargo vehicle;

[0103] Performing lane change control of the first vehicle according to the lane change decision result may include:

[0104] In a lane change scenario, if the truck is located in the rightmost lane before changing lanes, according to the lane change decision result, the first vehicle is prevented from changing lanes to the lane before the truck changes lanes; or

[0105] In a lane change scenario, when the cargo vehicle is not located in the rightmost lane of the road before changing lanes, the first vehicle is controlled to change lanes to the lane before the cargo vehicle changed lanes according to the lane change decision result.

[0106] In this embodiment, when the second vehicle is a cargo vehicle, if the cargo vehicle is in the rightmost lane of the road before changing lanes in a cut-in lane change scenario, the first vehicle is prevented from changing lanes to the lane before the cargo vehicle changes lanes based on the lane change decision result. Alternatively, if the cargo vehicle is not in the rightmost lane before changing lanes in a cut-in lane change scenario, the first vehicle is controlled to change lanes to the lane before the cargo vehicle changes lanes based on the lane change decision result. This improves the rationality of lane change decision results and further reduces the probability of invalid lane changes.

[0107] The above-mentioned cargo vehicles refer to any large transport vehicles such as trucks, vans, tankers, etc.

[0108] Because traffic regulations require trucks to keep to the right, the rightmost road is the default lane for trucks. When a lane change occurs, the assisted driving logic in related technologies is to change lanes directly to the left or right. Since the rightmost road is the lane for trucks, the truck is on the rightmost road before the lane change. When the truck cuts in and changes lanes to the left, human driving experience can determine that either there is a slow-moving vehicle on the rightmost road, causing the truck to overtake, or there is an obstacle or other obstruction on the rightmost road, forcing the truck to change lanes to the left. At this time, if the first vehicle changes lanes to the right, driving efficiency will not be improved, resulting in an invalid lane change. Furthermore, when the truck cuts in and changes lanes to the left, the first vehicle's onboard camera used for right-front detection will be blocked by the second vehicle, making it difficult to see the road conditions further ahead on the right road. Therefore, if there is a vehicle on the left road, a lane change request will be made.

[0109] Therefore, in this embodiment, when the cargo vehicle is located in the rightmost lane of the road before changing lanes in a lane change scenario, the target person's driving experience information can be used to make a judgment consistent with the driving experience, generating a lane change decision result that prevents the first vehicle from changing lanes to the lane before the cargo vehicle changes lanes, thereby avoiding invalid lane changes in this situation.

[0110] Exemplary, reference Figure 2As shown, this is the first lane-changing scenario. At this time, the first vehicle is located in the second lane on the right, and there is a vehicle in the left lane of the first vehicle, and the traffic efficiency of the left lane is low. At this time, the cargo vehicle located in the rightmost lane cuts left into the lane where the first vehicle is located. Since the traffic efficiency of the left lane of the first vehicle is low, the auxiliary system of the first vehicle will default to request a lane change to the right due to the speed reduction behavior of the cargo vehicle cutting in. At this time, there is a low-speed truck further ahead in the rightmost lane. Changing lanes to the right will further reduce the traffic efficiency. Therefore, in this embodiment, a judgment consistent with the driving experience is made based on the target person's driving experience information, and a lane change decision result is generated to prevent the first vehicle from changing lanes to the lane before the cargo vehicle changes lanes, so as to avoid invalid lane changes in this situation.

[0111] Therefore, in this embodiment, when the cargo vehicle is not located in the rightmost lane of the road before changing lanes in the lane change scenario, a lane change decision result that allows the lane change can be obtained based on the target human driving experience information, thereby controlling the first vehicle to change lanes to the lane before the cargo vehicle changed lanes.

[0112] Exemplary, reference Figure 3 The figure shows the second lane-changing scenario. At this time, the first vehicle is located in the leftmost lane, and there is no other vehicle to the right of the first vehicle. When a cargo vehicle on the right slowly cuts into the own lane, if the driver waits for the cargo vehicle to completely cut into the own lane and triggers the acceleration before changing lanes to the right, it will lead to a poor lane-changing experience due to excessive deceleration. Therefore, a judgment consistent with the driving experience is made based on the target person's driving experience information, and a lane-changing decision result of changing lanes to the right in advance is generated, thereby improving the rationality of the lane-changing decision result.

[0113] It should be noted that in addition to cargo vehicles, other lane-changing scenarios may exist for different second vehicles. For example, when the second vehicle is an ambulance, the first vehicle is in the slow lane, and the second vehicle cuts into the slow lane from the adjacent fast lane. Based on driving experience, the ambulance cutting into the slow lane from the fast lane can be considered to be an obstruction in the fast lane in front of the ambulance. In this case, the target driver's driving experience information will generate a decision result of not changing lanes. Alternatively, if an ambulance cuts into the host lane from the emergency lane, and according to traffic regulations, other vehicles except special vehicles are not allowed to enter the emergency lane, the target driver's driving experience information will also generate a decision result of not changing lanes.

[0114] In some embodiments, obtaining scene awareness information associated with the first vehicle and the second vehicle may include:

[0115] When detecting that the second vehicle changes lanes multiple times in a row toward the target direction, obtaining map information associated with the first vehicle and the second vehicle;

[0116] Determine the target scene characteristics based on scene cognition information, which may include:

[0117] In a case where it is determined based on the map information that the lane change behavior of the second vehicle is to enter a ramp on the road side corresponding to the target direction, a down-ramp lane change scene feature is determined, the target scene feature including the down-ramp lane change scene,

[0118] The lane change decision result is used to instruct the first vehicle to change lanes in a direction opposite to the target direction based on the target human driving experience.

[0119] In this embodiment, when a second vehicle is detected to have made multiple consecutive lane changes toward a target direction, map information associated with the first and second vehicles is obtained. Based on the map information, the second vehicle's lane change behavior is determined to be to enter a ramp on the road side corresponding to the target direction. Then, off-ramp lane change scenario characteristics are determined. The target scenario characteristics include an off-ramp lane change scenario. The lane change decision result is used to instruct the first vehicle to change lanes in a direction opposite to the target direction based on the target person's driving experience. This improves the rationality of lane change decision results and enhances traffic efficiency.

[0120] The above-mentioned obtaining of map information associated with the first vehicle and the second vehicle when it is detected that the second vehicle has changed lanes multiple times in a row toward the target direction means that if the second vehicle changes lanes in one direction at least twice in a row, the map information is used to check whether there is a ramp ahead of the direction in which the second vehicle changes lanes.

[0121] The above-mentioned determination of the off-ramp lane change scenario in the case where the lane change behavior of the second vehicle is determined based on map information to enter the ramp on the side of the road corresponding to the target direction means that if the second vehicle changes lanes in one direction at least twice in succession, and it is known through map information that there is a ramp ahead of the lane change direction of the second vehicle, it is considered that the purpose of the second vehicle's lane change is to perform an off-ramp lane change, and the target scenario characteristics corresponding to the off-ramp lane change scenario are obtained. Since the off-ramp lane change is usually accompanied by a deceleration behavior, it will cause the ego vehicle to be affected and the traffic efficiency to decrease. According to the target human driving experience information, after confirming that it is an off-ramp lane change scenario, a lane change decision result is generated to instruct the first vehicle to change lanes in the direction opposite to the target direction, so as to avoid the speed reduction behavior of the second vehicle in advance, thereby improving traffic efficiency.

[0122] The condition for determining multiple consecutive lane changes is that the driver changes lanes to the left or right at least twice in a row.

[0123] Exemplary, reference Figure 4As shown, the first vehicle is in the second lane on the right, and the second vehicle is in the leftmost lane. When the second vehicle continuously changes lanes to the right and the map information shows that there is a ramp exit in front of the right side of the road, a lane change decision result of changing lanes to the left in advance is generated based on the target person's driving experience information, which can avoid vehicles coming off the ramp more quickly and improve traffic efficiency.

[0124] It should be noted that, in the lane change decision result of changing lanes to the left in advance generated based on the target person's driving experience information, the lane change to the left can be one time or multiple times in succession until the second vehicle is located in the right lane of the vehicle.

[0125] It should be noted that the above are only some of the implementation methods of the present application. In addition to the above-mentioned lane change scenarios and off-ramp lane change scenarios, other lane change scenarios can also be judged in accordance with the preset driving logic based on the target person's driving experience to generate other lane change decision results based on driving experience.

[0126] In some embodiments, determining target human driving experience information that matches the lane change scenario in which the second vehicle is located based on target scenario characteristics may include:

[0127] Inputting the target scene features into a preset driving experience database to obtain human driving experience information that has a corresponding relationship with the target scene features in the driving experience database, where the driving experience database is a database including the corresponding relationships between different scene features and human driving experience information;

[0128] When human driving experience information having a corresponding relationship with the target scene feature is obtained in the driving experience database, the obtained human driving experience information is determined as target human driving experience information.

[0129] In this embodiment, the target scene features are first input into a preset driving experience database to obtain human driving experience information that corresponds to the target scene features. The driving experience database is a database that includes the correspondence between different scene features and human driving experience information. When human driving experience information that corresponds to the target scene features is obtained from the driving experience database, the obtained human driving experience information is determined as the target human driving experience information. This application matches the target scene features with the target human driving experience information through the driving experience database, which can improve the matching degree between the target human driving experience information and the corresponding scene, further reducing the probability of invalid lane changes.

[0130] The above-mentioned driving experience library refers to a scenario library based on driver behavior and driving experience, which may include some safety and comfort lane changes initiated by the driver's driving awareness, as well as cooperation with other road users and predictive lane changes.

[0131] The driving experience library refers to a database including corresponding relationships between different scene features and human driving experience information, and indicates that various different types of scenes and driving decision information that a driver may make according to experience in the scenes are stored in the driving experience library. According to the target scene feature, a lane changing scene corresponding to the target scene feature in the driving experience library can be found, and then human driving experience information in the lane changing scene is output.

[0132] In the case that the human driving experience information corresponding to the target scene feature in the driving experience library is obtained, the obtained human driving experience information is determined as the target human driving experience information, which indicates that if there is driving experience in the current corresponding scene in the driving experience library, the driving experience is output, and if there is no corresponding driving experience in the current scene, no output or a prompt information is output.

[0133] The construction process of the driving experience library corresponding to the lane changing scene provided by the present application is introduced as follows: first, typical lane changing scenes are extracted from a large number of test scene libraries, then cognitive rules are extracted and designed for the typical lane changing scenes, after the scene design, the scene features are labeled to obtain different target scene features, the human driving experience information corresponding to the typical lane changing scenes is constructed, for example, what kind of judgment and vehicle control the driver may make in a certain scene, the experience is data, and the human driving experience information is obtained, and then a driving experience library giving human driving experience is obtained according to the target scene feature and the corresponding human driving experience information.

[0134] Based on the vehicle lane changing control method provided in the above embodiment, the present application also provides a specific implementation mode of a vehicle lane changing control device.

[0135] As shown in Figure 5 The vehicle lane changing control device 200 provided by the embodiment of the present application can include:

[0136] The scene feature acquisition module 201 is configured to acquire a target scene feature in a case that a second vehicle located in front of a first vehicle is detected to have a lane changing behavior, the second vehicle is a vehicle having a lane changing behavior in front of the first vehicle, and the target scene feature is used to indicate a lane changing scene in which the second vehicle is located;

[0137] The driving experience determination module 202 is configured to determine target human driving experience information matched with the lane changing scene in which the second vehicle is located according to the target scene feature, and the target human driving experience information is used to describe target human driving experience conforming to a preset driving logic.

[0138] The lane changing decision generation module 203 is configured to generate a lane changing decision result according to the target human driving experience information, the lane changing decision result being used to instruct whether the first vehicle performs a lane changing behavior and a lane changing direction in the case where the first vehicle performs the lane changing behavior according to the target human driving experience.

[0139] The lane changing control execution module 204 is configured to execute lane changing control of the first vehicle according to the lane changing decision result.

[0140] The vehicle lane changing control apparatus 200 provided in the embodiments of the present application is used to execute the vehicle lane changing control method in the above embodiments, and the specific processing procedure is the same as that of the vehicle lane changing control method in the above embodiments, which will not be repeated here.

[0141] Figure 6 A hardware structure schematic diagram of an electronic device provided in the embodiments of the present application is shown.

[0142] The electronic device can include a processor 301 and a memory 302 having stored computer program instructions.

[0143] Specifically, the processor 301 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0144] The memory 302 can include a mass storage for data or instructions. By way of example and not limitation, the memory 302 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 302 can include removable or non-removable (or fixed) media. Where appropriate, the memory 302 can be internal or external to the integrated gateway disaster recovery device. In some embodiments, the memory 302 is a non-volatile solid-state memory.

[0145] In some embodiments, the memory 302 can include read-only memory (ROM), random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (e.g., by one or more processors), is operable to perform operations described with reference to the methods according to an aspect of the present disclosure.

[0146] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any one of the vehicle lane change control methods in the above embodiments.

[0147] In one example, the electronic device may further include a communication interface 303 and a bus 310. Figure 3 As shown, the processor 301 , the memory 302 , and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0148] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0149] Bus 310 includes hardware, software, or both, and couples the components of the online data traffic metering device to each other. By way of example, and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Area Network (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 310 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0150] In addition, in conjunction with the vehicle lane change control method in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the vehicle lane change control methods in the above embodiments is implemented.

[0151] The present application also relates to a vehicle, which is controlled to change lanes using the vehicle lane change control method in the above-mentioned embodiment. Specifically, the vehicle can be a private car, such as a sedan, SUV, MPV or pickup truck. The vehicle can also be an operating vehicle, such as a van, bus, small truck or large trailer. The vehicle can be a gasoline vehicle or a new energy vehicle. When the vehicle is a new energy vehicle, it can be a hybrid vehicle or a pure electric vehicle. The vehicle lane change control method can be run in a vehicle-mounted terminal, or it can be run in other terminal devices connected to the vehicle via Bluetooth, data cable or other communication methods, such as a mobile phone.

[0152] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0153] The functional blocks shown in the block diagrams described above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they may be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments may be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave over a transmission medium or communication link. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memory, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. Code segments may be downloaded via a computer network such as the Internet or an intranet.

[0154] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0155] Aspects of the present disclosure have been described above with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, as well as combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine such that execution of these instructions by the processor of the computer or other programmable data processing device enables the implementation of the functions / actions specified in one or more blocks in the flowcharts and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagrams and / or flowcharts, as well as combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware that performs the specified functions or actions, or by a combination of dedicated hardware and computer instructions.

[0156] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A method for controlling a vehicle lane change, characterized in that: The following steps are involved: When a second vehicle ahead of a first vehicle is detected to have changed lanes, a target scene feature is obtained, wherein the second vehicle is the vehicle ahead of the first vehicle that has changed lanes, and the target scene feature is used to indicate the lane change scene in which the second vehicle is located; Determining target human driving experience information that matches the lane change scenario in which the second vehicle is located based on the target scenario characteristics, wherein the target human driving experience information is used to describe the target human driving experience that conforms to a preset driving logic; generating a lane change decision result based on the target person's driving experience information, wherein the lane change decision result is used to indicate whether the first vehicle is to change lanes and the lane change direction if the lane change occurs, based on the target person's driving experience; Execute lane change control of the first vehicle according to the lane change decision result.

2. The vehicle lane change control method according to claim 1, characterized in that: The acquiring target scene features includes: Acquiring scene recognition information associated with the first vehicle and the second vehicle, the scene recognition information including at least one of traffic regulation information and map information; Determine the target scene characteristics based on the scene cognition information.

3. The vehicle lane change control method according to claim 2, characterized in that: The second vehicle is a target type vehicle, and the scene recognition information includes traffic rule information for describing a lane change rule for the target type vehicle; The determining the target scene feature according to the scene recognition information includes: When the speed of the second vehicle during the lane change is lower than the speed of the first vehicle, obtaining lane information of the lane in which the second vehicle was located before the lane change; A lane-cutting-in lane-changing scenario is determined according to the lane information and the traffic rule information, and the target scenario feature includes the lane-cutting-in lane-changing scenario.

4. The vehicle lane change control method according to claim 3, characterized in that: The second vehicle is a cargo vehicle; The performing lane change control of the first vehicle according to the lane change decision result includes: In the lane-changing scenario, if the truck is located in the rightmost lane of the road before changing lanes, preventing the first vehicle from changing lanes to the lane before the truck changes lanes according to the lane-changing decision result; or, In the lane-changing scenario, when the cargo vehicle is not located in the rightmost lane of the road before changing lanes, the first vehicle is controlled to change lanes to the lane before the cargo vehicle changed lanes according to the lane-changing decision result.

5. The vehicle lane change control method according to claim 2, characterized in that: The acquiring scene recognition information associated with the first vehicle and the second vehicle includes: When detecting that the second vehicle changes lanes multiple times in a row toward a target direction, obtaining map information associated with the first vehicle and the second vehicle; The determining the target scene feature according to the scene recognition information includes: In a case where it is determined based on the map information that the second vehicle's lane change behavior is to enter a ramp on the road side corresponding to the target direction, an off-ramp lane change scenario is determined, wherein the target scenario feature includes the off-ramp lane change scenario. The lane change decision result is used to instruct the first vehicle to change lanes in a direction opposite to the target direction based on the target human driving experience.

6. The vehicle lane change control method according to claim 1, characterized in that: The determining, based on the target scene feature, target human driving experience information that matches the lane change scene in which the second vehicle is located, includes: Inputting the target scene feature into a preset driving experience database to obtain human driving experience information corresponding to the target scene feature from the driving experience database, wherein the driving experience database is a database including correspondences between different scene features and human driving experience information; When human driving experience information having a corresponding relationship with the target scene feature is obtained from the driving experience database, the obtained human driving experience information is determined as target human driving experience information.

7. A vehicle lane change control device, characterized in that: include: a scene feature acquisition module, configured to acquire a target scene feature when a second vehicle located ahead of a first vehicle is detected to have changed lanes, wherein the second vehicle is the vehicle ahead of the first vehicle that has changed lanes, and the target scene feature is used to indicate the lane change scene in which the second vehicle is located; a driving experience determination module, configured to determine, based on the target scene characteristics, target human driving experience information that matches the lane change scene in which the second vehicle is located, wherein the target human driving experience information is used to describe the target human driving experience that conforms to a preset driving logic; a lane change decision generating module, configured to generate a lane change decision result based on the target person's driving experience information, wherein the lane change decision result is used to indicate whether the first vehicle is to change lanes and the lane change direction if the lane change occurs, based on the target person's driving experience; A lane change control execution module is used to execute lane change control of the first vehicle according to the lane change decision result.

8. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the vehicle lane change control method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the vehicle lane change control method according to any one of claims 1 to 6 is implemented.

10. A vehicle, characterized in that: The vehicle performs lane change control by using the vehicle lane change control method according to any one of claims 1 to 6.

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