Vehicle motion state recognition method and device

Abstract

The present application discloses a vehicle motion state recognition method and device, which relates to the field of autonomous driving. The method can predict the driving trajectory of other vehicles based on the position information and motion information of other vehicles, as well as the lane information of the lane where the other vehicles are located, the lane information of the first adjacent lane of the lane where the other vehicles are located, and the lane information of the second adjacent lane of the lane where the other vehicles are located, and obtain at least two predicted driving trajectories corresponding to the aforementioned at least two lane information. Then, based on the aforementioned at least two predicted driving trajectories and the lane information of the lane where the vehicle is located, the motion state of other vehicles relative to the vehicle can be identified, so that when monitoring the motion state of other vehicles relative to the vehicle, the sensitivity to the instantaneous kinematic information of other vehicles can be effectively reduced, and the misidentification of the motion state of other vehicles can be reduced.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of autonomous driving, and in particular to a method and device for identifying the motion state of a vehicle. Background Art

[0002] In the field of vehicle autonomous driving and assisted driving, vehicles need to know the movement status of other vehicles on the road in a timely and accurate manner, such as whether other vehicles are changing lanes, so as to perform path planning, adaptive cruise control, and reasonable deceleration in emergency situations based on the movement status of other vehicles. Therefore, the vehicle needs to monitor the movement status of other vehicles on the road during driving.

[0003] At present, vehicles can collect information such as the turn signals of other vehicles, the lateral distance between other vehicles and the lane lines of their lanes, the speed and acceleration of other vehicles through sensors, and predict the driving trajectories of other vehicles based on the collected information; then, the vehicle can identify whether other vehicles are changing lanes based on the predicted driving trajectories of other vehicles. Alternatively, vehicles can also collect lane line information of other vehicles' lanes, the lateral distance and heading angle between other vehicles and the lane lines of their lanes through sensors, and determine the actual driving trajectories of other vehicles based on the collected information, and then compare the actual driving trajectories of other vehicles with the driving trajectories of vehicles that normally maintain lane driving in the vehicle driving database. When the distance between the two is greater than a set threshold, it is considered that the other vehicle is changing lanes.

[0004] However, in the above-mentioned current methods of monitoring the motion status of other vehicles, when predicting the driving trajectory of other vehicles, or when comparing the actual driving trajectory of other vehicles with the driving trajectory of vehicles that normally maintain lane driving in the vehicle driving database, they are all relatively sensitive to the instantaneous kinematic information of other vehicles, which can easily lead to misidentification of the motion status of other vehicles. Summary of the invention

[0005] The embodiments of the present application provide a method and device for identifying the motion state of a vehicle, which can effectively reduce the sensitivity to the instantaneous kinematic information of other vehicles when monitoring the motion state of other vehicles, thereby reducing the misidentification of the motion state of other vehicles.

[0006] In a first aspect, an embodiment of the present application provides a method for identifying a vehicle motion state, the method comprising: acquiring first lane information, second lane information, and position information and motion information of a second vehicle; the first lane information is the lane information of a lane where the first vehicle is located; the second lane information comprises at least two of the lane information of a lane where the second vehicle is located, the lane information of a first adjacent lane to the lane where the second vehicle is located, and the lane information of a second adjacent lane to the lane where the second vehicle is located; determining at least two predicted driving trajectories of the second vehicle based on the second lane information and the position information and motion information of the second vehicle; each predicted driving trajectory corresponds to each type of lane information contained in the second lane information; determining the motion state of the second vehicle relative to the first vehicle based on the at least two predicted driving trajectories and the first lane information.

[0007] The position information of the second vehicle may be the relative position of the second vehicle relative to the first vehicle, such as relative position coordinates. Alternatively, the position information of the second vehicle may be the absolute position of the second vehicle, such as the real (absolute) position coordinates of the second vehicle may be determined based on the position coordinates of the first vehicle and the relative position coordinates of the second vehicle relative to the first vehicle.

[0008] The motion information of the second vehicle may include: kinematic information such as the speed, acceleration, and trajectory curvature of the second vehicle.

[0009] Each predicted driving trajectory corresponds to each lane information included in the second lane information, which means that when the second lane information includes two lane information, two predicted driving trajectories corresponding to the two lane information can be determined. When the second lane information includes three lane information, three predicted driving trajectories corresponding to the three lane information can be determined.

[0010] For example, when the second lane information includes lane information of the lane where the second vehicle is located and lane information of the first adjacent lane to which the second vehicle is located, the predicted driving trajectory of the second vehicle maintaining driving in the current lane and the predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane to which the second vehicle is located can be determined accordingly.

[0011] Alternatively, when the second lane information includes lane information of the lane where the second vehicle is located and lane information of the second adjacent lane of the lane where the second vehicle is located, the predicted driving trajectory of the second vehicle maintaining driving in the current lane and the predicted driving trajectory of the second vehicle changing lanes to the second adjacent lane of the lane can be determined accordingly.

[0012] Alternatively, when the second lane information may include lane information of a first adjacent lane of the lane where the second vehicle is located, and lane information of a second adjacent lane of the lane where the second vehicle is located, the predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane of its lane and the predicted driving trajectory of the second vehicle changing lanes to the second adjacent lane of its lane may be determined accordingly.

[0013] Alternatively, when the second lane information includes all three of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located, the predicted driving trajectory of the second vehicle maintaining driving in the current lane, the predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane of its lane, and the predicted driving trajectory of the second vehicle changing lanes to the second adjacent lane of its lane can be determined accordingly.

[0014] The method can predict the driving trajectory of the second vehicle based on the position information and motion information of the second vehicle, as well as at least two of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located, and obtain at least two predicted driving trajectories corresponding to the aforementioned at least two lane information. Then, the motion state of the second vehicle relative to the first vehicle can be identified based on the aforementioned at least two predicted driving trajectories and the lane information of the lane where the first vehicle is located, so that when monitoring the motion state of the second vehicle relative to the first vehicle, the sensitivity to the instantaneous kinematic information of the second vehicle can be effectively reduced, and the misidentification of the motion state of the second vehicle can be reduced.

[0015] Among them, the first vehicle can be understood as the vehicle that is automatically driven or assisted driven, and the second vehicle can be understood as other vehicles traveling on the road relative to the vehicle.

[0016] In one possible design, determining the motion state of the second vehicle relative to the first vehicle based on at least two predicted driving trajectories and the first lane information includes: determining the target predicted driving trajectory of the second vehicle based on the at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle; determining the motion state of the second vehicle relative to the first vehicle based on the target predicted driving trajectory and the first lane information.

[0017] In the present design, the target predicted driving trajectory of the second vehicle is determined based on at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle. This can make the determined target predicted driving trajectory of the second vehicle closer to the actual driving trajectory of the second vehicle in the future, thereby improving the accuracy of the subsequent motion state of the second vehicle relative to the first vehicle determined based on the target predicted driving trajectory and the first lane information.

[0018] In one possible design, before determining the target predicted driving trajectory of the second vehicle based on at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle, the method also includes: obtaining a first lateral coordinate vector of the historical driving trajectory of the second vehicle within a first preset time period; wherein the first lateral coordinate vector is used to indicate the distance vector between the historical driving trajectory and the lane line of the lane where the second vehicle is located; for each predicted driving trajectory: obtaining a second lateral coordinate vector of the predicted driving trajectory within the second preset time period, and determining the similarity between the predicted driving trajectory and the historical driving trajectory based on the second lateral coordinate vector and the first lateral coordinate vector; wherein the second lateral coordinate vector is used to indicate the distance vector between the predicted driving trajectory and the lane line of the lane where the second vehicle is located.

[0019] The first lateral coordinate vector of the historical driving trajectory of the second vehicle in the first preset time period includes a plurality of first lateral coordinates of the historical driving trajectory of the second vehicle in the first preset time period. The second lateral coordinate vector of the predicted driving trajectory in the second preset time period includes a plurality of second lateral coordinates of the predicted driving trajectory in the second preset time period.

[0020] In some embodiments, the first preset time period may be a time period consisting of a plurality of consecutive first preset periods, each of which may include at least one first horizontal coordinate. The second preset time period may be a time period consisting of a plurality of consecutive second preset periods, each of which may include at least one second horizontal coordinate.

[0021] Alternatively, in some other possible designs, other similarity calculation methods may be used to determine the similarity between each of the at least two predicted driving trajectories and the historical driving trajectory of the second vehicle, which is not limited here.

[0022] In one possible design, determining the target predicted driving trajectory of the second vehicle based on at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle includes: predicting the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle based on the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle; determining the target predicted driving trajectory of the second vehicle based on at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle.

[0023] For example, the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle can be normalized, and the probability of each predicted driving trajectory can be predicted, which can indicate the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle; based on at least two predicted driving trajectories and the probability of each predicted driving trajectory, the target predicted driving trajectory of the second vehicle is determined.

[0024] In the present design, by predicting the similarity between each of the at least two predicted driving trajectories and the actual driving trajectory of the second vehicle, the target predicted driving trajectory of the second vehicle can be determined based on the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle and taking into account the at least two predicted driving trajectories, so that the switching or transformation of the motion state of the second vehicle in the determined target predicted driving trajectory can have better continuity.

[0025] In a possible design, the determining the motion state of the second vehicle relative to the first vehicle based on the target predicted driving trajectory and the first lane information includes: determining the minimum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length based on the target predicted driving trajectory and the first lane information. If the minimum lateral distance is greater than a first threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is to maintain driving in a different lane from the first vehicle.

[0026] This design can determine whether the motion state of the second vehicle relative to the first vehicle is to maintain a different lane from the first vehicle by judging the minimum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length, relative to the size of the first threshold.

[0027] In a possible design, the method further includes: determining the maximum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length according to the target predicted driving trajectory and the first lane information; if the minimum lateral distance is less than the first threshold and the maximum lateral distance is greater than the second threshold, then determining that the motion state of the second vehicle relative to the first vehicle is crossing the lane where the first vehicle is located; wherein the second threshold is greater than the first threshold. If the maximum lateral distance is less than the third threshold, then determining that the motion state of the second vehicle relative to the first vehicle is maintaining driving in the same lane as the first vehicle; wherein the third threshold is less than the first threshold. If the minimum lateral distance is less than the first threshold, and the maximum lateral distance is greater than the third threshold and less than the second threshold, then determining that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located, or cutting out of the lane where the first vehicle is located.

[0028] This design can further refine the motion state of the second vehicle relative to the first vehicle by judging the relative sizes of the maximum lateral distance and the second threshold and the third threshold when the minimum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length is less than a first threshold.

[0029] In a possible design, the method further includes: obtaining a first longitudinal position corresponding to the minimum lateral distance, and a second longitudinal position corresponding to the maximum lateral distance. When the minimum lateral distance is less than the first threshold, and the maximum lateral distance is greater than the third threshold, and less than the second threshold, the determination of the motion state of the second vehicle relative to the first vehicle as cutting into the lane where the first vehicle is located, or cutting out of the lane where the first vehicle is located, includes: if the time when the second longitudinal position appears in the target predicted driving trajectory is earlier than the time when the first longitudinal position appears, then determining the motion state of the second vehicle relative to the first vehicle as cutting into the lane where the first vehicle is located. If the time when the first longitudinal position appears in the target predicted driving trajectory is earlier than the time when the second longitudinal position appears, then determining the motion state of the second vehicle relative to the first vehicle as cutting out of the lane where the first vehicle is located.

[0030] This design can distinguish the motion state of the second vehicle relative to the first vehicle as cutting in or out of the lane where the first vehicle is located based on the first longitudinal position corresponding to the minimum lateral distance and the second longitudinal position corresponding to the maximum lateral distance, and the order of their appearance in the target predicted driving trajectory.

[0031] In a possible design, the method further includes: obtaining a longitudinal distance between the first longitudinal position and a first end of a preset longitudinal length; wherein the longitudinal distance is the distance between the first longitudinal position and the first end of the preset longitudinal length in a direction parallel to the lane line of the lane where the first vehicle is located, and the first end of the preset longitudinal length is an end close to the first vehicle. If the longitudinal distance is less than a fourth threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is to cut into the lane where the first vehicle is located according to a first cut-in state, or to cut out of the lane where the first vehicle is located according to a first cut-out state. If the longitudinal distance is greater than the fourth threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is to cut into the lane where the first vehicle is located according to a second cut-in state, or to cut out of the lane where the first vehicle is located according to a second cut-out state.

[0032] For example, the first cut-in state can be called the tight cut-in state, and the first cut-out state can be called the tight cut-out state. The second cut-in state can be called the normal cut-in state, and the second cut-out state can be called the normal cut-out state. Alternatively, the first / second cut-in state and the first / second cut-out state can be called other names.

[0033] Taking the first cut-in state as called the tight cut-in state, the first cut-out state as called the tight cut-out state, the second cut-in state as called the normal cut-out state, and the second cut-out state as called the normal cut-out state as an example, in the present design, the movement state of the second vehicle relative to the first vehicle can be further refined from cutting into the lane where the first vehicle is located to: tight cut-in or normal cut-in, or the movement state of the second vehicle relative to the first vehicle can be refined from cutting out of the lane where the first vehicle is located to: tight cut-in or normal cut-out, based on the longitudinal distance between the first longitudinal position and the first end of the preset longitudinal length, relative to the size of the fourth threshold.

[0034] In one possible design, the motion information of the second vehicle includes a lateral speed of the second vehicle; before determining at least two predicted driving trajectories of the second vehicle based on the second lane information, and the position information and motion information of the second vehicle, the method also includes: correcting the lateral speed included in the motion information of the second vehicle based on the average historical lateral speed of the second vehicle.

[0035] This design can reduce the disturbance caused by the excessive instantaneous speed of the current lateral speed when determining the predicted driving trajectory of the second vehicle by correcting the lateral speed included in the motion information of the second vehicle according to the average historical lateral speed of the second vehicle.

[0036] In a second aspect, an embodiment of the present application provides a vehicle motion state recognition device, the device comprising: an acquisition module, used to acquire first lane information, second lane information, and position information and motion information of a second vehicle; the first lane information is the lane information of the lane where the first vehicle is located; the second lane information includes at least two of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located. A prediction module, used to determine at least two predicted driving trajectories of the second vehicle based on the second lane information, and the position information and motion information of the second vehicle; each predicted driving trajectory corresponds to each type of lane information contained in the second lane information. A determination module, used to determine the motion state of the second vehicle relative to the first vehicle based on at least two predicted driving trajectories and the first lane information.

[0037] In one possible design, the determination module is specifically used to determine the target predicted driving trajectory of the second vehicle based on at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle; and determine the motion state of the second vehicle relative to the first vehicle based on the target predicted driving trajectory and the first lane information.

[0038] In one possible design, the determination module is also used to obtain a first lateral coordinate vector of a historical driving trajectory of the second vehicle within a first preset time period; wherein the first lateral coordinate vector is used to indicate a distance vector between the historical driving trajectory and a lane line of a lane where the second vehicle is located; for each predicted driving trajectory: obtain a second lateral coordinate vector of the predicted driving trajectory within the second preset time period, and determine the similarity between the predicted driving trajectory and the historical driving trajectory based on the second lateral coordinate vector and the first lateral coordinate vector; wherein the second lateral coordinate vector is used to indicate a distance vector between the predicted driving trajectory and the lane line of the lane where the second vehicle is located.

[0039] In one possible design, the determination module is specifically used to predict the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle based on the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle; and determine the target predicted driving trajectory of the second vehicle based on at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle.

[0040] In one possible design, the determination module is specifically used to determine the minimum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length based on the target predicted driving trajectory and the first lane information; if the minimum lateral distance is greater than the first threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is to maintain driving in a different lane from the first vehicle.

[0041] In a possible design, the determination module is also used to determine the maximum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length based on the target predicted driving trajectory and the first lane information; if the minimum lateral distance is less than the first threshold and the maximum lateral distance is greater than the second threshold, the motion state of the second vehicle relative to the first vehicle is determined to be crossing the lane where the first vehicle is located; wherein the second threshold is greater than the first threshold. If the maximum lateral distance is less than the third threshold, the motion state of the second vehicle relative to the first vehicle is determined to be maintaining driving in the same lane as the first vehicle; wherein the third threshold is less than the first threshold. If the minimum lateral distance is less than the first threshold, the maximum lateral distance is greater than the third threshold and less than the second threshold, the motion state of the second vehicle relative to the first vehicle is determined to be cutting into the lane where the first vehicle is located, or cutting out of the lane where the first vehicle is located.

[0042] In a possible design, the determination module is also used to obtain a first longitudinal position corresponding to the minimum lateral distance and a second longitudinal position corresponding to the maximum lateral distance; when the minimum lateral distance is less than a first threshold, and the maximum lateral distance is greater than a third threshold and less than the second threshold, if the time when the second longitudinal position appears in the target predicted driving trajectory is earlier than the time when the first longitudinal position appears, then the motion state of the second vehicle relative to the first vehicle is determined to be cutting into the lane where the first vehicle is located; if the time when the first longitudinal position appears in the target predicted driving trajectory is earlier than the time when the second longitudinal position appears, then the motion state of the second vehicle relative to the first vehicle is determined to be cutting out of the lane where the first vehicle is located.

[0043] In one possible design, the determination module is also used to obtain the longitudinal distance between the first longitudinal position and the first end of the preset longitudinal length; wherein the longitudinal distance is the distance between the first longitudinal position and the first end of the preset longitudinal length in a direction parallel to the lane line of the lane where the first vehicle is located, and the first end of the preset longitudinal length is an end close to the first vehicle; if the longitudinal distance is less than a fourth threshold, then the movement state of the second vehicle relative to the first vehicle is determined to be cutting into the lane where the first vehicle is located according to the first cutting-in state, or cutting out of the lane where the first vehicle is located according to the first cutting-out state; if the longitudinal distance is greater than the fourth threshold, then the movement state of the second vehicle relative to the first vehicle is determined to be cutting into the lane where the first vehicle is located according to the second cutting-in state, or cutting out of the lane where the first vehicle is located according to the second cutting-out state.

[0044] In one possible design, the motion information of the second vehicle includes a lateral velocity of the second vehicle; the prediction module is further used to correct the lateral velocity included in the motion information of the second vehicle according to an average historical lateral velocity of the second vehicle.

[0045] In a third aspect, an embodiment of the present application also provides a vehicle motion state identification device, comprising: an interface circuit for receiving data transmitted by other devices; a processor, connected to the interface circuit and used to execute the method described in the first aspect or any possible design thereof.

[0046] In a fourth aspect, an embodiment of the present application provides a vehicle, comprising: a processor, the processor being used to be connected to a memory and call a program stored in the memory to execute a method as described in the first aspect or any possible design thereof.

[0047] In a fifth aspect, an embodiment of the present application provides a server, comprising: a processor, the processor being used to connect to a memory and call a program stored in the memory to execute a method as described in the first aspect or any possible design thereof.

[0048] In a sixth aspect, an embodiment of the present application provides a vehicle driving system, such as an automatic driving system or an assisted driving system, comprising a processor, the processor being used to connect to a memory and call a program stored in the memory to execute a method as described in the first aspect or any possible design thereof.

[0049] In the seventh aspect, an embodiment of the present application provides a computer-readable storage medium, including: computer software instructions; when the computer software instructions are executed in a vehicle motion state identification device or in a chip built into the vehicle motion state identification device, the vehicle motion state identification device executes the method described in the first aspect or any possible design thereof.

[0050] In an eighth aspect, an embodiment of the present application further provides a computer program product, which, when executed, can implement the method described in the first aspect or any possible design of the first aspect.

[0051] In the ninth aspect, an embodiment of the present application also provides a chip system, which is applied to a vehicle, a server, or a driving system. The chip system includes one or more interface circuits and one or more processors; the interface circuit and the processor are interconnected by lines; the processor receives and executes computer instructions from the memory of the electronic device through the interface circuit to implement a method as described in the first aspect or any possible design of the first aspect.

[0052] It can be understood that the beneficial effects that can be achieved by the second to ninth aspects provided above can refer to the beneficial effects in the first aspect and any possible design method thereof, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram showing an application scenario provided by an embodiment of the present application is shown;

[0054] Figure 2 A schematic diagram showing the change in the motion state of vehicle 2 relative to vehicle 1;

[0055] Figure 3 A schematic diagram showing a change in the motion state of vehicle 4 relative to vehicle 1;

[0056] Figure 4 A schematic diagram showing vehicle A identifying the motion state of vehicle B by predicting the driving trajectory of vehicle B;

[0057] Figure 5 Another schematic diagram showing vehicle A identifying the motion state of vehicle B by predicting the driving trajectory of vehicle B;

[0058] Figure 6 A schematic diagram showing that vehicle A identifies the motion state of vehicle B by comparing the driving trajectory of vehicles that normally maintain lane driving in a vehicle driving database;

[0059] Figure 7 Another schematic diagram showing that vehicle A identifies the motion state of vehicle B by comparing the driving trajectory of vehicles that normally maintain lane driving in the vehicle driving database;

[0060] Figure 8 A schematic diagram of the composition of a vehicle provided in an embodiment of the present application is shown;

[0061] Fig. 9 A schematic diagram of a process flow of a vehicle motion state recognition method provided by an embodiment of the present application is shown;

[0062] Fig.10 Another schematic diagram of the process of identifying the vehicle motion state provided by an embodiment of the present application is shown;

[0063] Fig.11 A schematic diagram showing that vehicle A according to an embodiment of the present application identifies the motion state of vehicle B according to the target predicted driving trajectory of vehicle B;

[0064] Fig.12 Another schematic diagram showing that vehicle A identifies the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application;

[0065] Fig.13 Another schematic diagram showing that vehicle A identifies the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application;

[0066] Fig.14 Another schematic diagram showing that vehicle A identifies the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application;

[0067] Fig.15 Another schematic diagram showing that vehicle A identifies the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application;

[0068] Fig.16 Another schematic diagram showing that vehicle A identifies the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application;

[0069] Fig.17 Another schematic diagram showing that vehicle A identifies the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application;

[0070] Fig.18 A schematic diagram of the structure of a vehicle motion state recognition device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0071] Figure 1 A schematic diagram of an application scenario provided by an embodiment of the present application is shown.

[0072] like Figure 1 As shown, the application scenario of the embodiment of the present application may include multiple vehicles traveling on the road. Figure 1 Four vehicles are exemplarily shown, including vehicle 1, vehicle 2, vehicle 3 and vehicle 4. Different vehicles may travel in different lanes, such as vehicle 1 traveling in lane 1, vehicle 2 traveling in lane 2, vehicle 3 traveling in lane 3, etc. Alternatively, different vehicles may travel in the same lane, such as vehicle 1 and vehicle 4 traveling together in lane 1.

[0073] In the field of vehicle autonomous driving and assisted driving, Figure 1 For a vehicle in the application scenario shown, it needs to timely and accurately know the movement status of other vehicles on the road, such as whether other vehicles are changing lanes, so as to perform path planning, adaptive cruise control, and reasonable deceleration in emergency situations according to the movement status of other vehicles.

[0074] by Figure 1 In the application scenario shown in FIG. 1 , vehicle 1 monitors the motion state of vehicle 2. Figure 2 FIG. 2 is a schematic diagram showing the change in the motion state of vehicle 2 relative to vehicle 1. Figure 2 As shown, during the driving process of vehicle 1 and vehicle 2, vehicle 2 may cut into the lane where vehicle 1 is located, such as cutting into lane 1 from lane 2. Vehicle 1 needs to monitor the change of the motion state of vehicle 2 in time to plan the path of the vehicle, decelerate reasonably, etc.

[0075] Or, Figure 1In the application scenario shown in FIG. 1 , vehicle 1 monitors the motion state of vehicle 4 as an example. Figure 3 FIG. 4 is a schematic diagram showing the change in the motion state of vehicle 4 relative to vehicle 1. Figure 3 As shown, during the driving process of vehicle 1 and vehicle 4, vehicle 4 may switch from the lane where vehicle 1 is located to another lane, such as switching from lane 1 to lane 2. At this time, vehicle 1 also needs to monitor the change of the motion state of vehicle 4 in time to plan the path of the vehicle.

[0076] Currently, there are two ways to monitor the motion status of other vehicles.

[0077] In one method, a vehicle can use sensors to collect information such as the turn signals of other vehicles, the lateral distance between other vehicles and the lane lines of their lanes, the speed and acceleration of other vehicles, and predict the driving trajectories of other vehicles based on the collected aforementioned information; then, the vehicle can identify whether other vehicles are changing lanes based on the predicted driving trajectories of other vehicles.

[0078] For example, the other vehicle is vehicle B and the vehicle is vehicle A: Figure 4 FIG. 2 shows a schematic diagram of vehicle A identifying the motion state of vehicle B by predicting the driving trajectory of vehicle B. Figure 4 As shown, when vehicle A recognizes that vehicle B will drive out of its lane and enter another lane based on the predicted driving trajectory of vehicle B, it can be considered that vehicle B may change lanes.

[0079] However, in this method, when predicting the driving trajectory of other vehicles, the instantaneous kinematic information of other vehicles is relatively sensitive, which can easily lead to misidentification of the motion status of other vehicles.

[0080] Take the other vehicle as vehicle B and the vehicle as vehicle A as an example: Figure 5 Another schematic diagram is shown in which vehicle A identifies the motion state of vehicle B by predicting the driving trajectory of vehicle B. Figure 5 As shown, when the driver of vehicle B makes an error in operation, causing vehicle B to suddenly deviate, vehicle A may recognize that vehicle B will change lanes based on the predicted driving trajectory of vehicle B, but in fact vehicle B may continue to stay in the original lane, causing vehicle A to misrecognize the movement state of vehicle B.

[0081] In another way, the vehicle can use sensors to collect lane line information of other vehicles' lanes, lateral distances and heading angles between other vehicles and their lane lines, and other information, and determine the actual driving trajectory of other vehicles based on the collected information. Then, the actual driving trajectory of other vehicles is compared with the driving trajectory of vehicles that normally maintain lane driving in the vehicle driving database. When the distance between the two is greater than a set threshold, it is considered that the other vehicle is changing lanes.

[0082] Let's continue with the example of other vehicle being vehicle B and this vehicle being vehicle A: Figure 6 FIG. 1 is a schematic diagram showing that vehicle A identifies the motion state of vehicle B by comparing the driving trajectory of vehicles that normally maintain lane driving in the vehicle driving database. Figure 6 As shown, when vehicle A recognizes that the distance between the actual driving trajectory of vehicle B and the driving trajectory of vehicles that normally maintain lane driving in the vehicle driving database exceeds a certain threshold, it can be considered that vehicle B may change lanes.

[0083] However, in this method, when comparing the actual driving trajectory of other vehicles with the driving trajectory of vehicles that normally maintain lanes in the vehicle driving database, it is also sensitive to the instantaneous kinematic information of other vehicles, and it is also easy to cause misidentification of the movement status of other vehicles.

[0084] Take the other vehicle as vehicle B and the vehicle as vehicle A as an example: Figure 7 Another schematic diagram shows that vehicle A identifies the motion state of vehicle B by comparing the driving trajectory of vehicles that normally maintain lane driving in the vehicle driving database. Figure 7 As shown, when the driver of vehicle B makes an error in operation, causing vehicle B to suddenly deviate, the distance between the actual driving trajectory of vehicle B at the time of the current deviation and the driving trajectory of vehicles that normally maintain lane driving in the vehicle driving database may exceed a certain threshold. Vehicle A may recognize that vehicle B will change lanes, but in fact vehicle B may continue to maintain its original lane, which may also cause vehicle A to misrecognize the movement state of vehicle B.

[0085] It can be seen from the above that the current methods for identifying the motion status of other vehicles are all sensitive to the instantaneous kinematic information of other vehicles, which can easily lead to misidentification of the motion status of other vehicles.

[0086] Based on this, an embodiment of the present application provides a method for identifying the motion state of a vehicle. Through this method, the driving trajectory of other vehicles can be predicted based on at least two of the lane information of the lane where other vehicles are located, the lane information of the first adjacent lane of the lane where other vehicles are located (such as the left adjacent lane), and the lane information of the second adjacent lane of the lane where other vehicles are located (such as the right adjacent lane), and combined with the position information and motion information of other vehicles, to obtain at least two predicted driving trajectories corresponding to the aforementioned at least two lane information. Then, the motion state of other vehicles relative to the vehicle can be identified based on the aforementioned at least two predicted driving trajectories and the lane information of the lane where the vehicle is located. For example, it is identified whether other vehicles will change lanes to the lane where the vehicle is located.

[0087] Taking the vehicle as the first vehicle and the other vehicle as the second vehicle as an example, the vehicle motion state recognition method may include: obtaining first lane information, second lane information, and position information and motion information of the second vehicle. The first lane information is the lane information of the lane where the first vehicle is located; the second lane information includes at least two of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located. According to the second lane information, and the position information and motion information of the second vehicle, at least two predicted driving trajectories of the second vehicle are determined, and each predicted driving trajectory corresponds to each type of lane information contained in the second lane information. According to the at least two predicted driving trajectories and the first lane information, the motion state of the second vehicle relative to the first vehicle is determined.

[0088] In the vehicle motion state identification method provided by the embodiment of the present application, since the second lane information includes at least two of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located, at least two predicted driving trajectories corresponding to each type of lane information contained in the second lane information can be determined in combination with the position information and motion information of the second vehicle. When determining the motion state of the second vehicle relative to the first vehicle based on the at least two predicted driving trajectories and the first lane information, the at least two predicted driving trajectories can play a role of mutual correction, thereby reducing the sensitivity to the instantaneous kinematic information of the second vehicle, thereby avoiding misidentification of the motion state of the second vehicle relative to the first vehicle due to instantaneous changes in the kinematic information of the second vehicle.

[0089] The following is an exemplary description of the vehicle motion state recognition method provided in an embodiment of the present application.

[0090] It should be noted that, in the description of the present application, the words "first", "second", etc. are only used to distinguish the description and are not used to specifically limit a certain feature. In the description of the embodiments of the present application, "and / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. At least one referred to in the present application refers to one or more; multiple refers to two or more.

[0091] Figure 8 FIG. 1 shows a schematic diagram of a vehicle provided in an embodiment of the present application. Figure 8 As shown, the vehicle may include various subsystems, such as a travel system 102 , a sensor system 104 , a control system 106 , one or more peripheral devices 108 , a power source 110 , a computer system 112 , a user interface 116 , and the like.

[0092] Optionally, the vehicle may also include more or fewer subsystems, and each subsystem may include multiple elements. In addition, each subsystem and element of the vehicle may be interconnected by wire or wirelessly.

[0093] The travel system 102 may include components that provide powered movement for the vehicle. In one embodiment, the travel system 102 may include an engine 118, an energy source 119, a transmission 120, and wheels / tires 121. The engine 118 may be an internal combustion engine, an electric motor, an air compression engine, or a combination of other types of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air compression engine. The engine 118 converts the energy source 119 into mechanical energy.

[0094] Examples of energy source 119 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. Energy source 119 may also provide energy to other systems of the vehicle.

[0095] The transmission 120 can transmit mechanical power from the engine 118 to the wheels 121. The transmission 120 may include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission 120 may also include other devices, such as a clutch. Among them, the drive shaft may include one or more shafts that can be coupled to one or more wheels 121.

[0096] The sensor system 104 may include several sensors that sense information about the environment around the vehicle. For example, the sensor system 104 may include a positioning system 122 (the positioning system 122 may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130. The sensor system 104 may also include sensors for the vehicle's internal systems, such as an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc. Sensor data from one or more of these sensors may be used to detect objects and their corresponding characteristics (position, shape, direction, speed, etc.).

[0097] Positioning system 122 can be used to estimate the geographic location of the vehicle. IMU 124 is used to sense the position and orientation changes of the vehicle based on inertial acceleration. In one embodiment, IMU 124 can be a combination of an accelerometer and a gyroscope.

[0098] Radar 126 can utilize radio signals to sense objects within the surrounding environment of the vehicle. In some embodiments, in addition to sensing objects, radar 126 can also be used to sense the speed and / or heading of the object.

[0099] The laser rangefinder 128 may utilize laser light to sense objects in the environment that the vehicle is located in. In some embodiments, the laser rangefinder 128 may include one or more laser sources, a laser scanner, and one or more detectors, among other system components.

[0100] The camera 130 may be used to capture multiple images of the vehicle's surroundings. The camera 130 may be a still camera or a video camera.

[0101] The control system 106 is an operating system for controlling the vehicle and its components. The control system 106 may include a steering system 132 , a throttle 134 , a brake unit 136 , a computer vision system 138 , a path control system 140 , and an obstacle avoidance system 142 .

[0102] The steering system 132 is operable to adjust the vehicle's forward direction. For example, in one embodiment, it can be a steering wheel system.

[0103] The throttle 134 is used to control the operating speed of the engine 118 and, in turn, the speed of the vehicle.

[0104] The brake unit 136 is used to control the vehicle to decelerate. The brake unit 136 can use friction to slow down the wheel 121. In other embodiments, the brake unit 136 can convert the kinetic energy of the wheel 121 into electric current. The brake unit 136 can also take other forms to slow down the rotation speed of the wheel 121 to control the speed of the vehicle.

[0105] The computer vision system 138 may be operable to process and analyze images captured by the camera 130 in order to identify objects and / or features in the vehicle's surrounding environment. The objects and / or features may include traffic signs, road boundaries, and obstacles. The computer vision system 138 may use object recognition algorithms, structure from motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 138 may be used to map the environment, track objects, estimate the speed of objects, and the like.

[0106] The route control system 140 is used to determine the driving route of the vehicle. In some embodiments, the route control system 140 can combine data from the sensor system 104 and one or more predetermined maps to determine the driving route for the vehicle.

[0107] The obstacle avoidance system 142 is used to identify, evaluate, and avoid or otherwise negotiate potential obstacles in the vehicle's environment.

[0108] Of course, in one example, the control system 106 may include additional or alternative components other than those shown and described, or may also reduce some of the components shown above.

[0109] The vehicle interacts with external sensors, other vehicles, other computer systems, or users through peripherals 108. Peripherals 108 may include a wireless communication system 146, an onboard computer 148, a microphone 150, and / or a speaker 152.

[0110] In some embodiments, the peripheral devices 108 provide a means for a user of the vehicle to interact with the user interface 116. For example, the onboard computer 148 can provide information to the user of the vehicle. The user interface 116 can also operate the onboard computer 148 to receive input from the user. The onboard computer 148 can be operated via a touch screen. In other cases, the peripheral devices 108 can provide a means for the vehicle to communicate with other devices located in the vehicle. For example, the microphone 150 can receive audio (e.g., voice commands or other audio input) from the user of the vehicle. Similarly, the speaker 152 can output audio to the user of the vehicle.

[0111] The wireless communication system 146 can communicate wirelessly with one or more devices directly or via a communication network. For example, the wireless communication system 146 can use 3G cellular communication, such as code division multiple access (CDMA), EVDO, GSM / GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system 146 can communicate with a wireless local area network (WLAN) using WiFi. In some embodiments, the wireless communication system 146 can communicate directly with the device using an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system 146 may include one or more dedicated short range communications (DSRC) devices, which may include public and / or private data communications between vehicles and / or roadside stations.

[0112] The power source 110 can provide power to various components of the vehicle. In one embodiment, the power source 110 can be a rechargeable lithium ion or lead acid battery. One or more battery packs of such batteries can be configured as a power source to provide power to various components of the vehicle. In some embodiments, the power source 110 and the energy source 119 can be implemented together, such as in some all-electric vehicles.

[0113] Some or all functions of the vehicle are controlled by a computer system 112. The computer system 112 may include at least one processor 113 that executes instructions 115 stored in a non-transitory computer-readable medium such as a memory 114. The computer system 112 may also be a plurality of computing devices that control individual components or subsystems of the vehicle in a distributed manner.

[0114] Processor 113 may be any conventional processor, such as a commercially available CPU. Alternatively, the processor may be a dedicated device such as an ASIC or other hardware-based processor. Figure 8 The processor and memory are functionally illustrated, but it should be understood by those skilled in the art that the processor or memory may actually include multiple processors or memories that may or may not be stored in the same physical housing. For example, the memory may be a hard drive or other storage medium located in a different physical housing. Therefore, references to processors will be understood to include references to a collection of processors or memories that may or may not operate in parallel. Different from using a single processor to perform the steps described herein, some components such as steering components and deceleration components can each have their own processor, which only performs calculations related to component-specific functions.

[0115] In various aspects described herein, the processor may be located remote from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle and others are performed by a remote processor, including taking the necessary steps to perform a single maneuver.

[0116] In some embodiments, the memory 114 may contain instructions 115 (e.g., program logic) that may be executed by the processor 113 to perform various functions of the vehicle, including those described above. The memory 114 may also contain additional instructions, including instructions to send data to, receive data from, interact with, and / or control one or more of the travel system 102, the sensor system 104, the control system 106, and the peripheral devices 108.

[0117] In addition to the instructions 115, the memory 114 may also store data such as road maps, route information, the vehicle's location, direction, speed, and other such vehicle data, and other information. This information may be used by the vehicle and the computer system 112 during operation of the vehicle in autonomous, semi-autonomous, and / or manual modes.

[0118] The user interface 116 is used to provide information to or receive information from a user of the vehicle. Optionally, the user interface 116 may include one or more input / output devices within the set of peripheral devices 108, such as a wireless communication system 146, a vehicle computer 148, a microphone 150, and a speaker 152.

[0119] Computer system 112 may control functions of the vehicle based on input received from various subsystems (e.g., travel system 102, sensor system 104, and control system 106) and from user interface 116. For example, computer system 112 may utilize input from control system 106 in order to control steering system 132 to avoid obstacles detected by sensor system 104 and obstacle avoidance system 144. In some embodiments, computer system 112 may be operable to provide control over many aspects of the vehicle and its subsystems.

[0120] Optionally, one or more of the above components may be installed or associated with the vehicle separately. For example, the memory 114 may be partially or completely separated from the vehicle. The above components may be communicatively coupled together in a wired and / or wireless manner.

[0121] Optionally, the above components are only examples. In actual applications, the components in the above modules may be added or deleted according to actual needs. Figure 8 It should not be understood as limiting the embodiments of the present application.

[0122] An autonomous vehicle traveling on a road, such as the vehicle above, can identify objects in its surroundings to determine adjustments to the current speed. The objects can be other vehicles, traffic control devices, or other types of objects. In some examples, each identified object can be considered independently, and based on the object's respective characteristics, such as its current speed, acceleration, spacing from the vehicle, etc., can be used to determine the speed to be adjusted by the autonomous vehicle.

[0123] Optionally, the vehicle or a computing device associated with the vehicle (such as Figure 8 The computer system 112, computer vision system 138, processor 113) can predict the behavior of the identified object based on the characteristics of the identified object and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of each other, so all the identified objects can also be considered together to predict the behavior of a single identified object. The vehicle can adjust its speed based on the predicted behavior of the identified object. In other words, the vehicle can determine what stable state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, other factors can also be considered to determine the speed of the vehicle, such as the lateral position of other vehicles in the road of travel, the curvature of the road, the proximity of static and dynamic objects, etc.

[0124] Optionally, Figure 8 The vehicle shown may be a car, truck, motorcycle, bus, amusement park vehicle, etc. capable of traveling on public roads and highways with or without a driver, and the embodiments of the present application are not particularly limited.

[0125] In an exemplary embodiment, the vehicle motion state recognition method provided by the embodiment of the present application can be applied to the aforementioned Figure 8 The vehicle shown in the figure, for example, can be executed by a processor in the vehicle, or by a computer system. Alternatively, in other exemplary embodiments, the vehicle motion state recognition method provided in the embodiment of the present application can also be applied to the aforementioned Figure 8 The server to which the vehicle is connected in communication is, for example, a cloud server, which can remotely control the vehicle for unmanned driving. The present application does not limit the application subject of the vehicle motion state recognition method.

[0126] Also, take the vehicle as the first vehicle and the other vehicle as the second vehicle as an example. Fig. 9 A flow chart of a vehicle motion state recognition method provided in an embodiment of the present application is shown.

[0127] like Fig. 9As shown, in the method, first, S901-S903 can be executed to obtain the first lane information, the second lane information, and the position information and movement information of the second vehicle.

[0128] S901. Acquire lane information of a lane where a first vehicle is located as first lane information.

[0129] The lane information of the lane where the first vehicle is located refers to the track information of the lane centerline of the lane where the first vehicle is located. Figure 1 Taking the vehicle 1 shown in FIG. 1 as an example, the lane information of the lane where the vehicle 1 is located refers to the trajectory of the lane centerline of the lane 1 ( Figure 1 (shown by the dashed line).

[0130] The process of obtaining the first lane information is described below.

[0131] The first lane information can be represented by the following curve expression.

[0132] y ego,t (x) = a 3,t x 3 +a 2,t x 2 +a 1,t x+a 0,t .

[0133] Among them, a 3,t represents one sixth of the curvature change rate of the trajectory of the lane centerline in the first lane information at time t; a 2,t represents half of the curvature of the trajectory of the lane centerline in the first lane information at time t; a 1,t a represents the tangent slope of the trajectory of the lane centerline in the first lane information at time t; 0,t Indicates the lateral offset of the trajectory of the lane centerline in the first lane information at time t.

[0134] By fitting the trajectory of the lane centerline of the lane where the first vehicle is located according to the above curve expression, the lane information of the lane where the first vehicle is located, that is, the first lane information, can be obtained.

[0135] S902. Obtain at least two of lane information of a lane where the second vehicle is located, lane information of a first adjacent lane to the lane where the second vehicle is located, and lane information of a second adjacent lane to the lane where the second vehicle is located, as second lane information.

[0136] For example, in one embodiment, the second lane information may include lane information of the lane where the second vehicle is located, and lane information of the first adjacent lane of the lane where the second vehicle is located. In another embodiment, the second lane information may include lane information of the lane where the second vehicle is located, and lane information of the second adjacent lane of the lane where the second vehicle is located. In yet another embodiment, the second lane information may include lane information of the first adjacent lane of the lane where the second vehicle is located, and lane information of the second adjacent lane of the lane where the second vehicle is located. Alternatively, the second lane information may include all three of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located.

[0137] Among them, the lane information of the lane where the second vehicle is located refers to the trajectory information of the lane centerline of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located refers to the trajectory information of the lane centerline of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located refers to the trajectory information of the lane centerline of the second adjacent lane of the lane where the second vehicle is located.

[0138] Please refer to Figure 1 As shown, taking the second vehicle as vehicle 4 as an example, the first adjacent lane of the lane (lane 1) where vehicle 4 is located is lane 2, and the second adjacent lane is lane 3, or the first adjacent lane is lane 3, and the second adjacent lane is lane 2. That is, the first adjacent lane and the second adjacent lane are respectively the left and right adjacent lanes of the lane where vehicle 4 is located. The lane information of lane 1, lane 2, and lane 3 are the trajectories of the corresponding lane center axes.

[0139] The process of obtaining the second lane information is described below.

[0140] The second lane information can be represented by the following curve expression.

[0141] y i,t (x) = b i 3,t x 3 +b i 2,t x 2 +b i 1,t x+b i 0,t , i=0,1,2.

[0142] When i=0, y i,t (x) is y 0,t (x), represents the lane information of the lane where the second vehicle is located; when i=1, y i,t (x) is y1,t (x), represents the lane information of the first adjacent lane of the lane where the second vehicle is located; when i=2, y i,t (x) is y 2,t (x), indicating the lane information of the second adjacent lane of the lane where the second vehicle is located; b i 3,t represents one sixth of the curvature change rate of the trajectory of the lane centerline in the lane information corresponding to i at time t; b i 2,t b represents half of the curvature of the trajectory of the lane centerline in the lane information corresponding to i at time t; i 1,t b represents the tangent slope of the trajectory of the lane centerline in the lane information corresponding to i at time t; i 0,t Represents the lateral offset of the trajectory of the lane centerline in the lane information corresponding to i at time t.

[0143] It should be noted that if the lane where the second vehicle is located does not have a first adjacent lane (such as the left lane), then in the curve expression of the second lane information above, when i is equal to 1, the corresponding b i 3,t 、b i 2,t 、b i 1,t 、b i 0,t The coefficients of the parameters are all 255. If there is no second adjacent lane (such as the right lane) in the lane where the second vehicle is located, then in the curve expression of the second lane information above, when i is equal to 2, the corresponding b i 3,t 、b i 2,t 、b i 1,t 、b i 0,t The coefficients of other parameters are also 255.

[0144] By fitting the trajectory of the lane centerline of the lane where the second vehicle is located, the trajectory of the lane centerline of the first adjacent lane to the lane where the second vehicle is located, and the trajectory of the lane centerline of the second adjacent lane to the lane where the second vehicle is located according to the above curve expression, the corresponding lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane to the lane where the second vehicle is located, and the lane information of the second adjacent lane to the lane where the second vehicle is located can be obtained.

[0145] S903: Acquire location information and movement information of the second vehicle.

[0146] Optionally, the sensor system (such as radar, distance sensor, etc.) and positioning system (such as GPS, Beidou) on the first vehicle can collect the position information and motion information of the second vehicle at a fixed period and upload it to the first vehicle. The position information of the second vehicle refers to the position information (x t ,y t ,θ t ), where x t represents the horizontal coordinate of the second vehicle, y t represents the ordinate of the second vehicle, θ t represents the heading angle of the second vehicle. The position information of the second vehicle can be the relative position of the second vehicle relative to the first vehicle, or the absolute position of the second vehicle. The motion information of the second vehicle refers to the speed v of the second vehicle at each cycle time t. x,t , acceleration a x,t , trajectory curvature γ t And other kinematic information.

[0147] It should be noted that the present application does not limit the execution order between S901, S902 and S903. They can be executed in parallel, or one step can be executed first or later.

[0148] After the first lane information, the second lane information, and the position information and motion information of the second vehicle are acquired through the above S901-S903, the motion state of the second vehicle relative to the first vehicle can be determined through the following S904-S910.

[0149] S904. Determine at least two predicted driving trajectories of the second vehicle according to the second lane information, and the position information and motion information of the second vehicle, each predicted driving trajectory corresponding to each type of lane information included in the second lane information.

[0150] For example, when the second lane information includes lane information of the lane where the second vehicle is located and lane information of the first adjacent lane to which the second vehicle is located, the predicted driving trajectory of the second vehicle maintaining driving in the current lane and the predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane to which the second vehicle is located can be determined accordingly.

[0151] Alternatively, when the second lane information includes lane information of the lane where the second vehicle is located and lane information of the second adjacent lane of the lane where the second vehicle is located, the predicted driving trajectory of the second vehicle maintaining driving in the current lane and the predicted driving trajectory of the second vehicle changing lanes to the second adjacent lane of the lane can be determined accordingly.

[0152] Alternatively, when the second lane information may include lane information of a first adjacent lane of the lane where the second vehicle is located, and lane information of a second adjacent lane of the lane where the second vehicle is located, the predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane of its lane and the predicted driving trajectory of the second vehicle changing lanes to the second adjacent lane of its lane may be determined accordingly.

[0153] Alternatively, when the second lane information includes all three of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located, the predicted driving trajectory of the second vehicle maintaining driving in the current lane, the predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane of its lane, and the predicted driving trajectory of the second vehicle changing lanes to the second adjacent lane of its lane can be determined accordingly.

[0154] The following describes a specific method for determining the predicted driving trajectory by taking an example in which the second lane information includes all three of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located.

[0155] 1) A predicted driving trajectory of the second vehicle maintaining driving in the current lane.

[0156] Assuming that the second vehicle will adjust the steering wheel and speed from the current position to return to the vicinity of the lane centerline of its lane, polynomial fitting can be used to plan the path for the second vehicle to maintain driving in the current lane.

[0157] For example, based on six conditions including the current position of the second vehicle, the heading angle, the path curvature, the lateral position satisfied by the centerline of the lane in which the second vehicle returns, and the first-order and second-order compatibilities, a fourth-order polynomial fitting can be performed to obtain the following equation (or it can be called a polynomial fitting model) to represent the predicted driving trajectory of the second vehicle while maintaining driving in the current lane.

[0158] z 0,t (x) = c 0 4,t x 4 +c 0 3,t x 3 +c 0 2,t x 2 +c 0 1,t x+c 0 0,t .

[0159] This polynomial satisfies the condition

[0160]

[0161] In the above equation, c 0 4,t 、c 0 3,t 、c 0 2,t 、c 0 1,t and c 0 0,t The five parameters can be obtained by fitting the above compatibility conditions and have no special meanings here.

[0162] 2) A predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane or the second adjacent lane of the lane to which the second vehicle is currently driving.

[0163] Taking the example that the first adjacent lane is the left lane of the lane where the second vehicle is located and the second adjacent lane is the right lane of the lane where the second vehicle is located, assuming that the second vehicle will change lanes from the current position to the left lane or the right lane, a fourth-order polynomial fitting can also be performed based on the position information, kinematic information and road information of the lane to be changed to the second vehicle, and the following equation (or it can be called a polynomial fitting model) is obtained to represent the predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane or the second adjacent lane of its lane.

[0164] z i,t (x) = c i 4,t x 4 +c i 3,t x 3 +c i 2,t x 2 +c i 1,t x+c i 0,t ,i=1,2.

[0165] Among them, i=1 indicates changing lane to the left lane; i=2 indicates changing lane to the right lane.

[0166] This polynomial satisfies the condition

[0167]

[0168] In the above equation, c i 4,t 、c i 3,t 、c i 2,t 、c i1,t and c i 0,t The five parameters can be obtained by fitting the above compatibility conditions and have no special meanings here.

[0169] Alternatively, in some embodiments, the above-mentioned polynomial fitting method may not be adopted, but other fitting functions such as Bessel function may be adopted, or extraction based on the driving database may be adopted to obtain the predicted driving trajectory of the second vehicle maintaining driving in the current lane, and the predicted driving trajectory of the second vehicle changing lanes to the first adjacent lane or the second adjacent lane of the lane. The present application does not impose any restrictions on this.

[0170] After executing S904 to obtain at least two predicted driving trajectories, S905 and S906 can be executed first to determine the target predicted driving trajectory of the second vehicle based on the at least two predicted driving trajectories obtained, and then S907 can be executed to determine the motion state of the second vehicle relative to the first vehicle based on the target predicted driving trajectory and the first lane information.

[0171] S905: Determine the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle.

[0172] In some embodiments, a first transverse coordinate vector of the historical driving trajectory of the second vehicle within a first preset time period may be obtained, and the first transverse coordinate vector is used to indicate the distance vector between the historical driving trajectory and the lane line of the lane where the second vehicle is located. Then, for each predicted driving trajectory of at least two predicted driving trajectories: a second transverse coordinate vector of the predicted driving trajectory within a second preset time period is obtained, and the second transverse coordinate vector is used to indicate the distance vector between the predicted driving trajectory and the lane line of the lane where the second vehicle is located, and the similarity between the predicted driving trajectory and the historical driving trajectory is determined based on the second transverse coordinate vector and the first transverse coordinate vector, thereby obtaining the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle.

[0173] The first lateral coordinate vector of the historical driving trajectory of the second vehicle in the first preset time period includes a plurality of first lateral coordinates of the historical driving trajectory of the second vehicle in the first preset time period. The second lateral coordinate vector of the predicted driving trajectory in the second preset time period includes a plurality of second lateral coordinates of the predicted driving trajectory in the second preset time period.

[0174] In some embodiments, the first preset time period may be a time period consisting of a plurality of consecutive first preset periods, each of which may include at least one first horizontal coordinate. The second preset time period may be a time period consisting of a plurality of consecutive second preset periods, each of which may include at least one second horizontal coordinate.

[0175] For example, since it is a continuous process for the driver to maintain or change the road (assuming that the second vehicle is a vehicle driven by a certain driver), the driver will make corresponding adjustments before obvious features appear in the corresponding scene. In view of this, the present application can first determine the first transverse coordinate time series (i.e., the aforementioned first transverse coordinate vector) composed of multiple first transverse coordinates of the historical driving trajectory of the second vehicle within the first preset time period, and the second transverse coordinate time series (i.e., the aforementioned second transverse coordinate vector) composed of multiple second transverse coordinates of each predicted driving trajectory within the second preset time period. Then, the similarity between the first transverse coordinate time series and the second transverse coordinate time series can be calculated to obtain the similarity between the historical driving trajectory of the second vehicle and the predicted driving trajectory.

[0176] Take the current time as t, the first preset time period as (t-MΔT, t-(M-1)ΔT, ..., t) and other M+1 first preset cycles, and the second preset time period as (M+N, M+N-1, M) and other N+1 second preset cycles as an example.

[0177] The first transverse coordinate time series formed by the first transverse coordinates of the historical driving trajectory within the first preset time period can be expressed as follows: t,h .

[0178] Y t,h =(y t-MΔT ,y t-(M-1)ΔT ,…,y t ).

[0179] Among them, Y t,h Represents the first horizontal coordinate time series; y t-MΔT represents the first horizontal coordinate at time t-MΔT, and so on.

[0180] In an exemplary embodiment, M may be 20; N may be 10; ΔT is a sampling period, and its size may be an integer multiple of 0.02 s.

[0181] The second transverse coordinate time series formed by the second transverse coordinates of the predicted driving trajectory within the second preset time period can be expressed as follows: i t,p .

[0182] Y i t,p =(z i N (t-MΔT),z i N (t-(M-1)ΔT),…,z i N (t)),i=0,1,2.

[0183] z i N (t-(Mj)ΔT)=z i,t-(M+N-j) (x t-(M-j)ΔT ),j=0,1,2…,M.

[0184] Among them, i=0 means keeping the lane; i=1 means changing lane to the left lane; i=2 means changing lane to the right lane; Y i t,p Represents the second horizontal coordinate time series. i N (t-(Mj)ΔT) is expressed as the predicted longitudinal position of the second vehicle at time t-(Mj)ΔT is x t-(M-j)ΔT , its horizontal position.

[0185] Furthermore, the first horizontal coordinate time series Y t,h and the second horizontal coordinate time series Y i t,p The similarity between (i=0,1,2) can be expressed as ρ i t The details can be as follows.

[0186]

[0187] Among them, i=0 means keeping the lane; i=1 means changing lane to the left lane; i=2 means changing lane to the right lane; It can be understood as the similarity between the predicted driving trajectory and the historical driving trajectory when i=0,1,2 Y t,h (j) indicates Y t,h The jth component of i t,p (j) indicates Y i t,p The jth component of j (j=0, ..M) represent the weights of the differences between the predicted driving trajectory and the historical driving trajectory in the j cycles before the current moment, respectively, satisfying the following conditions.

[0188]

[0189] It should be noted that a j The value of can be to set each a j The inverse of the time difference between the corresponding jth period and the current time t is taken as a j The initial value of is obtained after normalization, which will not be described in detail here.

[0190] In some other embodiments, the similarity between each of the at least two predicted driving trajectories and the historical driving trajectory of the second vehicle may also be determined by calculating mathematical methods such as cosine similarity, Mahalanobis distance, and Euclidean distance, which is not limited here.

[0191] S906: Determine a target predicted driving trajectory of the second vehicle according to the at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle.

[0192] Based on the similarity between each of the at least two predicted driving trajectories determined by S905 and the historical driving trajectory of the second vehicle, in a possible design, determining the target predicted driving trajectory of the second vehicle according to the at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle may refer to: selecting a predicted driving trajectory with the highest similarity to the historical driving trajectory of the second vehicle from the at least two predicted driving trajectories as the target predicted driving trajectory of the second vehicle. Since the target predicted driving trajectory selected in this way has the highest similarity to the historical driving trajectory of the second vehicle, it is the predicted driving trajectory of the at least two predicted driving trajectories that is most likely to be close to the actual driving trajectory of the second vehicle in the future.

[0193] In another possible design, after obtaining the similarity between each of the at least two predicted driving trajectories and the historical driving trajectory of the second vehicle, the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle can be predicted based on the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle, and then the target predicted driving trajectory of the second vehicle can be determined based on the at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle.

[0194] For example, the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle may be normalized, and the probability of each predicted driving trajectory may be predicted, and the probability may indicate the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle. Then, the probability of each predicted driving trajectory and the aforementioned at least two predicted driving trajectories may be combined for comprehensive calculation to obtain the target predicted driving trajectory of the second vehicle.

[0195] The following uses three predicted driving trajectories, namely, the predicted driving trajectory of the second vehicle maintaining driving in the current lane, the predicted driving trajectory of the second vehicle changing lanes to the left lane of the current lane, and the predicted driving trajectory of the second vehicle changing lanes to the right lane of the current lane, to illustrate the process of determining the target predicted driving trajectory. However, it should be noted that, in fact, the target predicted driving trajectory can also be determined based on any two of the above three predicted driving trajectories, which will not be described in detail here.

[0196] With the above similarity Take ρ as an example, by calculating the ρ corresponding to each predicted driving trajectory. i t After normalization, we can preliminarily get the probability corresponding to the predicted driving trajectory, which can be expressed as follows

[0197]

[0198] Among them, i=0 means keeping the lane; i=1 means changing lanes to the left lane; i=2 means changing lanes to the right lane.

[0199] Furthermore, the probability corresponding to the predicted driving trajectory of the second vehicle maintaining driving in the current lane can be expressed as follows P 0 t .

[0200]

[0201] The probability corresponding to the predicted driving trajectory of the second vehicle changing lanes to the left lane of the lane can be expressed as follows P 1 t .

[0202]

[0203] The probability corresponding to the predicted driving trajectory of the second vehicle changing lanes to the right lane of the lane can be expressed as follows P 2 t .

[0204]

[0205] After obtaining the above probability P corresponding to the predicted driving trajectory 0 t , P 1 t and P 2 t After that, the three predicted driving trajectories can be combined to comprehensively calculate the target predicted driving trajectory.

[0206] As described in the above embodiment, assuming that z i,t(x) represents the predicted driving trajectory, where i=0 means keeping the lane; i=1 means changing lanes to the left lane; i=2 means changing lanes to the right lane. The target predicted driving trajectory can be obtained by: t (x) to represent it.

[0207]

[0208] Optionally, since the second vehicle will not change lanes to the left or to the right at the same time, in some embodiments of the present application, when determining the target predicted driving trajectory based on the aforementioned three predicted driving trajectories, the minimum value of the probabilities corresponding to the three predicted driving trajectories can be corrected to 0, so as to further make the determined target predicted driving trajectory closer to the actual driving trajectory, thereby improving the accuracy of subsequent vehicle motion state recognition. For example, if the probability corresponding to the predicted driving trajectory of the second vehicle changing lanes to the left is the smallest, it can be corrected to 0, and the target predicted driving trajectory is determined only based on the predicted driving trajectory of the second vehicle changing lanes to the right and the predicted driving trajectory of the lane-keeping, and their corresponding probabilities.

[0209] In another possible design, after predicting the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle based on the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle, the predicted driving trajectory with the greatest similarity to the actual driving trajectory of the second vehicle can be directly selected as the target predicted driving trajectory of the second vehicle.

[0210] For example, after determining the probability of each predicted driving trajectory based on the similarity between each predicted driving trajectory of the at least two predicted driving trajectories and the historical driving trajectory of the second vehicle, the predicted driving trajectory with the highest probability can be directly selected as the target predicted driving trajectory of the second vehicle.

[0211] This design is similar to the first method of directly selecting the predicted driving trajectory with the highest similarity as the target predicted driving trajectory of the second vehicle. The determined predicted driving trajectory is the predicted driving trajectory among the at least two predicted driving trajectories that is most likely to be close to the actual future driving trajectory of the second vehicle.

[0212] It should be noted that the present application does not limit the specific method of determining the target predicted driving trajectory of the second vehicle based on at least two predicted driving trajectories and the similarity between each of the at least two predicted driving trajectories and the historical driving trajectory of the second vehicle.

[0213] S907: Determine a motion state of the second vehicle relative to the first vehicle according to the target predicted driving trajectory and the first lane information.

[0214] As described above, the first lane information refers to the trajectory information of the lane centerline of the lane where the first vehicle is located. After obtaining the target predicted driving trajectory, the first lane information can be combined to determine the motion state of the second vehicle relative to the first vehicle by judging the distance between the target predicted driving trajectory and the lane centerline of the lane where the first vehicle is located (such as: minimum lateral distance, maximum lateral distance), such as: whether the second vehicle will cut into the lane where the first vehicle is located from other lanes, whether the second vehicle will cut out from the lane where the first vehicle is located to other lanes, etc.

[0215] For example, Fig.10 Another flow chart of the vehicle motion state identification method provided in an embodiment of the present application is shown.

[0216] like Fig.10 As shown, S907 may include: S1001-S1005.

[0217] S1001. Based on the target predicted driving trajectory and the first lane information, determine the minimum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length, the first longitudinal position corresponding to the minimum lateral distance, and the maximum lateral distance, and the second longitudinal position corresponding to the maximum lateral distance.

[0218] As described above, the first lane information may be the lane centerline of the lane where the first vehicle is located.

[0219] For example, the first vehicle is vehicle A and the second vehicle is vehicle B. Fig.11 A schematic diagram is shown in which vehicle A according to an embodiment of the present application identifies the motion state of vehicle B based on the target predicted driving trajectory of vehicle B.

[0220] like Fig.11 As shown, assuming that the preset longitudinal length is [a, b], the position where a is located can be a position close to vehicle B in the direction along the center axis of the lane, or a position with the same longitudinal coordinate as vehicle B. Correspondingly, the position where b is located is a position far away from vehicle B in the direction along the center axis of the lane. It can be understood that the preset longitudinal length [a, b] is only used to limit a certain range in the direction along the center axis of the lane.

[0221] Target predicted driving trajectory z t (x) and the centerline y of the lane where vehicle A is located ego,t The minimum horizontal distance d within the preset longitudinal length [a,b] min , which can be expressed as

[0222]

[0223] Target predicted driving trajectory z t(x) and the centerline y of the lane where vehicle A is located ego,t The maximum lateral distance d within the preset longitudinal length [a,b] max , which can be expressed as

[0224]

[0225] The first longitudinal position corresponding to the minimum lateral distance refers to the target predicted driving trajectory d min The second longitudinal position refers to the target predicted driving trajectory d max The location.

[0226] S1002: If the minimum lateral distance is greater than a first threshold, determine that the motion state of the second vehicle relative to the first vehicle is that the second vehicle is traveling in a different lane from the first vehicle.

[0227] Optionally, the first threshold d 1 The size may be half the width of the body of the first vehicle plus 30 centimeters, or may be a value close thereto.

[0228] When the minimum lateral distance d min Greater than the first threshold d 1 , it can be determined that the second vehicle will not enter the lane where the first vehicle is located, and it can be considered that the motion state of the second vehicle relative to the first vehicle is to maintain a different lane from the first vehicle, such as: It can be expressed as

[0229] Relative state (objectState) = passby.

[0230] For example: Fig.12 Another schematic diagram of vehicle A identifying the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application is shown. Fig.12 As shown in the figure, if the minimum lateral distance d between the target predicted driving trajectory of vehicle B and the lane centerline of the lane where vehicle A is located is min Greater than the first threshold d 1 , then the motion state of vehicle B relative to vehicle A is determined to be objectState=passby.

[0231] S1003. If the minimum lateral distance is less than a first threshold and the maximum lateral distance is greater than a second threshold, determine that the motion state of the second vehicle relative to the first vehicle is crossing a lane where the first vehicle is located; wherein the second threshold is greater than the first threshold.

[0232] Optionally, the second threshold d 2 The value of can be the width of the lane where the first vehicle is located plus 30 cm, or can be a value close to it. It can be clearly seen that the second threshold d2 Much larger than the first threshold d 1 .

[0233] When the minimum lateral distance d min Less than the first threshold d 1 , and the maximum lateral distance d max is greater than the second threshold d 2 , it can be determined that the second vehicle will enter the lane where the first vehicle is located, and there will be a large lateral deviation. It can be considered that the motion state of the second vehicle relative to the first vehicle is crossing the lane where the first vehicle is located, such as: It can be expressed as

[0234] objectState=cross.

[0235] For example: Fig.13 FIG. 2 shows another schematic diagram of vehicle A identifying the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application. Fig.13 As shown in the figure, if the minimum lateral distance d between the target predicted driving trajectory of vehicle B and the lane centerline of the lane where vehicle A is located is min Less than the first threshold d 1 , and the maximum lateral distance d max is greater than the second threshold d 2 When , it can be determined that vehicle B will enter the lane where vehicle A is located, and there will be a large lateral deviation. It can be considered that the motion state of vehicle B relative to vehicle A is crossing the lane where vehicle A is located, such as: it can be expressed as objectState=cross.

[0236] S1004. If the maximum lateral distance is less than a third threshold, determine that the motion state of the second vehicle relative to the first vehicle is to maintain driving in the same lane as the first vehicle; wherein the third threshold is less than the first threshold.

[0237] Optionally, the third threshold d 3 The value of may be half the width of the body of the first vehicle minus 30 cm, or may be a value close thereto. 3 Less than the first threshold d 1 .

[0238] When the maximum lateral distance d max Less than the third threshold d 3 , it can be determined that the motion state of the second vehicle relative to the first vehicle is to maintain the same lane as the first vehicle, such as:

[0239] objectState = follow.

[0240] Of course, it is understandable that in S1004, the second vehicle may follow the first vehicle, or the first vehicle may follow the second vehicle.

[0241] For example: Fig.14 FIG. 2 shows another schematic diagram of vehicle A identifying the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application. Fig.14 As shown in the figure, if the maximum lateral distance d between the target predicted driving trajectory of vehicle B and the lane centerline of the lane where vehicle A is located is max Less than the third threshold d 3 When , it can be determined that the motion state of vehicle B relative to vehicle A is to maintain the same lane as vehicle A, such as: it can be expressed as objectState = follow. At this time, the minimum lateral distance d min must be less than the third threshold d 3 , no more details.

[0242] S1005. If the minimum lateral distance is less than the first threshold, the maximum lateral distance is greater than the third threshold and less than the second threshold, and the time when the second longitudinal position appears in the target predicted driving trajectory is earlier than the time when the first longitudinal position appears, then it is determined that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located.

[0243] For example: Fig.15 FIG. 2 shows another schematic diagram of vehicle A identifying the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application. Fig.15 As shown in the figure, if the minimum lateral distance d between the target predicted driving trajectory of vehicle B and the lane centerline of the lane where vehicle A is located is min Less than the first threshold d 1 , maximum lateral distance d max Greater than the third threshold d 3 , is less than the second threshold d 2 , and the second longitudinal position x in the target predicted driving trajectory max The time of occurrence is earlier than the first longitudinal position x min When the object state of vehicle B relative to vehicle A appears, it can be determined that the motion state of vehicle B is cutting into the lane where vehicle A is located, such as: it can be expressed as objectState=cut_in.

[0244] S1006. If the minimum lateral distance is less than the first threshold, the maximum lateral distance is greater than the third threshold and less than the second threshold, and the time when the first longitudinal position appears in the target predicted driving trajectory is earlier than the time when the second longitudinal position appears, then it is determined that the motion state of the second vehicle relative to the first vehicle is cutting out of the lane where the first vehicle is located.

[0245] For example: Fig.16 FIG. 2 shows another schematic diagram of vehicle A identifying the motion state of vehicle B according to the target predicted driving trajectory of vehicle B provided by an embodiment of the present application. Fig.16 As shown in the figure, if the minimum lateral distance d between the target predicted driving trajectory of vehicle B and the lane centerline of the lane where vehicle A is located is min Less than the first threshold d 1 , maximum lateral distance d max Greater than the third threshold d 3 , is less than the second threshold d 2 , and the first longitudinal position x in the target predicted driving trajectory min The time of occurrence is earlier than the second longitudinal position x max When the object state of vehicle B relative to vehicle A appears, it can be determined that the motion state of vehicle B is to cut out of the lane where vehicle A is located, such as: it can be expressed as objectState=cut_out.

[0246] It should be noted that, in the exemplary descriptions of S1002-S1006 above, although the motion state of the second vehicle relative to the first vehicle is divided into scenarios greater than or less than a certain threshold, in actual implementation, scenarios equal to a certain threshold may also need to be considered. For example, in some implementations, the minimum lateral distance d min Equal to the first threshold d 1 The scene is greater than the first threshold d 1 or, in some other implementations, the minimum lateral distance d min Equal to the first threshold d 1 The scene is less than the first threshold d 1 The comparison with other thresholds is similar to this and will not be described in detail.

[0247] In one possible design, when it is determined in S1005 that the movement state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located, the method can also further refine the movement state of the second vehicle relative to the first vehicle into cutting into the lane where the first vehicle is located according to the first cutting-in state, or cutting into the lane where the first vehicle is located according to the second cutting-in state, based on the first longitudinal position corresponding to the minimum lateral distance and the longitudinal distance between the preset longitudinal length and one end close to the first vehicle.

[0248] Alternatively, when it is determined in S1006 that the motion state of the second vehicle relative to the first vehicle is cutting out of the lane where the first vehicle is located, the method can also further refine the motion state of the second vehicle relative to the first vehicle into cutting out of the lane where the first vehicle is located according to the first cutting-out state, or cutting out of the lane where the first vehicle is located according to the second cutting-out state, based on the first longitudinal position corresponding to the minimum lateral distance and the longitudinal distance between the preset longitudinal length and one end close to the first vehicle.

[0249] For example, the first cut-in state can be called the tight cut-in state, and the first cut-out state can be called the tight cut-out state. The second cut-in state can be called the normal cut-in state, and the second cut-out state can be called the normal cut-out state. Alternatively, the first / second cut-in state and the first / second cut-out state can be called other names.

[0250] In the following, taking the first cut-in state as the tight cut-in state, the first cut-out state as the tight cut-out state, the second cut-in state as the normal cut-in state, and the second cut-out state as the normal cut-out state as examples, examples are given to illustrate how the movement state of the second vehicle relative to the first vehicle is further refined into tight cut-in or normal cut-in, and how the movement state of the second vehicle relative to the first vehicle is further refined into tight cut-out or normal cut-out.

[0251] Fig.17 Another schematic diagram showing that vehicle A according to an embodiment of the present application identifies the motion state of vehicle B based on the target predicted driving trajectory of vehicle B.

[0252] by Fig.17 As shown in the figure as an example, the motion state of the second vehicle relative to the first vehicle can be further refined according to the longitudinal distance between the first longitudinal position corresponding to the minimum lateral distance and the end of the preset longitudinal length a, that is, the distance between the first longitudinal position and the end of the preset longitudinal length a in the direction parallel to the lane line of the lane where the first vehicle is located. When it is determined that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located, if the longitudinal distance is less than a fourth threshold value (such as: 1 meter, 2 meters, 3 meters, etc., not limited here), the motion state of the second vehicle relative to the first vehicle is determined to be close_cut_in into the lane where the first vehicle is located. If the longitudinal distance is greater than the fourth threshold value, the motion state of the second vehicle relative to the first vehicle is determined to be normal cutting into the lane where the first vehicle is located.

[0253] It is easy to understand that if the longitudinal distance is equal to the fourth threshold, it can be determined that the movement state of the second vehicle relative to the first vehicle is closely approaching the lane where the first vehicle is located, or normally cutting into the lane where the first vehicle is located, and no limitation is made here.

[0254] Similarly, the motion state of the second vehicle relative to the first vehicle can also be further refined into a tight cut-out or a normal cut-out according to the longitudinal distance between the first longitudinal position corresponding to the minimum lateral distance and the end of the preset longitudinal length a, that is, the distance between the first longitudinal position in a direction parallel to the lane line of the lane where the first vehicle is located and the end of the preset longitudinal length a, and an example is given to illustrate the situation. For example, when it is determined that the motion state of the second vehicle relative to the first vehicle is to cut out of the lane where the first vehicle is located, if the longitudinal distance is less than the fourth threshold value, it can be determined that the motion state of the second vehicle relative to the first vehicle is to tightly cut out of the lane where the first vehicle is located. If the longitudinal distance is greater than the fourth threshold value, it is determined that the motion state of the second vehicle relative to the first vehicle is to normally cut out of the lane where the first vehicle is located. It is also easy to understand that if the longitudinal distance is equal to the fourth threshold value, it can be determined that the motion state of the second vehicle relative to the first vehicle is to tightly cut out of the lane where the first vehicle is located, or to normally cut out of the lane where the first vehicle is located, and this is not limited here.

[0255] In some embodiments, before determining the predicted driving trajectory of the second vehicle based on the second lane information, and the position information and motion information of the second vehicle in the above S904, the vehicle motion state identification method may also include a step of correcting the motion information of the second vehicle.

[0256] For example, the lateral velocities within several (for example, 6) historical periods can be collected first, and the average value of the lateral velocities within the aforementioned historical periods can be calculated to obtain the average historical lateral velocity of the second vehicle. Then, the lateral velocity included in the motion information of the second vehicle can be corrected based on the average historical lateral velocity of the second vehicle.

[0257] Alternatively, a time-speed polynomial fitting may be performed on the lateral speeds in the aforementioned several historical periods to obtain fitting coefficients (the polynomial fitting method may refer to the polynomial fitting method for the predicted driving trajectory of the second vehicle to keep the lane or change the lane in the aforementioned embodiment, which will not be described in detail here). Then, the current lateral speed of the second vehicle is predicted based on the fitting coefficient to obtain the predicted current lateral speed. Then, the lateral speed included in the motion information of the second vehicle may be corrected based on the predicted current lateral speed.

[0258] Taking the correction of the lateral speed included in the motion information of the second vehicle according to the predicted current lateral speed as an example (the correction method according to the average historical lateral speed of the second vehicle is the same): the collected lateral speed of the second vehicle can be compared with the predicted current lateral speed, and if the difference between the two is greater than the fifth threshold, the collected lateral speed is corrected to reduce the disturbance caused by the excessive instantaneous speed of the collected lateral speed. For example, the lateral speed can be increased or decreased, or the collected lateral speed and the predicted current lateral speed are weightedly combined according to a preset weight to update the collected lateral speed, etc.

[0259] Optionally, the magnitude of the fifth threshold is related to the driving stability of the second vehicle. The higher the driving stability of the second vehicle, the smaller the fifth threshold, and vice versa. For example, the fifth threshold may be 0.2 meters per second (m / s), 0.4 m / s, etc., which is not limited here.

[0260] In some other embodiments, when determining the similarity between each of at least two predicted driving trajectories and the historical driving trajectory of the second vehicle in the above S905, if a first horizontal coordinate time series consisting of the first horizontal coordinates of the historical driving trajectory of the second vehicle within the first preset time period and a second horizontal coordinate time series consisting of the second horizontal coordinates of each predicted driving trajectory within the second preset time period are first determined, and then the similarity between the first horizontal coordinate time series and the second horizontal coordinate time series is calculated to obtain the similarity between the historical driving trajectory of the second vehicle and the predicted driving trajectory, then in this method, the sizes of the first preset time period and the second preset time period can also be adjusted according to the difference between the collected lateral speed of the second vehicle and the current lateral speed predicted above.

[0261] For example, when the gap is large, it means that the driving intention of the second vehicle is more clear, and the delay effect of the historical driving trajectory of the second vehicle on the predicted driving trajectory can be reduced by reducing the size of the first preset period and the second preset period. On the contrary, when the gap is small, the size of the first preset period and the second preset period can be increased.

[0262] Assume that for the second vehicle, the polynomial fitting prediction result of the historical lateral velocity is v, the current lateral velocity collected is v0, and the first preset period is δ m , the second preset period is δ n (δ m , δ n is the calibration value, such as: m It can be 3, δ n can be 2.5), then δ m and δ nThe size can be changed as follows.

[0263] Δ m =[v-v0]*δ m ; Δ n =[v-v0]*δ n .

[0264] Optionally, the above δ m It may refer to the number of first preset periods included in the first preset time period, and adjusting the size of the first preset time period refers to adjusting the number of first preset periods included in the first preset time period.

[0265] Similarly, the above δ n It may refer to the number of second preset periods included in the second preset time period, and adjusting the size of the second preset time period refers to adjusting the number of second preset periods included in the second preset time period.

[0266] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of a vehicle or a server. It is understandable that in order to realize the above functions, the vehicle or the server may include a hardware structure and / or software module corresponding to executing each function.

[0267] For example, the embodiment of the present application may also provide a vehicle motion state recognition device. Fig.18 FIG. 1 is a schematic diagram showing the structure of a vehicle motion state recognition device provided by an embodiment of the present application. Fig.18 As shown, the vehicle motion state recognition device may include: an acquisition module 1801, used to acquire first lane information, second lane information, and position information and motion information of the second vehicle; the first lane information is the lane information of the lane where the first vehicle is located; the second lane information includes at least two of the lane information of the lane where the second vehicle is located, the lane information of the first adjacent lane of the lane where the second vehicle is located, and the lane information of the second adjacent lane of the lane where the second vehicle is located. A prediction module 1802, used to determine at least two predicted driving trajectories of the second vehicle according to the second lane information, and the position information and motion information of the second vehicle; each predicted driving trajectory corresponds to each type of lane information contained in the second lane information. A determination module 1803, used to determine the motion state of the second vehicle relative to the first vehicle according to the at least two predicted driving trajectories and the first lane information.

[0268] In one possible design, the determination module 1803 is specifically used to determine the target predicted driving trajectory of the second vehicle based on at least two predicted driving trajectories and the similarity between at least each predicted driving trajectory and the historical driving trajectory of the second vehicle; and determine the motion state of the second vehicle relative to the first vehicle based on the target predicted driving trajectory and the first lane information.

[0269] In one possible design, the determination module 1803 is also used to obtain a first lateral coordinate vector of a historical driving trajectory of the second vehicle within a first preset time period; wherein the first lateral coordinate vector is used to indicate a distance vector between the historical driving trajectory and the lane line of the lane where the second vehicle is located; for each predicted driving trajectory: obtain a second lateral coordinate vector of the predicted driving trajectory within the second preset time period, and determine the similarity between the predicted driving trajectory and the historical driving trajectory based on the second lateral coordinate vector and the first lateral coordinate vector; wherein the second lateral coordinate vector is used to indicate a distance vector between the predicted driving trajectory and the lane line of the lane where the second vehicle is located.

[0270] In one possible design, the determination module 1803 is specifically used to predict the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle based on the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle; and determine the target predicted driving trajectory of the second vehicle based on at least two predicted driving trajectories and the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle.

[0271] In one possible design, the determination module 1803 is specifically used to determine the minimum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length based on the target predicted driving trajectory and the first lane information; if the minimum lateral distance is greater than the first threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is to maintain driving in a different lane from the first vehicle.

[0272] In a possible design, the determination module 1803 is also used to determine the maximum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within a preset longitudinal length based on the target predicted driving trajectory and the first lane information; if the minimum lateral distance is less than the first threshold and the maximum lateral distance is greater than the second threshold, the motion state of the second vehicle relative to the first vehicle is determined to be crossing the lane where the first vehicle is located; wherein the second threshold is greater than the first threshold. If the maximum lateral distance is less than the third threshold, the motion state of the second vehicle relative to the first vehicle is determined to be maintaining driving in the same lane as the first vehicle; wherein the third threshold is less than the first threshold. If the minimum lateral distance is less than the first threshold, the maximum lateral distance is greater than the third threshold and less than the second threshold, the motion state of the second vehicle relative to the first vehicle is determined to be cutting into the lane where the first vehicle is located, or cutting out of the lane where the first vehicle is located.

[0273] In a possible design, the determination module 1803 is also used to obtain a first longitudinal position corresponding to the minimum lateral distance and a second longitudinal position corresponding to the maximum lateral distance; when the minimum lateral distance is less than the first threshold, and the maximum lateral distance is greater than the third threshold and less than the second threshold, if the time when the second longitudinal position appears in the target predicted driving trajectory is earlier than the time when the first longitudinal position appears, then the motion state of the second vehicle relative to the first vehicle is determined to be cutting into the lane where the first vehicle is located; if the time when the first longitudinal position appears in the target predicted driving trajectory is earlier than the time when the second longitudinal position appears, then the motion state of the second vehicle relative to the first vehicle is determined to be cutting out of the lane where the first vehicle is located.

[0274] In one possible design, the determination module 1803 is also used to obtain the longitudinal distance between the first longitudinal position and the first end of the preset longitudinal length; wherein the longitudinal distance is the distance between the first longitudinal position and the first end of the preset longitudinal length in a direction parallel to the lane line of the lane where the first vehicle is located, and the first end of the preset longitudinal length is an end close to the first vehicle; if the longitudinal distance is less than a fourth threshold, then the movement state of the second vehicle relative to the first vehicle is determined to be cutting into the lane where the first vehicle is located according to the first cutting-in state, or cutting out of the lane where the first vehicle is located according to the first cutting-out state; if the longitudinal distance is greater than the fourth threshold, then the movement state of the second vehicle relative to the first vehicle is determined to be cutting into the lane where the first vehicle is located according to the second cutting-in state, or cutting out of the lane where the first vehicle is located according to the second cutting-out state.

[0275] In one possible design, the motion information of the second vehicle includes a lateral speed of the second vehicle; the prediction module 1802 is further used to correct the lateral speed included in the motion information of the second vehicle according to an average historical lateral speed of the second vehicle.

[0276] It should be understood that the division of modules or units in the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or physically separated. Moreover, the modules in the device can be all implemented in the form of software calling through processing elements; or all implemented in the form of hardware; or some units can be implemented in the form of software calling through processing elements, and some units can be implemented in the form of hardware.

[0277] For example, each unit can be a separately established processing element, or it can be integrated in a certain chip of the device. In addition, it can also be stored in a memory in the form of a program, and called and executed by a certain processing element of the device. In addition, all or part of these units can be integrated together, or they can be implemented independently. The processing element described here can also be called a processor, which can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each unit above can be implemented by an integrated logic circuit of hardware in the processor element or in the form of software called by a processing element.

[0278] In one example, the unit in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more microprocessors (digital singnal processors, DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0279] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0280] For example, the embodiment of the present application may also provide a vehicle motion state recognition device, including: an interface circuit for receiving data transmitted by other devices; a processor connected to the interface circuit and used to execute the various steps described in the above method. The processor may include one or more.

[0281] In one implementation, the modules that implement the corresponding steps in the above method can be implemented in the form of a processing element scheduling program. For example, the vehicle motion state recognition device may include a processing element and a storage element, and the processing element calls the program stored in the storage element to execute the method described in the above method embodiment. The storage element can be a storage element on the same chip as the processing element, that is, an on-chip storage element.

[0282] In another implementation, the program for implementing the above method may be in a storage element on a different chip from the processing element, i.e., an off-chip storage element. In this case, the processing element calls or loads the program from the off-chip storage element to the on-chip storage element to call and execute the method described in the above method embodiment.

[0283] For example, an embodiment of the present application may also provide a vehicle including a processor, where the processor is used to connect to a memory and call a program stored in the memory to execute the method described in the above method embodiment.

[0284] Alternatively, an embodiment of the present application may also provide a server including a processor, which can communicate remotely with the vehicle, and the processor is used to connect to the memory and call the program stored in the memory to execute the method described in the above method embodiment.

[0285] Alternatively, the embodiment of the present application may also provide a vehicle driving system, such as an automatic driving system or an assisted driving system. The vehicle driving system may be composed of a vehicle and a server, or may be deployed separately in a vehicle or a server. The vehicle driving system includes a processor, the processor being used to connect to a memory, calling a program stored in the memory, so as to execute the method described in the above method embodiment.

[0286] In another implementation, the module for implementing each step in the above method may be configured as one or more processing elements, which may be arranged on a terminal. The processing elements here may be integrated circuits, such as one or more ASICs, or one or more DSPs, or one or more FPGAs, or a combination of these integrated circuits. These integrated circuits may be integrated together to form a chip.

[0287] In another implementation, the modules for implementing the various steps in the above method can be integrated together and implemented in the form of a SOC, and the SOC chip is used to implement the corresponding method. The chip can integrate at least one processing element and a storage element, and the corresponding method can be implemented in the form of a program stored in the storage element by the processing element; or, the chip can integrate at least one integrated circuit to implement the corresponding method; or, the above implementation methods can be combined, and the functions of some units can be implemented in the form of a processing element calling a program, and the functions of some units can be implemented in the form of an integrated circuit.

[0288] The processing element here is the same as described above, and can be a general-purpose processor, such as a CPU, or can be one or more integrated circuits configured to implement the above method, such as: one or more ASICs, or, one or more microprocessors DSPs, or, one or more FPGAs, etc., or a combination of at least two of these integrated circuit forms.

[0289] A storage element may be a memory or a collective term for multiple storage elements.

[0290] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0291] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0292] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0293] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0294] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, such as a program. The software product is stored in a program product, such as a computer-readable storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0295] For example, an embodiment of the present application may also provide a computer-readable storage medium, including: computer software instructions; when the computer software instructions are executed in a vehicle motion state identification device or in a chip built into the vehicle motion state identification device, the vehicle motion state identification device executes the method described in the aforementioned method embodiment.

[0296] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle motion state recognition method, characterized in that: The method comprises: Acquire first lane information, second lane information, and position information and motion information of a second vehicle; the first lane information is lane information of a lane where the first vehicle is located; the second lane information includes at least two of lane information of a lane where the second vehicle is located, lane information of a first adjacent lane to the lane where the second vehicle is located, and lane information of a second adjacent lane to the lane where the second vehicle is located; Determine at least two predicted driving trajectories of the second vehicle according to the second lane information, and the position information and motion information of the second vehicle; each of the predicted driving trajectories corresponds to each type of lane information included in the second lane information; Determining a motion state of the second vehicle relative to the first vehicle according to the at least two predicted driving trajectories and the first lane information; The determining, based on the at least two predicted driving trajectories and the first lane information, a motion state of the second vehicle relative to the first vehicle comprises: Determining a target predicted driving trajectory of the second vehicle according to the at least two predicted driving trajectories and a similarity between each of the predicted driving trajectories and a historical driving trajectory of the second vehicle; The motion state of the second vehicle relative to the first vehicle is determined according to the target predicted driving trajectory and the first lane information.

2. The method according to claim 1, characterized in that Before determining the target predicted driving trajectory of the second vehicle according to the at least two predicted driving trajectories and the similarity between each of the predicted driving trajectories and the historical driving trajectory of the second vehicle, the method further includes: Acquire a first transverse coordinate vector of a historical driving trajectory of the second vehicle within a first preset time period; wherein the first transverse coordinate vector is used to indicate a distance vector between the historical driving trajectory and a lane line of a lane where the second vehicle is located; For each of the predicted driving trajectory: obtain a second lateral coordinate vector of the predicted driving trajectory within a second preset time period, and determine the similarity between the predicted driving trajectory and the historical driving trajectory based on the second lateral coordinate vector and the first lateral coordinate vector; wherein the second lateral coordinate vector is used to indicate a distance vector between the predicted driving trajectory and the lane line of the lane where the second vehicle is located.

3. The method according to claim 1 or 2, characterized in that: The determining a target predicted driving trajectory of the second vehicle according to the at least two predicted driving trajectories and the similarity between each of the predicted driving trajectories and a historical driving trajectory of the second vehicle comprises: Predicting the similarity between each predicted driving trajectory and the actual driving trajectory of the second vehicle according to the similarity between each predicted driving trajectory and the historical driving trajectory of the second vehicle; A target predicted driving trajectory of the second vehicle is determined according to the at least two predicted driving trajectories and the similarity between each of the predicted driving trajectories and the actual driving trajectory of the second vehicle.

4. The method according to claim 1 or 2, characterized in that: The determining, according to the target predicted driving trajectory and the first lane information, a motion state of the second vehicle relative to the first vehicle comprises: Determine, according to the target predicted driving trajectory and the first lane information, a minimum lateral distance between the target predicted driving trajectory and a lane line of a lane where the first vehicle is located within a preset longitudinal length; If the minimum lateral distance is greater than a first threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is that the second vehicle is traveling in a different lane from the first vehicle.

5. The method according to claim 4, characterized in that The method further comprises: Determine, according to the target predicted driving trajectory and the first lane information, a maximum lateral distance between the target predicted driving trajectory and a lane line of a lane where the first vehicle is located within the preset longitudinal length; If the minimum lateral distance is less than the first threshold and the maximum lateral distance is greater than a second threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is crossing the lane where the first vehicle is located; wherein the second threshold is greater than the first threshold; If the maximum lateral distance is less than a third threshold, determining that the motion state of the second vehicle relative to the first vehicle is to maintain driving in the same lane as the first vehicle; wherein the third threshold is less than the first threshold; If the minimum lateral distance is less than the first threshold, the maximum lateral distance is greater than the third threshold and less than the second threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located, or cutting out of the lane where the first vehicle is located.

6. The method according to claim 5, characterized in that The method further comprises: Acquire a first longitudinal position corresponding to the minimum transverse distance and a second longitudinal position corresponding to the maximum transverse distance; When the minimum lateral distance is less than the first threshold value, and the maximum lateral distance is greater than the third threshold value and less than the second threshold value, determining that the motion state of the second vehicle relative to the first vehicle is cutting into a lane where the first vehicle is located, or cutting out of a lane where the first vehicle is located, includes: If the time when the second longitudinal position appears in the target predicted driving trajectory is earlier than the time when the first longitudinal position appears, determining that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located; If the time when the first longitudinal position appears in the target predicted driving trajectory is earlier than the time when the second longitudinal position appears, it is determined that the motion state of the second vehicle relative to the first vehicle is cutting out of the lane where the first vehicle is located.

7. The method according to claim 6, characterized in that The method further comprises: Acquire a longitudinal distance between the first longitudinal position and a first end of the preset longitudinal length; wherein the longitudinal distance is a distance between the first longitudinal position and the first end of the preset longitudinal length in a direction parallel to a lane line of a lane where the first vehicle is located, and the first end of the preset longitudinal length is an end close to the first vehicle; If the longitudinal distance is less than a fourth threshold, determining that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located according to a first cutting-in state, or cutting out of the lane where the first vehicle is located according to a first cutting-out state; If the longitudinal distance is greater than a fourth threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located according to a second cutting-in state, or cutting out of the lane where the first vehicle is located according to a second cutting-out state.

8. The method according to claim 1 or 2, characterized in that: The motion information of the second vehicle includes a lateral speed of the second vehicle; before determining at least two predicted driving trajectories of the second vehicle according to the second lane information and the position information and motion information of the second vehicle, the method further includes: The lateral velocity included in the motion information of the second vehicle is corrected according to an average historical lateral velocity of the second vehicle.

9. A vehicle motion state recognition device, characterized in that: The device comprises: an acquisition module, configured to acquire first lane information, second lane information, and position information and motion information of a second vehicle; the first lane information is lane information of a lane where the first vehicle is located; the second lane information includes at least two of lane information of a lane where the second vehicle is located, lane information of a first adjacent lane to the lane where the second vehicle is located, and lane information of a second adjacent lane to the lane where the second vehicle is located; a prediction module, configured to determine at least two predicted driving trajectories of the second vehicle according to the second lane information and the position information and motion information of the second vehicle; each of the predicted driving trajectories corresponds to each type of lane information included in the second lane information; a determination module, configured to determine a motion state of the second vehicle relative to the first vehicle based on the at least two predicted driving trajectories and the first lane information; The determination module is specifically configured to determine a target predicted driving trajectory of the second vehicle according to the at least two predicted driving trajectories and a similarity between each of the predicted driving trajectories and a historical driving trajectory of the second vehicle; The motion state of the second vehicle relative to the first vehicle is determined according to the target predicted driving trajectory and the first lane information.

10. The device according to claim 9, characterized in that The determination module is further used to obtain a first transverse coordinate vector of the historical driving trajectory of the second vehicle within a first preset time period; wherein the first transverse coordinate vector is used to indicate a distance vector between the historical driving trajectory and a lane line of a lane where the second vehicle is located; For each of the predicted driving trajectory: obtain a second lateral coordinate vector of the predicted driving trajectory within a second preset time period, and determine the similarity between the predicted driving trajectory and the historical driving trajectory based on the second lateral coordinate vector and the first lateral coordinate vector; wherein the second lateral coordinate vector is used to indicate a distance vector between the predicted driving trajectory and the lane line of the lane where the second vehicle is located.

11. The device according to claim 9 or 10, characterized in that The determination module is specifically configured to predict the similarity between each of the predicted driving trajectories and the actual driving trajectory of the second vehicle according to the similarity between each of the predicted driving trajectories and the historical driving trajectory of the second vehicle; A target predicted driving trajectory of the second vehicle is determined according to the at least two predicted driving trajectories and the similarity between each of the predicted driving trajectories and the actual driving trajectory of the second vehicle.

12. The device according to claim 9 or 10, characterized in that The determination module is specifically configured to determine, based on the target predicted driving trajectory and the first lane information, a minimum lateral distance between the target predicted driving trajectory and a lane line of a lane where the first vehicle is located within a preset longitudinal length; If the minimum lateral distance is greater than a first threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is that the second vehicle is traveling in a different lane from the first vehicle.

13. The device according to claim 12, characterized in that The determination module is further used to determine the maximum lateral distance between the target predicted driving trajectory and the lane line of the lane where the first vehicle is located within the preset longitudinal length according to the target predicted driving trajectory and the first lane information; If the minimum lateral distance is less than the first threshold and the maximum lateral distance is greater than a second threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is crossing the lane where the first vehicle is located; wherein the second threshold is greater than the first threshold; If the maximum lateral distance is less than a third threshold, determining that the motion state of the second vehicle relative to the first vehicle is to maintain driving in the same lane as the first vehicle; wherein the third threshold is less than the first threshold; If the minimum lateral distance is less than the first threshold, the maximum lateral distance is greater than the third threshold and less than the second threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located, or cutting out of the lane where the first vehicle is located.

14. The device according to claim 13, characterized in that The determination module is further used to obtain a first longitudinal position corresponding to the minimum transverse distance and a second longitudinal position corresponding to the maximum transverse distance; When the minimum lateral distance is less than the first threshold value, and the maximum lateral distance is greater than the third threshold value and less than the second threshold value, if the time when the second longitudinal position appears in the target predicted driving trajectory is earlier than the time when the first longitudinal position appears, it is determined that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located; If the time when the first longitudinal position appears in the target predicted driving trajectory is earlier than the time when the second longitudinal position appears, it is determined that the motion state of the second vehicle relative to the first vehicle is cutting out of the lane where the first vehicle is located.

15. The device according to claim 14, characterized in that The determination module is further used to obtain a longitudinal distance between the first longitudinal position and a first end of the preset longitudinal length; wherein the longitudinal distance is a distance between the first longitudinal position and the first end of the preset longitudinal length in a direction parallel to a lane line of a lane where the first vehicle is located, and the first end of the preset longitudinal length is an end close to the first vehicle; If the longitudinal distance is less than a fourth threshold, determining that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located according to a first cutting-in state, or cutting out of the lane where the first vehicle is located according to a first cutting-out state; If the longitudinal distance is greater than a fourth threshold, it is determined that the motion state of the second vehicle relative to the first vehicle is cutting into the lane where the first vehicle is located according to a second cutting-in state, or cutting out of the lane where the first vehicle is located according to a second cutting-out state.

16. The device according to claim 9 or 10, characterized in that The motion information of the second vehicle includes a lateral velocity of the second vehicle; The prediction module is further used to correct the lateral speed included in the motion information of the second vehicle according to the average historical lateral speed of the second vehicle.

17. A vehicle motion state recognition device, characterized in that: include: An interface circuit for receiving data transmitted by other devices; A processor connected to the interface circuit and configured to execute the method according to any one of claims 1 to 8.

18. A vehicle, characterized in that: include: A processor, wherein the processor is connected to a memory and calls a program stored in the memory to execute the method according to any one of claims 1 to 8.

19. A server, characterized in that: include: A processor, wherein the processor is connected to a memory and calls a program stored in the memory to execute the method according to any one of claims 1 to 8.

20. A vehicle driving system, characterized in that: include: A processor, wherein the processor is connected to a memory and calls a program stored in the memory to execute the method according to any one of claims 1 to 8.

21. A computer-readable storage medium, characterized in that: include: Computer software instructions; When the computer software instructions are executed in a vehicle motion state recognition device, the vehicle motion state recognition device is enabled to perform the method according to any one of claims 1 to 8.

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