Vehicle control method, system, device, vehicle, storage medium and program product

Abstract

The application relates to the technical field of automobile electronics, and discloses a vehicle control method, a vehicle control system, a vehicle control device, a vehicle, a storage medium and a program product. The vehicle control method comprises the following steps: when it is detected that a split-flow triggering condition is met, for example, when it is detected that there are N split-flow lanes in front of the vehicle, an electronic device can acquire sensing data of the vehicle, a historical driving track of the vehicle and an electronic map. Then, the future driving track of the vehicle in a future time period can be predicted based on the sensing data, the historical driving track and the electronic map. Finally, a target split-flow lane can be selected from the N split-flow lanes based on the deviation degree between the future driving track and each split-flow lane. In this way, the electronic device can accurately identify the target split-flow lane in the N split-flow lanes in advance, so that the autonomous vehicle can drive in the target split-flow lane.

Description

Vehicle control methods, systems, equipment, vehicles, storage media, and software products Technical Field

[0001] This application relates to the field of automotive electronics technology, and in particular to a vehicle control method, system, device, vehicle, storage medium, and program product. Background Technology

[0002] When an autonomous vehicle's navigation is not activated, it will default to choosing the straight-ahead lane when encountering lane separation situations. For example, as shown in Figure 1, vehicle 100 may encounter lane separation situations when its navigation is not activated. For instance, the lanes ahead of vehicle 100 may include a straight-ahead lane 101 and a left-turn lane 102. In this case, vehicle 100 may default to choosing the straight-ahead lane 101. Therefore, accurately identifying the target lane (such as a straight-ahead lane) in advance among multiple lanes, so that the autonomous vehicle can drive in the target lane, is a pressing problem that needs to be solved. Summary of the Invention

[0003] This application provides a vehicle control method, system, device, vehicle, storage medium, and program product for identifying a target divergence lane among multiple divergence lanes.

[0004] Firstly, this application provides a vehicle control method, comprising: detecting that a diversion trigger condition is met; acquiring vehicle perception data, the vehicle's historical driving trajectory, and an electronic map corresponding to the vehicle's current location; wherein the diversion trigger condition includes the existence of N diversion lanes ahead of the vehicle's current location, where N is an integer greater than 1. Based on the vehicle's perception data, historical driving trajectory, and electronic map, predicting the vehicle's future driving trajectory within a future time period. Based on the degree of deviation between the future driving trajectory and each of the N diversion lanes, selecting a target diversion lane from the N diversion lanes. Controlling the vehicle to drive in the target diversion lane.

[0005] Thus, by using the above method, when the electronic device detects that there are N divergence lanes ahead, it can accurately identify the target divergence lane (such as a straight-ahead divergence lane) in advance among the N divergence lanes, so that the autonomous vehicle can drive in the target divergence lane.

[0006] In one possible implementation of the first aspect above, the diversion triggering conditions also include: the vehicle's navigation function is turned off, or no lane landmarks corresponding to the N diversion lanes are detected.

[0007] In this way, even when the navigation function is turned off or lane landmarks cannot be obtained, electronic devices can accurately identify the target diversion lane (such as a straight-ahead diversion lane) in advance among N diversion lanes, so that autonomous vehicles can drive in the target diversion lane.

[0008] In one possible implementation of the first aspect above, the method further includes: detecting that the diversion triggering condition is met, and, if lane landmarks corresponding to the N diversion lanes are detected, selecting a target diversion lane from the N diversion lanes based on the lane landmarks.

[0009] In this way, when the electronic equipment can obtain lane landmarks, it can quickly determine the target diversion lane based on the lane landmarks.

[0010] In one possible implementation of the first aspect described above, selecting a target diversion lane from the N diversion lanes based on the degree of deviation between the future driving trajectory and each of the N diversion lanes includes: mapping the future driving trajectory and the sensing data to the same coordinate system, wherein the sensing data includes image data of the N diversion lanes; determining lane reference lines corresponding to each of the N diversion lanes based on the image data; determining the degree of deviation between the future driving trajectory and the lane reference lines based on the distance between each sampling point in the future driving trajectory and the lane reference lines; and selecting the diversion lane corresponding to the lane reference line with the smallest degree of deviation from the future driving trajectory as the target diversion lane.

[0011] In this way, by calculating the degree of deviation between the future driving trajectory and the reference lines of each lane to determine the target diversion lane, the specific target diversion lane to which the future driving trajectory belongs can be determined.

[0012] In one possible implementation of the first aspect above, determining the degree of deviation between the future driving trajectory and the lane reference line based on the distance between each sampling point in the future driving trajectory and the lane reference line includes: obtaining the distance between each sampling point in the future driving trajectory and the j-th lane reference line respectively; and taking the sum of the distances between each sampling point in the future driving trajectory and the j-th lane reference line as the degree of deviation between the future driving trajectory and the j-th lane reference line, where j is a positive integer less than or equal to N.

[0013] In one possible implementation of the first aspect above, selecting the target diverging lane from N diverging lanes based on lane landmarks includes: determining a lane topology map based on the image data corresponding to the lane in the perception data, wherein the lane includes N diverging lanes and N subsequent lanes of the diverging lanes, and the lane topology map is used to characterize the connection relationship between each lane in the lane and the lane extension direction; at the first lane diverging point in the lane topology map, dividing the N diverging lanes and N subsequent lanes of the diverging lanes into N diverging lane groups according to the lane extension direction; and selecting the target diverging lane group containing the target diverging lane from the N diverging lane groups based on lane landmarks, wherein the first diverging lane in the target diverging lane group is the target diverging lane.

[0014] Thus, by using the above method, when the electronic device detects that there are N diverging lanes in front of the vehicle, if the electronic device can detect the lane markers of the corresponding N diverging lanes, the electronic device can quickly and accurately identify the target diverging lane (such as a straight diverging lane) in advance based on the lane markers, so that the autonomous vehicle can drive in the target diverging lane.

[0015] Secondly, this application provides a vehicle control system, including: a first detection module, a perception data acquisition module, a historical driving trajectory acquisition module, an electronic map acquisition module, a prediction module, a target diversion lane selection module, and a control module.

[0016] The detection module is used to detect whether the diversion triggering conditions are met. The diversion triggering conditions include the existence of N diversion lanes ahead of the vehicle's current position.

[0017] The sensing data acquisition module is used to acquire sensing data when the diversion triggering conditions are met.

[0018] The historical driving trajectory acquisition module is used to acquire the historical driving trajectory of a vehicle when the diversion triggering conditions are met.

[0019] The electronic map acquisition module is used to acquire the electronic map corresponding to the current location of the vehicle when the diversion triggering conditions are met.

[0020] The prediction module is used to predict the vehicle's future driving trajectory within a future time period based on the vehicle's perception data, historical driving trajectory, and electronic map.

[0021] The target diversion lane selection module is used to select the target diversion lane from the N diversion lanes based on the degree of deviation between the future driving trajectory and each of the N diversion lanes.

[0022] The control module is used to control the vehicle's movement in the target diversion lane.

[0023] Thus, through the vehicle control system described above, if N divergence lanes are detected ahead of the vehicle, the target divergence lane (such as a straight-ahead divergence lane) can be accurately identified in advance among the N divergence lanes, so that the autonomous vehicle can drive in the target divergence lane.

[0024] Thirdly, this application provides an electronic device comprising: at least one memory and at least one processor, wherein the memory is coupled to the processor. The memory is used to store computer program code / instructions. When the computer program code / instructions are executed by the processor, the electronic device performs the vehicle control method mentioned in the first aspect and any possible implementation thereof.

[0025] Fourthly, this application provides a vehicle that includes the electronic equipment mentioned in the third aspect above.

[0026] Fifthly, this application provides a readable storage medium storing instructions. When executed on an electronic device, the instructions cause the electronic device to perform the vehicle control method mentioned in the first aspect and any possible implementation thereof.

[0027] Sixthly, this application provides a computer program product, including: computer instructions. When the computer instructions are executed on an electronic device, they cause the electronic device to perform the vehicle control method mentioned in the first aspect and any possible implementation thereof.

[0028] The beneficial effects of the third to sixth aspects mentioned above can be referred to the relevant descriptions in the first and second aspects and various possible implementations of the first aspect, and will not be repeated here. Attached Figure Description

[0029] Figure 1 illustrates a schematic diagram of a vehicle selecting a straight-ahead divergence lane for travel, according to some embodiments;

[0030] Figure 2 shows a schematic flowchart of a vehicle control method according to some embodiments of this application;

[0031] Figure 3 illustrates a schematic diagram of determining a target diversion lane based on a future driving trajectory, according to some embodiments of this application.

[0032] Figure 4 illustrates a schematic diagram of reasoning about diverging lanes based on lane landmarks, according to some embodiments of this application.

[0033] Figure 5 illustrates a flowchart of selecting a target diversion lane from N diversion lanes based on lane landmarks, according to some embodiments of this application.

[0034] Figure 6 illustrates a flowchart of determining a target diversion lane based on lane landmarks and lane topology maps, according to some embodiments of this application.

[0035] Figure 7 shows a schematic diagram of a vehicle control system according to some embodiments of this application;

[0036] Figure 8 shows a schematic diagram of the structure of a vehicle according to some embodiments of this application. Detailed Implementation

[0037] The illustrative embodiments of this application include, but are not limited to, a vehicle control method, system, device, vehicle, storage medium, and program product.

[0038] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0039] As mentioned earlier, if a lane separation occurs, the vehicle can default to choosing the straight-ahead lane. As shown in Figure 1, when the navigation function is not activated, vehicle 100 may encounter a lane separation situation. For example, the lanes ahead of vehicle 100 may include a straight-ahead lane 101 and a left-turn lane 102. In this case, vehicle 100 can default to choosing the straight-ahead lane 101.

[0040] Currently, electronic devices (such as vehicle-mounted systems) can acquire road landmarks based on visual perception (such as radar or cameras) and determine the straight-ahead divergence lanes among N divergence lanes based on the road landmarks.

[0041] However, if some roads do not have road signs, or if some lane signs (such as left and right turn signs) are far from the vehicles, the vehicles may not be able to perceive the road signs visually, and the electronic equipment will also be unable to determine the straight-ahead lane from the N divergence lanes based on the road signs.

[0042] To address the aforementioned issues, this application proposes a vehicle control method. Specifically, when an electronic device detects that a diversion trigger condition is met, such as detecting the existence of N diversion lanes ahead of the vehicle's current position, the electronic device can acquire the vehicle's perception data (such as image data acquired by an onboard camera), the vehicle's historical driving trajectory, and the electronic map corresponding to the vehicle's current location. Next, based on the perception data, historical driving trajectory, and electronic map, the electronic device can predict the vehicle's future driving trajectory within a future time period. Finally, based on the degree of deviation between the future driving trajectory and each diversion lane, the electronic device can select a target diversion lane from the N diversion lanes. For example, if diversion lanes A and B exist, and the degree of deviation between the future driving trajectory and diversion lane A is less than the degree of deviation between the future driving trajectory and diversion lane B, the electronic device can select diversion lane A, which has a smaller degree of deviation, as the target diversion lane.

[0043] In this way, even if the electronic device cannot obtain lane landmarks, it can accurately identify the target diversion lane (such as a straight-ahead diversion lane) in advance among the N diversion lanes, so that the autonomous vehicle can drive in the target diversion lane.

[0044] It should be noted that the electronic device mentioned in this application can be any vehicle equipment such as an in-vehicle computer, vehicle infotainment system, or in-vehicle electronic device. Alternatively, the electronic device mentioned in this application can also be any terminal device capable of data interaction with the vehicle, such as a mobile phone, wearable device, tablet computer, or terminal in self-driving vehicles. This application does not limit the specific form of the electronic device in its embodiments.

[0045] The vehicle control method provided in this application embodiment will be described below with reference to the flowchart shown in Figure 2. This method can be applied to electronic devices, such as the aforementioned vehicle infotainment system. Specifically, as shown in Figure 2, the method is as follows:

[0046] S201: The diversion trigger condition is detected, and the vehicle's perception data, historical driving trajectory, and electronic map corresponding to the vehicle's current location are obtained.

[0047] In some embodiments, the diversion triggering condition may include the existence of N diversion lanes ahead of the vehicle's current position, where N is an integer greater than 1. That is, if the electronic device detects that there are N diversion lanes (such as intersections) ahead of the vehicle, the electronic device may start executing S201 to S204 to select a target diversion lane (such as a straight-ahead diversion lane) from among the multiple diversion lanes, so that the vehicle can travel in the target diversion lane.

[0048] For example, an electronic device can determine whether there are N diverging lanes ahead of the vehicle's current position using perception data collected by vehicle sensors. Alternatively, the electronic device can also determine whether there are N diverging lanes ahead of the vehicle's current position from an electronic map. This application does not limit this.

[0049] It is understood that in some embodiments, once the electronic device detects that there are N divergence lanes ahead of the vehicle, it can select the target divergence lane (such as a straight-ahead divergence lane) from among the multiple divergence lanes by executing the methods shown in S201 to S204. That is, regardless of whether there are lane markers corresponding to the N divergence lanes on the road, the electronic device can select the target divergence lane from among the multiple divergence lanes by executing the methods shown in S201 to S204. The lane markers can be used to indicate the permitted direction of travel for the vehicle ahead.

[0050] In other embodiments, when the electronic device detects that there are N divergence lanes ahead of the vehicle, it may also select the target divergence lane from the multiple divergence lanes by executing the methods shown in S201 to S204 only if the vehicle navigation function is turned off or if the lane markers corresponding to the N divergence lanes are not detected. That is, the divergence triggering conditions may also include the vehicle navigation function being turned off or the electronic device not detecting the lane markers corresponding to the N divergence lanes.

[0051] In some embodiments, if there are N divergence lanes ahead of the vehicle, and the electronic device also detects lane markers for the corresponding N divergence lanes, the electronic device can directly select the target divergence lane from the N divergence lanes based on the lane markers. The specific process of determining the target divergence lane based on the lane markers is illustrated in Figure 5 and will not be detailed here.

[0052] For example, electronic devices can determine whether lane markers corresponding to the N divergence lanes exist on the road based on perception data collected by vehicle sensors. For instance, electronic devices can determine whether lane markers exist on the road by detecting the presence of lane markers in image data of the surrounding environment captured by an onboard camera.

[0053] For example, the vehicle's perception data can be sensor data collected by sensors such as cameras or radar, for instance, image data of the surrounding environment captured by a camera. Furthermore, the vehicle sensors can also transmit the collected perception data to electronic devices.

[0054] For example, the vehicle's historical driving trajectory can be location information of the vehicle in previous time periods obtained through a positioning module such as a Global Positioning System (GPS). Furthermore, the GPS positioning module can also send the acquired vehicle location information to an electronic device. Alternatively, the vehicle's historical driving trajectory can also include acceleration and / or angular velocity information of the vehicle in previous time periods collected by inertial sensors such as accelerometers and / or gyroscopes. Furthermore, the accelerometers and / or gyroscopes can also send the acquired acceleration and / or angular velocity information to an electronic device.

[0055] For example, the electronic map corresponding to the vehicle's current location can be downloaded from the cloud by the electronic device, or it can be pre-stored in the electronic device.

[0056] S202: Based on vehicle perception data, historical driving trajectories, and electronic maps, predict the vehicle's future driving trajectory within a future time period.

[0057] In some embodiments, the electronic device can determine the future driving trajectory corresponding to the current vehicle's perception data, historical driving trajectory, and electronic map based on the pre-learned correlation between perception data, historical driving trajectory, electronic map, and future driving trajectory.

[0058] In some embodiments, developers can also pre-train an inference model and deploy it in the electronic device. The inference model can be used to infer the vehicle's future trajectory based on received perception data, historical driving trajectories, and electronic maps. Thus, when the electronic device needs to select a target diversion lane from N diversion lanes, it can directly input the perception data, historical driving trajectories, and electronic maps into the inference model to obtain the vehicle's future trajectory over a future time period.

[0059] In some embodiments, when training the inference model, developers can collect a large amount of vehicle trajectory data before the divergence point (such as an intersection), vehicle trajectory data on the target divergence lane when passing the divergence point (such as manual driving trajectory data), vehicle perception data, and lane topology information in electronic maps (such as information representing the connection relationship or lane extension direction of each lane), and use this as a training set to train the inference model. Specifically, during the training process, the vehicle trajectory data before the divergence point (such as an intersection), the vehicle perception data, and the lane topology information in electronic maps can be used as input data in the training set, and the vehicle trajectory data on the target divergence lane when passing the divergence point can be used as output data in the training set. This allows the inference model to learn the data features and / or correlations between the input and output data, thereby achieving the training process of the inference model.

[0060] In the acquired training data, if the lane topology information in the electronic map contains errors, the manual driving trajectory data can be prioritized as the accurate data. For example, if a manually driven vehicle is traveling on the target divergence lane A1 when passing the divergence point, but the target divergence lane A1 is not present in the lane topology information of the electronic map, then the manual driving trajectory data can be used as the accurate data to train the inference model.

[0061] S203: Based on the degree of deviation between the future driving trajectory and each of the N divergence lanes, select the target divergence lane from the N divergence lanes.

[0062] In some embodiments, the future driving trajectory can refer to the distance and direction of the vehicle's position in each future time period relative to its current position. Therefore, after acquiring the future driving trajectory, the electronic device can also map the future driving trajectory and the perception data into the same coordinate system, thereby mapping the future driving trajectory to the actual driving environment to calculate the degree of deviation between the future driving trajectory and each diversion lane. For example, as shown in Figure 3, the electronic device can map the future driving trajectory 301 and the perception data into the same coordinate system based on the distance and direction of the vehicle's position in each future time period indicated by the future driving trajectory 301 relative to the vehicle's current position, and based on the distance and direction of each data point in the perception data relative to the vehicle's current position, thereby mapping the future driving trajectory 301 to the actual driving environment. The perception data may include image data of N (N is an integer greater than 1) diversion lanes. For example, as shown in Figure 3, the perception data may include image data corresponding to diversion lanes 302 and 303.

[0063] Next, after mapping the future driving trajectory onto the actual driving environment, the electronic device can determine the lane reference lines (also known as slice projections) for each of the N diverging lanes based on the image data corresponding to the diverging lanes in the perception data. For example, as shown in Figure 3, the lane reference line 3021 corresponding to diverging lane 302 can refer to the center line of diverging lane 302, and the lane reference line 3031 corresponding to diverging lane 303 can refer to the center line of diverging lane 303. Alternatively, the electronic device can also use the edge lines of the diverging lanes as the lane reference lines for the corresponding diverging lanes; this application does not limit this.

[0064] Next, after determining the lane reference lines corresponding to each diversion lane, the electronic device can determine the degree of deviation between the future driving trajectory and each lane reference line based on the distance between each sampling point (also called a switching point) in the future driving trajectory and each lane reference line. Each sampling point can be determined at preset intervals in the future driving trajectory; for example, a sampling point can be determined every 3 meters (or 5 meters, 10 meters, etc.) in the driving trajectory. Alternatively, each sampling point can be determined based on the vehicle's current driving speed and a preset interval time in the future driving trajectory. For example, if the vehicle's current driving speed is 60 meters per second and the preset interval time is 0.1 seconds (or 0.05 seconds, 2 seconds, etc.), the electronic device can determine a sampling point every "60 meters per second × 0.1 seconds" in the future driving trajectory. This application does not limit the method of determining each sampling point in the future driving trajectory.

[0065] For example, as shown in Figure 3, the electronic device can determine the degree of deviation between the future driving trajectory 301 and the lane reference line 3021 based on the distance d1 between each sampling point in the future driving trajectory 301 and the lane reference line 3021. Similarly, the electronic device can also determine the degree of deviation between the future driving trajectory 301 and the lane reference line 3031 based on the distance d2 between each sampling point in the future driving trajectory 301 and the lane reference line 3031. The process of the electronic device determining the degree of deviation between the future driving trajectory 301 and the lane reference line 3021 will be described below using lane reference line 3021 (as an example of the j-th lane reference line) as an example.

[0066] For example, after obtaining the distances between each sampling point in the future driving trajectory 301 and the lane reference line 3021, the electronic device can use the sum of the distances between each sampling point in the future driving trajectory 301 and the lane reference line 3021 as the degree of deviation between the future driving trajectory 301 and the lane reference line 3021. Specifically, refer to formula (1):

[0067] (1);

[0068] Among them, cost j This represents the deviation between the future driving trajectory (e.g., future driving trajectory 301) and the j-th lane reference line (e.g., lane reference line 3021) out of N lane reference lines, where N is an integer greater than 1, and j is a positive integer less than or equal to N. m refers to the number of sampling points in the future driving trajectory. d ij It is the distance between the i-th sampling point out of m sampling points and the j-th lane reference line.

[0069] Thus, using the above formula (1), the electronic device can determine the degree of deviation between the future driving trajectory and the reference lines of each lane.

[0070] Next, after determining the degree of deviation between the future driving trajectory and each lane reference line, the electronic device can select the diversion lane corresponding to the lane reference line with the smallest deviation as the target diversion lane. For example, as shown in Figure 3, if the degree of deviation between the future driving trajectory 301 and lane reference line 3031 is less than the degree of deviation between the future driving trajectory 301 and lane reference line 3021, the electronic device can select the diversion lane 303 corresponding to lane reference line 3031 with the smallest deviation as the target diversion lane.

[0071] In this way, by calculating the degree of deviation between the future driving trajectory and the reference lines of each lane to determine the target diversion lane, the specific target diversion lane to which the future driving trajectory belongs can be determined.

[0072] S204: Control vehicles to travel in the target diversion lane.

[0073] Thus, by using the above method, when the electronic device detects that there are N divergence lanes in front of the vehicle, it can accurately identify the target divergence lane (such as a straight divergence lane) in advance among the N divergence lanes, so that the autonomous vehicle can drive in the target divergence lane.

[0074] Furthermore, in some embodiments, once the electronic device detects that there are N divergence lanes in front of the vehicle, the electronic device can select the target divergence lane (such as a straight-ahead divergence lane) from the multiple divergence lanes by executing the methods shown in S201 to S204 in Figure 2.

[0075] In other embodiments, when the electronic device detects that there are N diverging lanes in front of the vehicle, the electronic device may also select the target diverging lane from the multiple diverging lanes by executing the methods shown in S201 to S204 only when the vehicle navigation function is turned off or when no lane landmarks corresponding to the N diverging lanes are detected.

[0076] In other embodiments, when the electronic device detects N diverging lanes ahead of the vehicle's current position, if it also detects lane markers corresponding to those N diverging lanes, it can directly select the target diverging lane from among the N diverging lanes based on the lane markers. For example, as shown in Figure 4, after detecting lane markers 403 corresponding to diverging lanes 401 and 402, the electronic device can directly infer from lane markers 403 that diverging lane 401 is a left-turn diverging lane and determine that diverging lane 402 is a straight-ahead diverging lane. Furthermore, the electronic device can determine the target diverging lane from diverging lanes 401 and 402 to control the vehicle 100 to travel in the target diverging lane. The specific process of determining the target diverging lane based on lane markers can be found in Figure 5.

[0077] The process of selecting the target diversion lane from N diversion lanes based on lane landmarks, as mentioned above, will be described below with reference to the flowchart shown in Figure 5. This method can be applied to electronic devices, such as the vehicle-mounted systems mentioned above. Specifically, as shown in Figure 5, the method is as follows:

[0078] S501: The diversion trigger condition has been detected and the lane landmarks corresponding to the N diversion lanes have been detected.

[0079] In some embodiments, the diversion triggering condition may include the existence of N diversion lanes ahead of the vehicle's current position, where N is an integer greater than 1. See step S201 in Figure 2 for details, which will not be repeated here.

[0080] In some embodiments, the electronic device can determine whether there are lane markers corresponding to N divergence lanes ahead of the vehicle's current position based on perception data collected by vehicle sensors. For example, the electronic device can detect whether there are lane markers corresponding to N divergence lanes in the image data of the surrounding environment captured by the vehicle-mounted camera. If the existence of lane markers is determined, the electronic device can execute S502 to S504 below to select the target divergence lane from the N divergence lanes based on the lane markers.

[0081] S502: Determine the lane topology map.

[0082] In some embodiments, the electronic device can determine a lane topology map based on image data corresponding to the lanes in the perception data. A lane can include N divergence lanes and N subsequent lanes of each divergence lane, where the subsequent lanes can be empty. For example, as shown in Figure 6, the lane topology map can be used to characterize the connection relationships between lanes and the lane extension directions. A lane can include divergence lane 601, divergence lane 602, the subsequent lane of divergence lane 601, and the subsequent lane of divergence lane 6021. The subsequent lane of divergence lane 601 can be empty, meaning there are no other lanes in the lane extension direction of divergence lane 601.

[0083] S503: Perform lane topology map parsing.

[0084] In some embodiments, after determining the lane topology map, the electronic device can divide the N diverging lanes and their successor lanes into N diverging lane groups at the first lane diverging point of the lane topology map, according to the lane extension direction. For example, as shown in Figure 6, after determining the lane topology map, the electronic device can divide diverging lane 601, diverging lane 602, and their successor lane 6021 into diverging lane group F11 and diverging lane group F12 at the first lane diverging point F1 of the lane topology map, according to the lane extension direction. Each lane in diverging lane group F12 has a different lane extension direction relative to diverging lane group F11.

[0085] S504: Perform diversion lane reasoning.

[0086] In some embodiments, after determining N divergence lane groups, the electronic device can select the target divergence lane group containing the target divergence lane from the N divergence lane groups based on lane landmarks, and the first divergence lane in the target divergence lane group is the target divergence lane.

[0087] For example, as shown in Figure 6, if the lane markings indicate that vehicles can turn left or go straight ahead, then after determining the diversion lane group F11 and diversion lane group F12, the left diversion lane group F11 can be designated as the diversion lane group corresponding to the left-turn diversion lane, and the right diversion lane group F12 can be designated as the diversion lane group corresponding to the straight-ahead diversion lane. Furthermore, the first diversion lane in the diversion lane group F11 corresponding to the left-turn diversion lane is the left-turn diversion lane, and the first diversion lane in the diversion lane group F12 corresponding to the straight-ahead diversion lane is the straight-ahead diversion lane.

[0088] Thus, by using the above method, when the electronic device detects that there are N diverging lanes ahead, if the electronic device can detect the lane markers corresponding to the N diverging lanes, the electronic device can quickly and accurately identify the target diverging lane (such as a straight diverging lane) in advance among the N diverging lanes based on the lane markers, so that the autonomous vehicle can drive in the target diverging lane.

[0089] Furthermore, in some embodiments, corresponding to the above-described vehicle control method, this application also provides a vehicle control system. For example, as shown in FIG7, the vehicle control system 700 may include a first detection module 701, a perception data acquisition module 702, a historical driving trajectory acquisition module 703, an electronic map acquisition module 704, a prediction module 705, a target diversion lane selection module 706, and a control module 707.

[0090] The first detection module 701 is used to detect whether the diversion triggering condition is met. For example, it is used to detect whether there are N diversion lanes ahead of the vehicle's current position, where N is an integer greater than 1. If the diversion triggering condition is met, the perception data acquisition module 702 is used to acquire perception data, the historical driving trajectory acquisition module 703 is used to acquire the vehicle's historical driving trajectory, and the electronic map acquisition module 704 is used to acquire the electronic map corresponding to the vehicle's current position.

[0091] The prediction module 705 is used to predict the vehicle's future driving trajectory within a future time period based on the vehicle's perception data, historical driving trajectory, and electronic map. For example, as shown in Figure 3, the prediction module 705 can predict the vehicle's future driving trajectory 301 within a future time period.

[0092] The target diversion lane selection module 706 is used to select the target diversion lane from the N diversion lanes based on the degree of deviation between the future driving trajectory and each of the N diversion lanes.

[0093] The control module 707 is used to control the vehicle to travel in the target diversion lane.

[0094] Thus, through the vehicle control system described above, when N divergence lanes are detected in front of the vehicle, the target divergence lane (such as a straight divergence lane) can be accurately identified in advance among the N divergence lanes, so that the autonomous vehicle can drive in the target divergence lane.

[0095] In other embodiments, the vehicle control system may also include a second detection module for detecting whether lane markers corresponding to the N diverging lanes exist in the driving environment.

[0096] In other embodiments, when lane landmarks corresponding to N diverging lanes are detected in the driving environment, the target diverging lane selection module 706 can also be used to select the target diverging lane from the N diverging lanes based on the lane landmarks.

[0097] Thus, with the aforementioned vehicle control system, when there are N divergence lanes ahead of the vehicle, if lane markers are present in the driving environment, the vehicle control system can directly determine the target divergence lane based on the lane markers. Conversely, if lane markers are not present in the driving environment, the vehicle control system can also accurately identify the target divergence lane (such as a straight-ahead divergence lane) in advance among the N divergence lanes, enabling the autonomous vehicle to drive in the target divergence lane.

[0098] In some embodiments, this application also provides a readable storage medium. The readable storage medium stores instructions that, when executed on an electronic device, cause the electronic device to perform the vehicle control method mentioned in this application.

[0099] In other embodiments, this application also provides a computer program product, wherein the computer program product includes computer instructions. When the computer instructions are executed on an electronic device, the electronic device can implement the vehicle control method mentioned in this application.

[0100] In other embodiments, this application also provides an electronic device. The electronic device includes at least one memory and at least one processor, the memory being coupled to the processor. The memory stores computer program code / instructions. When the computer program code / instructions are executed by the processor, the electronic device can implement the vehicle control method mentioned in this application.

[0101] Furthermore, in some embodiments, this application also provides a vehicle, which may include the aforementioned electronic devices. For example, taking a computer system 50 as an example, a schematic diagram of the structure of the vehicle 100 provided in this application is illustrated with reference to FIG8.

[0102] As shown in Figure 8, the functional framework of vehicle 100 may include various subsystems, such as the sensor system 10, control system 20, one or more peripheral devices 30 (one is shown as an example), power supply 40, and computer system 50. Optionally, vehicle 100 may also include other functional systems, such as an engine system that provides power to vehicle 100, etc., which are not limited herein.

[0103] The sensor system 10 may include several detection devices that can sense the measured information and convert the sensed information into electrical signals or other required forms of information output according to a certain rule. As shown in the figure, these detection devices may include a global positioning system 11 (GPS), a vehicle speed sensor 12, an inertial measurement unit 13 (IMU), etc., and this application is not limited thereto.

[0104] The Global Positioning System 11 is a system that uses GPS positioning satellites to perform real-time positioning and navigation globally. In this application, the Global Positioning System 11 can be used to achieve real-time positioning of the vehicle 100 and provide the geographical location information of the vehicle 100. The vehicle speed sensor 12 is used to detect the driving speed of the vehicle 100. The inertial measurement unit 13 may include a combination of an accelerometer and a gyroscope, and is a device for measuring the angular rate and acceleration of the vehicle 100. For example, during vehicle movement, the inertial measurement unit 13 can measure the changes in the position and angle of the vehicle body based on the inertial acceleration of the vehicle 100, such as measuring the acceleration and angular rate of the vehicle 100.

[0105] The control system 20 may include a steering unit 21, a braking unit 22, etc.

[0106] Steering unit 21 can represent a system for adjusting the direction of travel of vehicle 100, which may include, but is not limited to, a steering wheel or other structural device for adjusting or controlling the direction of travel of vehicle 100. Braking unit 22 can represent a system for slowing down the speed of vehicle 100, and may also be called a vehicle braking system. It may include, but is not limited to, a brake controller, a reducer, or other structural device for slowing down the vehicle. In practical applications, braking unit 22 can use friction to slow down the vehicle tires, thereby slowing down the speed of vehicle 100.

[0107] Peripheral device 30 may include several components, such as the communication system 31, touch screen 32, user interface 33, etc., as shown in the figure. The communication system 31 is used to enable network communication between vehicle 100 and other devices besides vehicle 100. In practical applications, the communication system 31 can employ wireless communication technology or wired communication technology to achieve network communication between vehicle 100 and other devices. This wired communication technology can refer to communication between vehicle 100 and other devices via network cable or fiber optic cable, etc. This wireless communication technology includes, but is not limited to, Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Wireless Local Area Networks (WLAN) (such as Wireless Fidelity (Wi-Fi) networks), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), and Infrared (IR) technology, etc.

[0108] The touchscreen 32 can be used to detect operation commands on the touchscreen 32. For example, the user can perform touch operations on the content data displayed on the touchscreen 32 according to actual needs to achieve the corresponding function, such as playing music, video, or other multimedia files. The user interface 33 can specifically be a touch panel, used to detect operation commands on the touch panel. The user interface 33 can also be a physical button or a mouse. The user interface 33 can also be a display screen, used to output data and display images or data. Optionally, the user interface 33 can also be at least one device belonging to the category of peripheral devices, such as a touchscreen, microphone, and speaker.

[0109] Several functions of vehicle 100 are controlled and implemented by computer system 50. Computer system 50 may include multiple processing systems, such as processor 51, continuous damping control (CDC) 52, mobile device control (MDC) 53, telematics-BOX (T-BOX) 54, as well as memory 55 (also referred to as a storage device) and gateway 56. In practical applications, memory 55 can be located inside or outside computer system 50, for example, as a cache within vehicle 100; this application does not limit this. Processor 51 may be, for example, a graphics processing unit (GPU). Processor 51, CDC 52, MDC 53, and T-BOX 54 can be used to run relevant programs or corresponding instructions stored in memory 55 to implement the corresponding functions of vehicle 100.

[0110] The memory 55 may include volatile memory; it may also include non-volatile memory, such as read-only memory (ROM), flash memory, or solid-state drive; or it may include a combination of the above types of memory. The memory 55 can be used to store a set of program code or instructions corresponding to the program code, so that the processor 51 can call the program code or instructions stored in the memory 55 to implement the corresponding functions of the vehicle 100. In this application, the memory 55 may store a set of program code for vehicle control, which the processor 51, CDC 52, MDC 53, and T-BOX 54 can call to control the vehicle to perform the vehicle control described in this application.

[0111] Optionally, in addition to storing program code or instructions, memory 55 may also store information such as road maps, driving routes, and sensor data. Computer system 50 can be combined with other components in the vehicle functional framework diagram, such as the sensors in sensor system 10, to realize the relevant functions of vehicle 100. For example, computer system 50 can control the driving direction or speed of vehicle 100 based on data input from sensor system 10; this application does not impose limitations on this.

[0112] It should be noted that Figure 8 is only a schematic diagram of one possible functional framework of vehicle 100. In practical applications, vehicle 100 may include more or fewer systems or components, and this application does not impose any limitations.

[0113] The embodiments disclosed herein can be implemented in hardware, software, firmware, or a combination of these implementations. The embodiments herein can be implemented as computer programs or program code executable on a programmable system including at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0114] Program code can be applied to input instructions to perform the functions described herein and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this document, the processing system includes any system having a processor such as, for example, a digital signal processor, a microcontroller, an application-specific integrated circuit, or a microprocessor.

[0115] The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used when needed. In fact, the mechanisms described in this paper are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0116] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored thereon on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or through other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine-readable (e.g., computer-readable) form, including but not limited to floppy disks, optical disks, optical discs, magneto-optical disks, read-only memory (ROM), random access memory (RAM), magnetic cards or optical cards, erasable programmable read-only memory (EPROM), flash memory, electrically erasable programmable read-only memory (EEPROM), or tangible machine-readable storage for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in the form of electrical, optical, acoustic, or other propagation signals. Therefore, machine-readable media includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a machine-readable (e.g., computer-readable) form.

[0117] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Furthermore, the inclusion of structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0118] It should be noted that the units / modules mentioned in the device embodiments of this paper are logical units / modules. Physically, a logical unit / module can be a physical unit / module, a part of a physical unit / module, or a combination of multiple physical units / modules. The physical implementation of these logical units / modules is not the most important factor; rather, the combination of functions implemented by these logical units / modules is the key to solving the technical problem proposed in this paper. Furthermore, to highlight the innovative aspects of this paper, the device embodiments described above have not introduced units / modules that are not closely related to solving the technical problem proposed in this paper. This does not mean that the device embodiments do not contain other units / modules.

[0119] It should be noted that in the examples and description of this patent, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0120] While this document has been illustrated and described with reference to certain preferred embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made therein without departing from the scope of this document.

Claims

1. A vehicle control method, characterized in that, include: Upon detecting that the diversion triggering conditions are met, the system acquires the vehicle's perception data, its historical driving trajectory, and the electronic map corresponding to its current location. The diversion triggering conditions include the existence of N diversion lanes ahead of the vehicle's current location and the vehicle's navigation function being turned off, where N is an integer greater than 1. Based on the vehicle's perception data, historical driving trajectory, and electronic map, the system predicts the vehicle's future driving trajectory within a future time period. Based on the degree of deviation between the future driving trajectory and each of the N diversion lanes, a target diversion lane is selected from the N diversion lanes. The system then controls the vehicle to drive in the target diversion lane.

2. The vehicle control method according to claim 1, characterized in that, The diversion triggering condition also includes: no lane landmark corresponding to the N diversion lanes is detected.

3. The vehicle control method according to claim 1, characterized in that, The method further includes: detecting that the diversion triggering condition is met, and, if lane landmarks corresponding to the N diversion lanes are detected, selecting the target diversion lane based on the lane landmarks among the N diversion lanes.

4. The vehicle control method according to claim 2, characterized in that, The step of selecting a target diversion lane from the N diversion lanes based on the degree of deviation between the future driving trajectory and each of the N diversion lanes includes: mapping the future driving trajectory and the sensing data to the same coordinate system, wherein the sensing data includes image data of the N diversion lanes; determining lane reference lines corresponding to each of the N diversion lanes based on the image data; determining the degree of deviation between the future driving trajectory and the lane reference lines based on the distance between each sampling point in the future driving trajectory and the lane reference lines; and selecting the diversion lane corresponding to the lane reference line with the smallest degree of deviation from the future driving trajectory as the target diversion lane.

5. The vehicle control method according to claim 4, characterized in that, The step of determining the degree of deviation between the future driving trajectory and the lane reference line based on the distance between each sampling point in the future driving trajectory and the lane reference line includes: obtaining the distance between each sampling point in the future driving trajectory and the j-th lane reference line respectively; and taking the sum of the distances between each sampling point in the future driving trajectory and the j-th lane reference line as the degree of deviation between the future driving trajectory and the j-th lane reference line, where j is a positive integer less than or equal to N.

6. The vehicle control method according to claim 3, characterized in that, The step of selecting the target diverging lane from the N diverging lanes based on the lane landmarks includes: determining a lane topology map based on the image data corresponding to the lanes in the perception data, wherein the lanes include the N diverging lanes and the subsequent lanes of the N diverging lanes, and the lane topology map is used to characterize the connection relationship and lane extension direction between the lanes in the lanes; at the first lane diverging point in the lane topology map, dividing the N diverging lanes and the subsequent lanes of the N diverging lanes into N diverging lane groups according to the lane extension direction; and selecting the target diverging lane group containing the target diverging lane from the N diverging lane groups based on the lane landmarks, wherein the first diverging lane in the target diverging lane group is the target diverging lane.

7. A vehicle control system, characterized in that, include: A first detection module is used to detect whether the diversion triggering conditions are met, wherein the diversion triggering conditions include the existence of N diversion lanes ahead of the vehicle's current position and the vehicle's navigation function being turned off, where N is an integer greater than 1; a perception data acquisition module is used to acquire perception data when the diversion triggering conditions are met; a historical driving trajectory acquisition module is used to acquire the vehicle's historical driving trajectory when the diversion triggering conditions are met; an electronic map acquisition module is used to acquire the electronic map corresponding to the vehicle's current position when the diversion triggering conditions are met; a prediction module is used to predict the vehicle's future driving trajectory within a future time period based on the vehicle's perception data, the historical driving trajectory, and the electronic map; a target diversion lane selection module is used to select a target diversion lane from the N diversion lanes based on the degree of deviation between the future driving trajectory and each of the N diversion lanes; and a control module is used to control the vehicle to drive in the target diversion lane.

8. An electronic device, characterized in that, include: At least one memory and at least one processor, the memory being coupled to the processor; the memory being used to store computer program code / instructions; when the computer program code / instructions are executed by the processor, causing the electronic device to perform the vehicle control method as described in any one of claims 1 to 6.

9. A vehicle, characterized in that, Includes the electronic device as described in claim 8.

10. A readable storage medium, characterized in that, The readable storage medium stores instructions that, when executed on an electronic device, cause the electronic device to perform the vehicle control method as described in any one of claims 1 to 6.

11. A computer program product, characterized in that, include: Computer instructions, when executed on an electronic device, cause the electronic device to perform the vehicle control method as described in any one of claims 1 to 6.

Images