Vehicle control method and related device

Abstract

The invention discloses a vehicle control method and a related device, and the method comprises the steps: obtaining map information which comprises information of environment elements of a toll station; acquiring first sensing information from at least one sensing device of the vehicle, wherein the first sensing information comprises detection data of the toll station; and according to the map information and / or the first perception information, obtaining traffic attribute information of at least one lane of the toll station, the traffic attribute information being used for indicating traffic conditions of the lane. And a driver can be assisted to select a proper lane.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, specifically to a vehicle control method and related devices. Background Technology

[0002] With the development and advancement of technology, vehicles are increasingly involved in people's daily lives and work, inevitably leading to situations where vehicles pass through toll booths. Toll booths typically have multiple lanes, and drivers generally rely on their experience to choose the appropriate lane. However, this method is prone to errors and heavily depends on driver experience, especially when there are many lanes. Therefore, there is an urgent need for a method to assist drivers in choosing lanes. Summary of the Invention

[0003] This application provides a vehicle control method and related device that can help drivers select appropriate lanes.

[0004] In a first aspect, embodiments of this application provide a vehicle control method, which can perform steps including but not limited to the following:

[0005] Obtain map information, which includes information about the environmental elements of the toll station;

[0006] Acquire first sensing information from at least one sensing device of the vehicle, the first sensing information including detection data of the toll station;

[0007] Based on map information and / or first perception information, the passage attribute information of at least one lane of the toll station is obtained, and the passage attribute information is used to indicate the passage conditions of the lane.

[0008] In this embodiment of the application, the map information may include one or more of the following: road information, lane information, toll station information, gate information, traffic signs, toll booths, dynamic events, and weather information.

[0009] Map information can be categorized by precision into high-precision map information and ordinary map information. High-precision map information can come from remote servers, while ordinary map information can come from vehicle-mounted navigation maps; ordinary maps are also known as low-precision maps. Remote servers can establish communication connections with vehicles and push accurate map information to them.

[0010] Environmental elements at toll stations can include those with low time-varying characteristics, such as road topology, building locations, lane markings, lane directions, or traffic infrastructure layout. They can also include those with high time-varying characteristics, such as traffic accidents, weather changes, road icing, road construction, or traffic congestion.

[0011] The sensing device can be installed on the vehicle and can establish a communication connection with the vehicle's control system.

[0012] At least one sensing device may include at least one camera and / or at least one ranging device, wherein the ranging device may include at least one of lidar, millimeter-wave radar, and ultrasonic radar. When at least one sensing device may include at least one camera, the first sensing information may include image data from at least one camera; when the sensing device includes a ranging device, the first sensing information may include point cloud data from at least one ranging device; when at least one sensing device includes at least one camera and at least one ranging device, the first sensing information may include image data from at least one camera and point cloud data from at least one ranging device.

[0013] The traffic attribute information of each lane can be used to indicate the traffic conditions of that lane, thereby determining whether the lane meets the needs of the vehicle based on the traffic conditions of that lane.

[0014] In this embodiment of the application, the traffic attribute information of at least one lane of the first toll station can be obtained. The traffic attribute information is used to indicate the traffic conditions of the lane, thereby assisting the driver in selecting the appropriate lane.

[0015] In one possible implementation, the traffic attribute information includes one or more of the following: lane toll type, lane passability status, lane vehicle type, lane height limit, lane width limit, lane speed limit, and lane vehicle queuing information.

[0016] The toll lane type can include any of the following: ETC lane, manual lane, or a hybrid ETC and manual lane. The lane's passability status can include: impassable lane or passable lane. The type of vehicle allowed in the lane can include any of the following: passenger vehicles or freight vehicles.

[0017] Trucks can be classified into six categories according to the number of axles, vehicle length, and total mass: Class I trucks, Class II trucks, Class III trucks, Class IV trucks, Class V trucks, and Class VI trucks.

[0018] The height limit, width limit, and speed limit of a lane can be obtained from the initial sensing information of the sensing device. For example, when the sensing device is a camera, the lane's toll type, height limit, width limit, speed limit, and other traffic attributes can be identified based on the image data collected by the camera. When the sensing device is a camera and a ranging device, the lane's toll type, height limit, width limit, speed limit, and other traffic attributes, as well as its three-dimensional (3D) location, can be accurately identified based on the image data collected by the camera and the point cloud data collected by the ranging device.

[0019] Lane vehicle queuing information may include at least one of the following: lane vehicle queue length and number of vehicles in the queue.

[0020] The traffic attribute information in this application embodiment can include various types of information. In human-driven mode, it can provide the driver with rich, multi-dimensional information to select a suitable lane, thereby accurately assisting the driver in choosing the appropriate lane. In intelligent driving mode, it can provide the intelligent driving system with even richer, multi-dimensional information to help the intelligent driving system make more accurate decisions. The intelligent driving system can include: an advanced driving assistance system (ADAS) and an autonomous driving system (ADS).

[0021] In one possible implementation, the toll type of the lane includes any of the following: electronic toll collection (ETC) lane, manual lane, ETC and manual hybrid lane;

[0022] The passability status of a lane includes both impassable and passable lanes.

[0023] The proposed implementation can provide lanes with accurate toll type and passability status. In human-driven mode, the driver can determine whether the vehicle can pass through the lane based on the toll type and passability status, thus accurately assisting the driver in selecting the appropriate lane to pass through the toll station. In intelligent driving mode, the intelligent driving system can select the appropriate lane to pass through the toll station based on the toll type and passability status.

[0024] In one possible implementation, at least one sensing device includes at least one camera and / or at least one ranging device.

[0025] A camera's detection range can be at least one of the following: in front of or to the side of the vehicle. Cameras can be categorized by the number of lenses: monocular cameras, binocular cameras, tri-lens cameras, etc. They can also be categorized by their installation location: forward-view cameras and surround-view cameras.

[0026] The detection range of the ranging device can be at least one of the following: in front of, behind, and to the side of the vehicle. The ranging device can include at least one of the following: lidar, millimeter-wave radar, and ultrasonic radar.

[0027] The embodiments of this application can obtain first perception information through a camera and a ranging device, which can obtain more accurate perception information compared with using a pure camera.

[0028] In one possible implementation, the image data provided by at least one camera includes information including one or more of the following: the toll type of at least one lane of the toll station, the type of vehicles passing through the lane, the height limit of the lane, the width limit of the lane, the speed limit of the lane, vehicle queuing information of the lane, the size information of the vehicle in front of the vehicle, and the vehicle type of the vehicle in front of the vehicle.

[0029] In this embodiment, the image data provided by the camera can come from images captured by the camera. Target recognition can be performed on the images captured by the camera to identify various information contained within the images.

[0030] The vehicle in front of the vehicle can be a vehicle in the same lane as the vehicle itself. The size information of the vehicle in front can include at least one of its width and height. The vehicle type of the vehicle in front can be determined based on an image of the vehicle in front captured by a camera.

[0031] In this embodiment, the image data provided by the camera may include information directly related to the lane's traffic attributes (e.g., the toll type of at least one lane of the toll station, the type of vehicles passing through the lane, the height limit of the lane, the width limit of the lane, the speed limit of the lane, and the vehicle queuing information of the lane), or information indirectly related to the lane's traffic attributes (e.g., the size information of the vehicle in front of the vehicle and the vehicle type of the vehicle in front), so that more accurate lane traffic attributes can be obtained based on the image data provided by the camera.

[0032] In one possible implementation, the size information of the vehicle ahead and / or the vehicle type of the vehicle ahead are used to indicate the likelihood of the vehicle's passage.

[0033] The size information of the vehicle ahead can include at least one of its width and height. The vehicle can indirectly determine whether it can pass through the lane based on the width and height of the vehicle ahead in the same lane. This allows it to obtain information such as the lane's height limit, width limit, and vehicle type, even in scenarios where the vehicle is following closely. The vehicle can determine whether its current lane is suitable for passage based on the width and / or height of the vehicle ahead.

[0034] When a vehicle's camera blocks a traffic sign, it cannot capture the sign, preventing the vehicle from directly obtaining information such as lane vehicle types, height restrictions, width restrictions, and speed limits. In this situation, the vehicle can indirectly determine its lane passage ability based on the dimensions and / or type of the vehicle in front. This method provides an indirect way to determine lane passage ability when the traffic sign is obscured, improving the accuracy of lane clearance assessment in special scenarios (e.g., when a vehicle blocks a traffic sign).

[0035] In one possible implementation, the first sensing information further includes traffic flow information, which comes from point cloud information collected by at least one ranging device and / or image data collected by at least one camera. The traffic flow information includes traffic flow information between vehicles and toll stations.

[0036] Traffic flow information can include the number of vehicles and their speed on the road between the vehicle and the toll station. It reflects the queuing information for each lane and the average speed of vehicles in each lane between the vehicle and the toll station. Based on this information, drivers can assess the queuing situation at the toll station, estimate the time required for their vehicle to reach the toll station in each lane, and flexibly determine the optimal lane to pass through, thus assisting drivers in lane selection.

[0037] In one possible implementation, the map information includes at least one of information from the vehicle-mounted navigation map and map information from a remote server.

[0038] In this embodiment, the navigation map information on the vehicle side can be ordinary map information, while the map information from the remote server can be high-definition map information. The map information can be ordinary map information, high-definition map information, or a combination of ordinary map information and high-definition map information, thereby meeting the map information needs of more scenarios.

[0039] In one possible implementation, obtaining the traffic attribute information of at least one lane of the toll station based on map information and / or first perception information can be achieved in ways including but not limited to the following:

[0040] Input map information and / or first perception information into at least one artificial intelligence (AI) model to obtain traffic attribute information for at least one lane.

[0041] In this embodiment, the AI ​​model outputs lane traffic attribute information. The AI ​​model can learn the correspondence between each lane and traffic attribute information at the toll station. Using the AI ​​model eliminates the need for preprocessing map information and / or initial perception information, allowing for the rapid acquisition of traffic attribute information for at least one lane, thus improving efficiency.

[0042] In one possible implementation, the AI ​​model includes a trained deep learning model.

[0043] After training, the AI ​​model can learn the correspondence between the various lanes and traffic attribute information at a toll station. Deep learning models can include attention-based deep learning models.

[0044] In one possible implementation, at least one sensing device includes at least one camera and at least one ranging device;

[0045] Based on map information and / or first-sensor information, obtain the traffic attribute information of at least one lane of the toll station, including:

[0046] When the distance between the toll station and the vehicle is less than a first threshold and greater than a second threshold, without introducing information from a ranging device, the traffic attribute information of at least one lane of the toll station is obtained based on map information and image data from at least one camera; or

[0047] When the distance between the toll station and the vehicle is less than a second threshold, based on map information, image data from at least one camera, and point cloud data from at least one ranging device, the traffic attribute information of at least one lane of the toll station is obtained, or

[0048] When the distance between the toll station and the vehicle is less than the second threshold, there is no need to introduce map information. Based on image data from at least one camera and point cloud data from at least one ranging device, the traffic attribute information of at least one lane of the toll station can be obtained.

[0049] This application embodiment can achieve coarse mapping at a distance and precise mapping up close. When the vehicle is far from the toll station (less than a first threshold and greater than a second threshold), the traffic attribute information of at least one lane of the toll station can be obtained based on map information and image data from at least one camera (without information from a ranging device). Even without the participation of a ranging device, the traffic attribute information of at least one lane of the toll station can be obtained. When the vehicle is close to the toll station (less than the second threshold), the traffic attribute information of at least one lane of the toll station can be obtained based on map information, image data from at least one camera, and point cloud data from at least one ranging device. With the participation of a ranging device, more accurate traffic attribute information of at least one lane of the toll station can be obtained. Furthermore, when the vehicle is close to the toll station (less than the second threshold), the traffic attribute information of at least one lane of the toll station can also be obtained based on image data from at least one camera and point cloud data from at least one ranging device. Even without map information, the traffic attribute information of at least one lane of the toll station can be obtained. This can be applied to scenarios where map information cannot be obtained (e.g., map software malfunction, or map information cannot be obtained due to network issues).

[0050] Even when the vehicle is far from the toll station, and without the participation of point cloud data, it can still obtain the traffic attribute information of at least one lane of the toll station. This can also assist the driver in choosing the appropriate lane when the vehicle is far from the toll station, thus improving the user experience.

[0051] When using an AI model, the data input to the AI ​​model can be switched between when the vehicle is far away from or close to the toll station, allowing the AI ​​model to flexibly switch inputs from far to near.

[0052] Optionally, the AI ​​model can be activated when the distance between the toll station and the vehicle is less than a first threshold. After the vehicle passes through the toll station, the inference of the AI ​​model is turned off, thereby avoiding the waste of computing resources.

[0053] In one possible implementation, the first threshold is related to one or more of the camera's detection range, the camera's image quality, weather conditions, and the vehicle's speed.

[0054] The first threshold can be a pre-set fixed value or a dynamically changing value. When the first threshold is a pre-set fixed value, it can be related to the camera's detection range. When the first threshold is a dynamically changing value, it can be related to one or more of the camera's detection range, the camera's image quality, weather conditions, and the vehicle's speed. Weather conditions can include visibility. The first threshold can dynamically change based on the camera's detection range, the camera's image quality, weather conditions, and the vehicle's speed, thus making it suitable for different scenarios.

[0055] In one possible implementation, the second threshold is related to one or more of the ranging device's detection range, detection accuracy, weather conditions, and vehicle speed.

[0056] The second threshold can be a pre-set fixed value or a dynamically changing value. When the second threshold is a pre-set fixed value, it can be related to the detection range of the ranging device. When the second threshold is a dynamically changing value, it can be related to one or more of the following: the detection range of the ranging device, the image quality of the ranging device, weather conditions, and vehicle speed. Weather conditions can include visibility. The second threshold can dynamically change based on the camera's detection range, the camera's image quality, weather conditions, and vehicle speed, thus making it suitable for different scenarios.

[0057] In one possible implementation, the above-mentioned method of obtaining traffic attribute information for at least one lane of the toll station based on map information and image data from at least one camera can specifically include, but is not limited to, the following methods:

[0058] Input map information and image data from at least one camera into the AI ​​model to obtain traffic attribute information for at least one lane of the toll station; or

[0059] The distance between the toll station and the vehicle is determined based on the map information. The camera depth of at least one camera is determined based on the distance between the toll station and the vehicle. The traffic attribute information of at least one lane of the toll station is determined based on the camera depth of at least one camera and the image data of at least one camera.

[0060] In this embodiment, when sketching from a distance, the traffic attribute information of at least one lane of the toll station can be obtained based on map information and image data from at least one camera. Either an AI model can be used to quickly obtain the traffic attribute information of at least one lane, improving efficiency, or a white-box algorithm can be used, which is easier to optimize.

[0061] In one possible implementation, the passage attribute information of at least one lane of the toll station is obtained based on map information, image data from at least one camera, and point cloud data from at least one ranging device. This can be achieved, but is not limited to, the following methods:

[0062] By inputting map information, image data from at least one camera, and point cloud data from at least one ranging device into the AI ​​model, traffic attribute information for at least one lane of the toll station can be obtained; or

[0063] The image data from at least one camera and the point cloud data from at least one ranging device are used to perform 3D reconstruction to obtain a first 3D reconstruction result; the first 3D reconstruction result is fused with map information to obtain a fusion result; at least one image detection result is determined based on the image data from at least one camera; the fusion result is matched with at least one image detection result to obtain at least one first matching result; and multi-camera fusion processing is performed based on at least one first matching result to obtain traffic attribute information for at least one lane.

[0064] In this embodiment of the application, when the accuracy is accurate at close range, the geometric and semantic fusion of multi-source data (camera, ranging device, map) is combined with the redundant detection and complementary perspective of at least one camera to finally output a more robust and accurate environmental perception result, thereby obtaining accurate traffic attribute information for at least one lane.

[0065] In one possible implementation, the passage attribute information of at least one lane of the toll station is obtained based on image data from at least one camera and point cloud data from at least one ranging device. This can be achieved, but is not limited to, the following methods:

[0066] Image data from at least one camera and point cloud data from at least one ranging device are input into an AI model to obtain traffic attribute information for at least one lane of the toll station; or

[0067] A second 3D reconstruction result is obtained by performing 3D reconstruction on image data from at least one camera and point cloud data from at least one ranging device; at least one image detection result is determined based on the image data from at least one camera; the second 3D reconstruction result is matched with at least one image detection result to obtain at least one second matching result; multi-camera fusion processing is performed based on at least one second matching result to obtain traffic attribute information for at least one lane.

[0068] In this embodiment of the application, when the accuracy is accurate at close range, the geometric and semantic fusion of multi-source data (camera, ranging device) is combined with the redundant detection and complementary perspective of at least one camera to finally output a more robust and accurate environmental perception result, thereby obtaining accurate traffic attribute information for at least one lane.

[0069] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0070] Output the first prompt message, which is used to indicate the traffic attribute information of at least one lane.

[0071] In this embodiment, the first prompt information can be output through at least one of the following methods: interface display, projection, and voice output. When the first prompt information is output through the interface display, each lane can display traffic attribute information such as the toll type, passability status, height limit, width limit, and vehicle queuing information, thereby assisting the driver in selecting the appropriate lane.

[0072] In one possible implementation, the display of access attribute information has multiple display modes, including a three-dimensional element mode.

[0073] The above-mentioned first prompt message can be displayed in ways including but not limited to the following:

[0074] The first interface displays a 3D element mode, which provides traffic attribute information for at least one lane in a 3D manner.

[0075] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0076] In response to the user's mode switching operation input on the first interface, switch the display mode.

[0077] In this embodiment, the first interface can be a human-machine interface (HMI), which can accurately display the lane's traffic attribute information in three dimensions (3D) for the driver's reference and flexibly switch display modes. The lane's traffic attribute information can be displayed in 3D, thereby improving the stereoscopic display effect, enhancing the sense of space, and improving the human-machine interaction experience.

[0078] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0079] At least one recommended lane is determined based on the traffic attribute information of at least one lane and the traffic constraints of the vehicle. The recommended lane is the lane that satisfies the traffic constraints of the vehicle.

[0080] The display device outputs a second prompt message, which is used to indicate the location of at least one recommended lane and / or the recommended value for each recommended lane.

[0081] The second prompt information can be output through at least one of the following methods: interface display, projection, and voice output. The second prompt information can indicate the location of at least one recommended lane and / or the recommended value for each recommended lane, allowing the driver to intuitively see and / or hear the recommended value of the recommended lane, thereby further assisting the driver in selecting the appropriate lane.

[0082] Before a vehicle passes through a toll station, it can accurately identify whether each lane meets the vehicle's passage constraints (ETC or manual, vehicle width, vehicle height, etc.).

[0083] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0084] If the lane the vehicle is currently in does not meet the vehicle's traffic constraints, output a third prompt message to indicate that the current lane does not meet the vehicle's traffic constraints; or,

[0085] If the lane in which the vehicle is currently located meets the traffic constraints, output a fourth prompt message. The fourth prompt message is used to indicate that the current lane meets the traffic constraints.

[0086] The system can output third or fourth prompts through at least one of the following methods: interface display, projection, or voice output. It can indicate whether the current lane meets the vehicle's passage constraints, thus promptly reminding the driver and allowing them to visually and / or audibly see whether the current lane meets the vehicle's passage constraints, thereby further assisting the driver in selecting the appropriate lane.

[0087] In this embodiment of the application, before or when a vehicle passes through a toll station, it can be determined whether the lane in which the vehicle is currently located meets the vehicle's passage constraints. It can not only determine whether it is ETC or manual, but also supports width and height limit determination, thereby accurately determining whether the lane in which the vehicle is currently located meets the vehicle's passage constraints.

[0088] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0089] Based on the traffic attribute information of at least one lane of the toll station, plan the first path for vehicles to pass through the toll station, and the first path passes through the first lane of at least one lane;

[0090] Navigate the vehicle based on the first route.

[0091] In this embodiment of the application, the first path passes through the first lane of at least one lane of the toll station. The path planning and navigation can be performed based on the traffic attribute information of at least one lane of the toll station, which can provide the driver with an accurate navigation path through the toll station.

[0092] In one possible implementation, the above-mentioned planning of a first path for a vehicle to pass through the toll station based on traffic attribute information of at least one lane of the toll station may specifically include, but is not limited to, the following methods:

[0093] The first lane is determined based on the traffic attribute information of at least one lane at the toll station;

[0094] The first path for vehicles to pass through the toll station is planned based on the first lane.

[0095] The first lane can be determined based on the traffic attribute information of at least one lane of the toll station. The first path for vehicles to pass through the toll station can be planned based on the first lane, so that vehicles can pass through the toll station from the first lane, thus planning a reasonable path for vehicles.

[0096] In one possible implementation, the first route for the planned vehicle to pass through the toll station may specifically include, but is not limited to, the following:

[0097] The first lane is determined based on the traffic attribute information of at least one lane and the traffic constraints of the vehicle, and the first lane satisfies the traffic constraints of the vehicle.

[0098] In this embodiment of the application, the vehicle's traffic constraint can be the constraint condition for the vehicle to travel in the lane. It can be determined that the first lane meets the vehicle's traffic constraint based on the vehicle's traffic constraint and the traffic attribute information of the first lane, thereby accurately determining that the first lane meets the vehicle's traffic constraint.

[0099] In one possible implementation, the first route for the planned vehicle to pass through the toll station may specifically include, but is not limited to, the following:

[0100] At least one recommended lane is determined based on the traffic attribute information of at least one lane and the traffic constraints of the vehicle, and each recommended lane satisfies the traffic constraints of the vehicle.

[0101] In response to a selection instruction from the user, a first lane is determined from at least one recommended lane.

[0102] This application embodiment allows users to select a first lane through human-computer interaction. The first lane is determined by the user, satisfying the user's need for autonomous selection and improving the interactive experience of lane selection. The first lane can be any one of at least one recommended lane, or it can be the lane with the highest recommendation value.

[0103] In one possible implementation, the first route for the planned vehicle to pass through the toll station may specifically include, but is not limited to, the following:

[0104] At least one recommended lane and a recommended value for each recommended lane are determined based on the traffic attribute information of at least one lane and the traffic constraints of the vehicle. Each recommended lane satisfies the traffic constraints of the vehicle.

[0105] The lane with the highest recommendation value from at least one recommended lane is selected as the first lane.

[0106] In this embodiment, the vehicle control device can automatically select the lane with the highest recommended value as the first lane without user intervention. This reduces the number of times the user needs to take over in intelligent driving mode, thereby improving the user experience. It can detect the traffic attribute information of at least one lane and provide the optimal lane to pass (the first lane). For example, the optimal lane can be flexibly determined based on traffic flow information.

[0107] In one possible implementation, vehicle traffic constraints include at least one of the following: vehicle type, height, width, and pre-set toll selection strategy.

[0108] In one possible implementation, the pre-set toll selection strategy includes: ETC priority, manual priority, or automatic mode.

[0109] Both ETC priority and manual priority modes can be preset by the user. If the user does not preset them, the system defaults to automatic mode. Automatic mode can be a condition-based toll collection method. For example, if the vehicle does not have ETC or the ETC is unavailable, the system will automatically select the manual lane; or if the vehicle's ETC is available, the system will select the ETC lane.

[0110] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0111] When the distance between the toll station and the vehicle is less than the third threshold, the gate status of the lane where the vehicle is located is detected based on the first perception information.

[0112] Output the gate status of the lane where the vehicle is located.

[0113] In this embodiment, the gate status may include a gate arm raised or a gate arm lowered state. The gate status of the lane where the vehicle is located can be output through at least one of the following methods: interface display, projection, and voice output, supporting lane-level gate status detection, thereby assisting the driver or intelligent driving system in accurately controlling the vehicle's start / stop / continue driving based on the gate status.

[0114] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0115] When the distance between the toll station and the vehicle is less than the fourth threshold, second sensing information from at least one sensing device is acquired;

[0116] Based on obstacle information detected in the vehicle's lane using second-sensor information;

[0117] The vehicle's lane accessibility information is updated based on obstacle information.

[0118] In this embodiment, when the distance between the toll station and the vehicle is relatively close, the passage attribute information of the vehicle's lane can be updated after detecting obstacle information in the lane where the vehicle is located, providing more accurate lane passage attribute information, thereby assisting the driver or intelligent driving system in selecting a more suitable lane to pass through the toll station.

[0119] Secondly, embodiments of this application provide a vehicle control device, which includes a unit for performing the method as described in any of the first aspects.

[0120] In one possible design, the device includes:

[0121] The communication unit is used to acquire map information, which includes information about the environmental elements of the toll station.

[0122] The communication unit is also used to acquire first sensing information from at least one sensing device of the vehicle, the first sensing information including detection data of the toll station;

[0123] The processing unit is used to obtain the traffic attribute information of at least one lane of the toll station based on map information and / or first perception information. The traffic attribute information is used to indicate the traffic conditions of the lane.

[0124] Regarding the processing unit and communication unit of the second aspect and any possible implementation, the steps performed thereon can be referred to the corresponding implementation of the first aspect.

[0125] For the technical effects of the second aspect and any possible implementation, please refer to the description of the technical effects corresponding to the first aspect and the corresponding implementation.

[0126] Optionally, in the vehicle control device of the second aspect and any of the possible embodiments described above:

[0127] In one implementation, the vehicle control device is a vehicle control equipment. When the vehicle control device is a vehicle control equipment, the communication unit can be a transceiver or an input / output interface; the processing unit can be at least one processor (e.g., at least one of a processor for intelligent driving or a vehicle-mounted processor). Optionally, the transceiver can be a transceiver circuit. Optionally, the input / output interface can be an input / output circuit.

[0128] In another implementation, the vehicle control device is a chip (system) or circuit used in vehicle control equipment. When the vehicle control device is a chip (system) or circuit used in vehicle control equipment, the communication unit can be a communication interface (input / output interface), interface circuit, output circuit, input circuit, pin, or related circuit on the chip (system) or circuit; the processing unit can be at least one processor, processing circuit, or logic circuit.

[0129] Thirdly, embodiments of this application provide a vehicle control device including a processor. The processor is coupled to a memory and can be used to execute instructions in the memory to implement the methods described in the first aspect and any of the possible implementations. Optionally, the vehicle control device further includes a memory. Optionally, the vehicle control device further includes a communication interface, and the processor is coupled to the communication interface.

[0130] Fourthly, embodiments of this application provide a chip, including: logic circuitry and an interface. The interface is used to receive or send information; the logic circuitry is used to receive or send information through the interface, causing the chip to execute the methods described in the first aspect and any of the possible implementations.

[0131] Fifthly, embodiments of this application provide a computer-readable storage medium for storing a computer program (also referred to as code or instructions); when the computer program is run on a computer, the methods described in the first aspect and any possible implementation are implemented.

[0132] In a sixth aspect, embodiments of this application provide a computer program product, which includes: a computer program (also referred to as code or instructions); and a method that, when the computer program is run, causes the computer to perform the first aspect and any possible implementation described above.

[0133] In a seventh aspect, embodiments of this application provide a vehicle that includes at least one vehicle control device as described in the second aspect, or the third aspect, or the fourth aspect.

[0134] Optionally, the vehicle may include commercial vehicles, passenger vehicles, industrial vehicles (such as forklifts, trailers, tractors, etc.), engineering vehicles (such as excavators, bulldozers, cranes, etc.), etc., and this application embodiment does not limit this.

[0135] Optionally, the vehicle is used to implement the method described in the first aspect and any possible implementation.

[0136] Furthermore, in the process of implementing any aspect of the first aspect and any possible implementation of the method described above, the processes related to sending and / or receiving information can be understood as the process of the processor outputting information and / or the processor receiving input information. When outputting information, the processor can output the information to a transceiver (or communication interface, or transmitting module) for transmission. After the information is output by the processor, it may require further processing before reaching the transceiver. Similarly, when the processor receives input information, the transceiver (or communication interface, or transmitting module) receives the information and inputs it to the processor. Furthermore, after the transceiver receives the information, it may require further processing before being input to the processor.

[0137] Based on the above principles, for example, the information sent mentioned in the aforementioned method can be understood as information output by the processor. Similarly, the information received can be understood as information received by the processor from input.

[0138] Optionally, unless otherwise specified, or unless they contradict their actual function or internal logic in the relevant description, the operations of the processor, such as transmitting, sending, and receiving, can be more generally understood as processor output and receiving, input, and other operations.

[0139] Optionally, in performing the methods of the first aspect and any possible implementation described above, the processor may be a processor specifically designed to perform these methods, or it may be a processor that performs these methods by executing computer instructions stored in memory, such as a general-purpose processor. The memory may be a non-transitory memory, such as read-only memory (ROM), which may be integrated with the processor on the same chip or disposed on separate chips. This application does not limit the type of memory or the arrangement of the memory and processor.

[0140] In one possible implementation, at least one of the aforementioned memories is located outside the device.

[0141] In yet another possible implementation, at least one of the aforementioned memories is located within the device.

[0142] In another possible implementation, a portion of the memory of the at least one memory is located inside the device, while another portion is located outside the device.

[0143] In this application, the processor and memory may also be integrated into a single device, that is, the processor and memory can be integrated together. Attached Figure Description

[0144] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0145] Figure 1 A schematic diagram illustrating a scenario of a vehicle passing through a toll station, provided as an embodiment of this application;

[0146] Figure 2 A schematic diagram of a toll station provided for an embodiment of this application;

[0147] Figure 3 A schematic diagram of a sensing device on a vehicle provided in an embodiment of this application;

[0148] Figure 4 A schematic flowchart of a vehicle control method provided in an embodiment of this application;

[0149] Figure 5 This is a schematic diagram illustrating a process for obtaining traffic attribute information of at least one lane using an AI model, as provided in an embodiment of this application.

[0150] Figure 6 This is a schematic diagram illustrating a process for obtaining traffic attribute information of at least one lane through three-dimensional reconstruction and data fusion, as provided in an embodiment of this application.

[0151] Figure 7 A schematic diagram illustrating another process for obtaining traffic attribute information of at least one lane through three-dimensional reconstruction and data fusion, as provided in an embodiment of this application;

[0152] Figure 8 This application provides a schematic diagram illustrating the display of a first prompt message via an interface.

[0153] Figure 9 This is an illustration of another way to display the first prompt information via an interface, as provided in an embodiment of this application.

[0154] Figure 10 This is a schematic diagram illustrating a display mode switching method via an interface, as provided in an embodiment of this application.

[0155] Figure 11 This is a schematic diagram illustrating another method of switching display modes via an interface, as provided in an embodiment of this application.

[0156] Figure 12 This application provides a schematic diagram illustrating the display of a second prompt message via an interface.

[0157] Figure 13 This application provides a schematic diagram illustrating the display of third prompt information via an interface.

[0158] Figure 14 This application provides a schematic diagram illustrating the display of a fourth prompt message via an interface.

[0159] Figure 15 A schematic diagram illustrating the selection of a first lane via user interface interaction, as provided in an embodiment of this application;

[0160] Figure 16 A schematic diagram illustrating the planning of a first path provided in an embodiment of this application;

[0161] Figure 17 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application;

[0162] Figure 18 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0163] Figure 19 A schematic diagram of a chip structure provided in an embodiment of this application:

[0164] Figure 20 This is a schematic diagram of the structure of an intelligent driving device provided in an embodiment of this application;

[0165] Figure 21 This is a schematic diagram of the architecture of an intelligent driving system provided in an embodiment of this application. Detailed Implementation

[0166] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described below with reference to the accompanying drawings.

[0167] The terms "first" and "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0168] The term "embodiment" as used herein means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that, unless otherwise specified or logically conflicting, the terminology and / or descriptions between the various embodiments of this application are consistent and can be mutually referenced, and technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0169] It should be understood that in this application, "at least one (item)" means one or more, "more than one" means two or more, "at least two (items)" means two or three or more, and "and / or" is used to describe the relationship between related objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0170] It should be noted that, in this application, "instruction" can include direct instruction, indirect instruction, explicit instruction, and implicit instruction. When describing a certain instruction information for the purpose of instructing A, it can be understood that the instruction information carries A, directly instructs A, or indirectly instructs A.

[0171] In this application, the information indicated by the instruction information is called the information to be instructed. In specific implementations, there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is a correlation between the other information and the information to be instructed. It can also indicate only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various information, thereby reducing instruction overhead to some extent. The information to be instructed can be sent as a whole or divided into multiple sub-information units, and the sending period and / or timing of these sub-information units can be the same or different. This application does not limit the specific sending method. The sending period and / or timing of these sub-information units can be predefined, for example, according to a protocol, or configured by the transmitting device by sending configuration information to the receiving device.

[0172] It should be noted that in this application, "send" can be understood as "output" and "receive" can be understood as "input". "Send information to A", where "to A" simply indicates the direction of information transmission, and A is the destination, does not limit "send information to A" to a direct transmission over the air interface. "Send information to A" includes sending information directly to A, as well as sending information indirectly to A through a transmitter. Therefore, "send information to A" can also be understood as "outputting information destined for A". Similarly, "receive information from A" indicates that the source of the information is A, including receiving information directly from A, as well as receiving information indirectly from A through a receiver. Therefore, "receive information from A" can also be understood as "inputting information from A".

[0173] When a vehicle passes through a tollbooth, which typically has multiple lanes, the driver usually chooses the appropriate lane based on experience. This method is prone to errors and heavily relies on driver experience, especially when there are many lanes. This application provides a vehicle control method and related apparatus, applicable to the field of vehicle technology, which can help drivers select the appropriate lane.

[0174] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating a scenario of a vehicle passing through a toll station, as provided in an embodiment of this application. Figure 1As shown, when a vehicle passes through a toll station, the toll station may include at least one lane, and traffic signs may be installed on the toll station, with lanes corresponding to traffic signs. Traffic signs may include toll type signs, passability status signs, height restriction signs, width restriction signs, speed limit signs, etc. Figure 1 The signs indicate toll type: "Manual," "ETC," and "ETC / Manual." "↓" and "×" indicate passability. "3.5m" indicates a height restriction, "3m" an width restriction, and "20" a speed limit. Figure 1 It also displays a "No Entry" sign. For example, such as... Figure 1 As shown, the traffic attributes of lane 1 include: toll type "manual" and passability status "prohibited"; the traffic attributes of lane 2 include: toll type "ETC", passability status "allowed", and speed limit of 20 km / h; the traffic attributes of lane 3 include: toll type "ETC", passability status "allowed", and height limit of 3.5 meters; the traffic attributes of lane 4 include: toll type "ETC / manual", passability status "allowed", and width limit of 3 meters; the traffic attributes of lane 5 include: toll type "ETC / manual" and passability status "allowed". If the toll type of a lane is "ETC / manual", it means that the lane is a mixed ETC and manual lane, supporting both manual and ETC toll collection.

[0175] Figure 1 One possible example is... Figure 1 The types, quantities, and locations of various traffic signs at toll stations can be adjusted according to needs, and this application does not limit them.

[0176] Please see Figure 2 , Figure 2 This is a schematic diagram of a toll station provided as an embodiment of this application. Figure 2 As shown, a toll station may include at least one lane, and traffic signs may be installed on the toll station. These traffic signs may include identification signs and directional signs. Identification signs may include toll type signs, passage status signs, height restriction signs, width restriction signs, speed limit signs, etc. Figure 2 The signs indicate toll type: "Manual," "ETC Passenger Vehicle," and "ETC / Manual." "↓" and "×" indicate passability. "3.5m" indicates a height restriction, "3m" an width restriction, and "20" a speed limit. "ETC Dedicated ↘" and "ETC / Manual ↘" are directional signs. Figure 2 It also displays a "No Entry" sign. Figure 2 One possible example is... Figure 2The types, quantities, and locations of various traffic signs can be adjusted according to different toll station scenarios, and this application embodiment does not limit them.

[0177] Please see Figure 3 , Figure 3 This is a schematic diagram of a sensing device on a vehicle provided in an embodiment of this application. Figure 3 As shown, at least one sensing device can be installed on a vehicle. The types of sensing devices can include cameras, LiDAR, millimeter-wave radar, ultrasonic sensors, etc. The vehicle can acquire information about its surrounding environment through these sensing devices and use that information to achieve assisted driving or autonomous driving. Figure 3 The sector-shaped area in the diagram represents the detection range of the corresponding sensing device. Figure 3 The types, quantities, and locations of various sensing devices can be adjusted as needed.

[0178] Cameras can collect image data of the area around the vehicle. LiDAR and millimeter-wave radar can collect point cloud data of the area around the vehicle, while ultrasonic sensors can collect distance and orientation data of targets around the vehicle.

[0179] Please see Figure 4 , Figure 4 This is a schematic flowchart illustrating a vehicle control method provided in an embodiment of this application. Figure 4 As shown, this vehicle control method is applied in the field of vehicle technology. The vehicle control method includes, but is not limited to, the following steps:

[0180] 401. The vehicle control unit acquires map information, which includes information about the environmental elements of the toll station.

[0181] 402, The vehicle control unit acquires first sensing information from at least one sensing device of the vehicle, the first sensing information including detection data of the toll station.

[0182] 403. The vehicle control device obtains the passage attribute information of at least one lane of the toll station based on map information and / or first perception information. The passage attribute information is used to indicate the passage conditions of the lane.

[0183] It is understood that the vehicle control device in this application embodiment may be a device equipped with a processor / chip capable of executing computer-executed instructions, or it may be a processor / chip capable of executing computer-executed instructions. Optionally, the vehicle control device may be an electronic device, or it may be a processor / chip within an electronic device, used to execute the vehicle control method in this application embodiment to help the driver select a suitable lane. For example, the chip may include at least one of: a chip for intelligent driving and a vehicle-mounted chip. The electronic device may be an intelligent driving vehicle or a terminal device, or it may be a device that communicates with an intelligent driving vehicle or a terminal device, such as a computer, mobile phone, tablet computer, etc.

[0184] Optionally, the vehicle control device and vehicle control method in the embodiments of this application can be applied to, but are not limited to, vehicle systems. The vehicle equipped with the vehicle system is an intelligent driving vehicle and can be replaced by a terminal device. The terminal device can be, but is not limited to, vehicles such as commercial vehicles, passenger cars, industrial vehicles (such as forklifts, trailers, tractors, etc.), engineering vehicles (such as excavators, bulldozers, cranes, etc.). The embodiments of this application do not specifically limit this.

[0185] In this embodiment of the application, the map information may include one or more of the following: road information, lane information, toll station information, gate information, traffic signs, toll booths, dynamic events, and weather information.

[0186] The information includes: road information (such as the number of lanes and the location of surrounding buildings); lane information (such as lane markings, color, and whether they are solid or dashed); lane markings (such as speed limit signs and deceleration signs); and lane direction. Toll station information includes the shape and size of the toll station, the number of lanes, and gate information (such as the size and color of the gate arm and gate itself, and their relative positions). Traffic signs include those displayed on signs or electronic screens, and those on the ground or buildings. Traffic signs can take the form of text, icons, guide lines, or lights. Police booths include their number, shape, and size. Dynamic events include congestion events, accidents, and construction events. Weather information includes temperature, humidity, precipitation, wind speed, wind direction, lightning, and fog / haze.

[0187] Map information can be categorized by precision into high-precision map information and ordinary map information. High-precision map information can come from remote servers, while ordinary map information can come from vehicle-mounted navigation maps; ordinary maps are also known as low-precision maps. Remote servers can establish communication connections with vehicles and push accurate map information to them.

[0188] Optionally, if the map information includes high-precision map information, the vehicle control device can obtain the traffic attribute information of at least one lane of the toll station based on the high-precision map information.

[0189] When the map information includes information from a regular map, the vehicle control device can obtain the passage attribute information of at least one lane of the toll station based on the information from the regular map and the first perception information.

[0190] Environmental elements at toll stations can include those with low time-varying characteristics, such as road topology, building locations, lane markings, lane directions, or traffic infrastructure layout. They can also include those with high time-varying characteristics, such as traffic accidents, weather changes, road icing, road construction, or traffic congestion.

[0191] The sensing device can be installed on the vehicle and can establish a communication connection with the vehicle's control system.

[0192] At least one sensing device may include at least one camera and / or at least one ranging device, wherein the ranging device may include at least one of lidar, millimeter-wave radar, and ultrasonic radar. When at least one sensing device may include at least one camera, the first sensing information may include image data from at least one camera; when the sensing device includes a ranging device, the first sensing information may include point cloud data from at least one ranging device; when at least one sensing device includes at least one camera and at least one ranging device, the first sensing information may include image data from at least one camera and point cloud data from at least one ranging device.

[0193] The traffic attribute information for each lane can be used to indicate the traffic conditions of that lane, thereby determining whether the lane meets the needs of the vehicle. For example, if the height limit of the lane is 3.5 meters and the vehicle height is 4 meters, then the vehicle does not meet the vehicle's needs; if the lane is an ETC lane and the vehicle supports ETC, then the vehicle meets the vehicle's needs. Here, the vehicle can be the vehicle mentioned in step 402.

[0194] Map information and / or first-sensory information may include one of the following three cases: map information, first-sensory information, or map information and first-sensory information.

[0195] In this embodiment of the application, the traffic attribute information of at least one lane of the toll station can be obtained. The traffic attribute information is used to indicate the traffic conditions of the lane, thereby assisting the driver in selecting the appropriate lane.

[0196] Steps 401, 402, and 403 can be executed repeatedly. For example, step 401 can be executed periodically, step 402 can be executed periodically, and step 403 can also be executed periodically.

[0197] In one possible implementation, the traffic attribute information includes one or more of the following: lane toll type, lane passability status, lane vehicle type, lane height limit, lane width limit, lane speed limit, and lane vehicle queuing information.

[0198] The toll lane type can include any of the following: ETC lane, manual lane, or a hybrid ETC and manual lane. The lane's passability status can include: impassable lane or passable lane. The vehicle type allowed in the lane can include any of the following: passenger vehicle or freight vehicle. For example, passenger vehicles can be classified according to the number of seats as: Class I passenger vehicles, Class II passenger vehicles, Class III passenger vehicles, and Class IV passenger vehicles. Freight vehicles can be classified according to the number of axles, vehicle length, and gross vehicle weight as: Class I freight vehicles, Class II freight vehicles, Class III freight vehicles, Class IV freight vehicles, Class V freight vehicles, and Class VI freight vehicles.

[0199] The height limit, width limit, and speed limit of a lane can be obtained from the initial sensing information of the sensing device. For example, when the sensing device is a camera, the lane's toll type, height limit, width limit, speed limit, and other traffic attributes can be identified based on the image data collected by the camera. When the sensing device is a camera and a ranging device, the lane's toll type, height limit, width limit, speed limit, 3D location, and other traffic attributes can be accurately identified based on the image data collected by the camera and the point cloud data collected by the ranging device.

[0200] Lane vehicle queuing information may include at least one of the following: lane vehicle queue length and number of vehicles in the queue.

[0201] The traffic attribute information in this application embodiment can include various types of information. In human-driven mode, it can provide the driver with rich, multi-dimensional information to select a suitable lane, thereby accurately assisting the driver in choosing the appropriate lane. In intelligent driving mode, it can provide the intelligent driving system with even richer, multi-dimensional information to help the intelligent driving system make more accurate choices.

[0202] In one possible implementation, the toll type of the lane includes any of the following: Electronic Toll Collection (ETC) lane, manual lane, and a hybrid ETC and manual lane; the passability status of the lane includes impassable lane or passable lane.

[0203] Passable lanes can be such as Figure 1The "↓" shown can represent a green ETC lane or an ETC-accessible lane, a green manual lane or a manual-accessible lane, or a green ETC and manual mixed lane (or an ETC and manual mixed lane). Lanes that are not permitted to pass can include: closed lanes (such as...). Figure 1 The “×” indicates a lane that is impassable due to an obstacle. Obstacles may include one or more of the following: traffic cones, water-filled barriers, fallen rocks, mud, cargo dropped by other vehicles, accident vehicles, signs placed on the lane (e.g., construction signs, accident signs), and other obstacles that impede the passage of vehicles.

[0204] Among them, ETC-accessible lanes, manual-accessible lanes, and ETC-and-manual-accessible lanes all include the toll type and accessibility status of the lanes.

[0205] This application's embodiments can provide lanes with accurate toll type and passability status. In human-driven mode, the driver can determine whether the vehicle can pass through the lane based on the toll type and passability status, thus accurately assisting the driver in selecting the appropriate lane to pass through the toll station. In intelligent driving mode, the intelligent driving system can select the appropriate lane to pass through the toll station based on the lane's toll type and passability status.

[0206] In one possible implementation, at least one sensing device includes at least one camera and / or at least one ranging device.

[0207] A camera's detection range can be at least one of the following: in front of or to the side of the vehicle. Cameras can be categorized by the number of lenses: monocular cameras, binocular cameras, tri-lens cameras, etc. They can also be categorized by their installation location: forward-view cameras and surround-view cameras.

[0208] The detection range of the ranging device can be at least one of the following: in front of, behind, and to the side of the vehicle. The ranging device can include at least one of the following: lidar, millimeter-wave radar, and ultrasonic radar.

[0209] The embodiments of this application can obtain first perception information through a camera and a ranging device, which can obtain more accurate perception information compared with using a pure camera.

[0210] In one possible implementation, the image data provided by at least one camera includes information including one or more of the following: the toll type of at least one lane of the toll station, the type of vehicles passing through the lane, the height limit of the lane, the width limit of the lane, the speed limit of the lane, vehicle queuing information of the lane, the size information of the vehicle in front of the vehicle, and the vehicle type of the vehicle in front of the vehicle.

[0211] In this embodiment, the image data provided by the camera can come from images captured by the camera. Target recognition can be performed on the images captured by the camera to identify various information contained within the images. For example, by recognizing traffic signs in the image, it is possible to identify the toll type, vehicle type, height limit, width limit, speed limit, vehicle queue information, size information of the vehicle in front of the vehicle, and vehicle type of the vehicle in front of the vehicle in each lane of the toll station.

[0212] The vehicle in front of the vehicle can be a vehicle in the same lane as the vehicle itself. The size information of the vehicle in front can include at least one of its width and height. The vehicle type of the vehicle in front can be determined based on image recognition of the vehicle in front captured by a camera; for example, the type of the vehicle in front can include: bus, truck, etc.

[0213] In this embodiment, the image data provided by the camera may include information directly related to the lane's traffic attributes (e.g., the toll type of at least one lane of the toll station, the type of vehicles passing through the lane, the height limit of the lane, the width limit of the lane, the speed limit of the lane, and the vehicle queuing information of the lane), or information indirectly related to the lane's traffic attributes (e.g., the size information of the vehicle in front of the vehicle and the vehicle type of the vehicle in front), so that more accurate lane traffic attributes can be obtained based on the image data provided by the camera.

[0214] In one possible implementation, the size information of the vehicle ahead and / or the vehicle type of the vehicle ahead are used to indicate the likelihood of the vehicle's passage.

[0215] The size information of the vehicle ahead can include at least one of its width and height. The vehicle can indirectly determine whether it can pass through the lane based on the width and height of the vehicle ahead in the same lane. This allows it to obtain information such as the lane's height limit, width limit, and vehicle type, even in close following situations. For example, if the vehicle's width is less than or equal to the width of the vehicle ahead, and the vehicle's height is less than or equal to the height of the vehicle ahead, then the vehicle is considered to have a higher probability of passing through the lane in terms of both height and width limits.

[0216] The vehicle type of the vehicle ahead can also be used to indicate the likelihood of the vehicle's passage. The likelihood of passage can be indicated based on whether the vehicle type of the vehicle ahead is the same as the vehicle's type. For example, if the vehicle type of the vehicle ahead is the same as the vehicle's type, it can be considered that the vehicle has a higher probability of passing through the lane in terms of vehicle type; if the vehicle type of the vehicle ahead is different from the vehicle's type, it can be considered that the vehicle has a lower probability of passing through the lane in terms of vehicle type.

[0217] If the size information and vehicle type of the vehicle in front of the vehicle are obtained, the vehicle can be determined to be able to pass through the lane if the size information and vehicle type of the vehicle in front indicate that the vehicle can pass through the lane; otherwise, the vehicle cannot pass through the lane if the size information or vehicle type of the vehicle in front indicates that the vehicle cannot pass through the lane.

[0218] When a vehicle's camera blocks a traffic sign, it cannot capture the sign, preventing the vehicle from directly obtaining information such as lane vehicle types, height restrictions, width restrictions, and speed limits. In this situation, the vehicle can indirectly determine its lane passage ability based on the dimensions and / or type of the vehicle in front. This method provides an indirect way to determine lane passage ability when the traffic sign is obscured, improving the accuracy of lane clearance assessment in special scenarios (e.g., when a vehicle blocks a traffic sign).

[0219] In one possible implementation, the first sensing information further includes traffic flow information, which comes from point cloud information collected by at least one ranging device and / or image data collected by at least one camera. The traffic flow information includes traffic flow information between vehicles and toll stations.

[0220] In this embodiment, traffic flow information may include the number of vehicles and their speed on the road between the vehicle and the toll station. Traffic flow information can reflect the queuing information for each lane between the vehicle and the toll station, and the average speed of vehicles in each lane. The vehicle can estimate the time required to reach the toll station in each lane based on the traffic flow information, and can flexibly determine the optimal lane to pass through, thereby assisting the driver in lane selection.

[0221] Optionally, the first perception information also includes road topology, which is a set of information describing the spatial relationships and connection structures between elements in a road network. Road topology may include one or more of the following: lane lines and boundaries (e.g., the number, location, and direction of extension of lane lines), nodes and connection relationships (e.g., the connection relationship between lane start and end points and intersections, the connection relationship between adjacent lanes, and the association relationship between lanes and intersections), road connectivity (e.g., the connectivity between lanes), and loop and path rules (e.g., one-way restrictions).

[0222] For example, road topology can be obtained by fusing point cloud data acquired by LiDAR and image data acquired by cameras.

[0223] In one possible implementation, the map information includes at least one of information from the vehicle-mounted navigation map and map information from a remote server.

[0224] In this embodiment, the navigation map information on the vehicle side can be ordinary map information, while the map information from the remote server can be high-definition map information. The map information can be ordinary map information, high-definition map information, or a combination of ordinary map information and high-definition map information, thereby meeting the map information needs of more scenarios.

[0225] In one possible implementation, obtaining the traffic attribute information of at least one lane of the toll station based on map information and / or first perception information can be achieved in ways including but not limited to the following:

[0226] Input map information and / or first perception information into at least one artificial intelligence (AI) model to obtain traffic attribute information for at least one lane.

[0227] In this embodiment, the AI ​​model can learn the correspondence between the lanes and traffic attribute information of the toll station. After inputting map information and / or first perception information into the AI ​​model, the AI ​​model can output the traffic attribute information of at least one lane of the toll station. The image data input into the AI ​​model can be in the form of an image, and the point cloud data input into the AI ​​model can be in the form of a point cloud image or raw point cloud data.

[0228] By using an AI model, traffic attribute information for at least one lane can be obtained quickly without preprocessing map information and / or initial perception information, thus improving efficiency.

[0229] In one possible implementation, the AI ​​model includes a trained deep learning model. After training, the AI ​​model can learn the correspondence between the various lanes and traffic attribute information at the toll station. The deep learning model can include attention-based deep learning models, such as the Transformer model.

[0230] In one possible implementation, at least one sensing device includes at least one camera and at least one ranging device; the above-mentioned obtaining the traffic attribute information of at least one lane of the toll station based on map information and / or first sensing information can be implemented in ways including but not limited to the following:

[0231] If the distance between the toll station and the vehicle is less than a first threshold but greater than a second threshold, traffic attribute information for at least one lane of the toll station is obtained based on map information and image data from at least one camera; or

[0232] When the distance between the toll station and the vehicle is less than a second threshold, the traffic attribute information of at least one lane of the toll station is obtained based on map information, image data from at least one camera, and point cloud data from at least one ranging device.

[0233] When the distance between the toll station and the vehicle is less than a second threshold, the traffic attribute information of at least one lane of the toll station is obtained based on image data from at least one camera and point cloud data from at least one ranging device.

[0234] In this embodiment of the application, the distance between the toll station and the vehicle can be determined based on one or more of the following: map information, image data from at least one camera, and point cloud data from at least one ranging device.

[0235] This application embodiment can achieve coarse mapping at a distance and precise mapping up close. When the vehicle is far from the toll station (less than a first threshold and greater than a second threshold), the traffic attribute information of at least one lane of the toll station can be obtained based on map information and image data from at least one camera. This information is available even without the participation of a ranging device. When the vehicle is close to the toll station (less than the second threshold), the traffic attribute information of at least one lane of the toll station can be obtained based on map information, image data from at least one camera, and point cloud data from at least one ranging device. With the participation of a ranging device, more accurate traffic attribute information for at least one lane of the toll station can be obtained. Furthermore, when the vehicle is close to the toll station (less than the second threshold), the traffic attribute information of at least one lane of the toll station can also be obtained based on image data from at least one camera and point cloud data from at least one ranging device. This allows for the acquisition of traffic attribute information for at least one lane of the toll station even without map information, and can be applied to scenarios where map information cannot be obtained (e.g., map software malfunction or inability to obtain map information due to network issues).

[0236] Optionally, the first threshold is related to one or more of the camera's detection range, the camera's image quality, weather conditions, and the vehicle's speed.

[0237] The first threshold can be a pre-set fixed value or a dynamically changing value. When the first threshold is a pre-set fixed value, it can be related to the camera's detection range. For example, if the camera's detection range is within 180 meters, the first threshold can be set to a value less than or equal to 180 meters. Optionally, the first threshold can be set to the camera's maximum detection distance; if the camera's detection range is within 180 meters, the first threshold can be set to 180 meters.

[0238] When the first threshold is a dynamically changing value, it can be related to one or more of the following: the camera's detection range, the camera's image quality, weather conditions, and the vehicle's speed. Weather conditions can include visibility. For example, all other things being equal, the first threshold may be positively correlated with visibility (e.g., the higher the visibility, the higher the first threshold can be set) and positively correlated with vehicle speed (e.g., the higher the vehicle speed, the higher the first threshold can be set).

[0239] Optionally, the second threshold is related to one or more of the ranging device's detection range, detection accuracy, weather conditions, and vehicle speed.

[0240] The second threshold can be a pre-set fixed value or a dynamically changing value. When the second threshold is a pre-set fixed value, it can be related to the detection range of the ranging device. For example, taking a lidar ranging device as an example, if the detection range of the ranging device is within 130 meters, then the second threshold can be set to a value less than or equal to 130 meters. Optionally, the second threshold can be set to the maximum detection distance of the ranging device. If the detection range of the ranging device is within 130 meters, then the second threshold can be set to 130 meters.

[0241] When the second threshold is a dynamically changing value, it can be related to one or more of the following: the detection range of the ranging device, the image quality of the ranging device, weather conditions, and vehicle speed. Weather conditions can include visibility. For example, all other things being equal, the second threshold may be positively correlated with visibility (e.g., the higher the visibility, the higher the second threshold can be set) and positively correlated with vehicle speed (e.g., the higher the vehicle speed, the higher the second threshold can be set).

[0242] In one possible implementation, the above-mentioned method of obtaining traffic attribute information for at least one lane of the toll station based on map information and image data from at least one camera can be implemented in ways including but not limited to the following:

[0243] Input map information and image data from at least one camera into the AI ​​model to obtain traffic attribute information for at least one lane of the toll station; or

[0244] The distance between the toll station and the vehicle is determined based on the map information. The camera depth of at least one camera is determined based on the distance between the toll station and the vehicle. The traffic attribute information of at least one lane of the toll station is determined based on the camera depth of at least one camera and the image data of at least one camera.

[0245] In one possible example, an AI model can be used to obtain traffic attribute information for at least one lane of the toll station. For instance, if the distance between the toll station and the vehicle is less than a first threshold and greater than a second threshold, map information and image data from at least one camera can be input into the AI ​​model to obtain the traffic attribute information for at least one lane of the toll station.

[0246] In another possible example, the map information can provide the distance between the toll station and the vehicle. Based on the distance between the toll station and the vehicle, the camera intrinsic parameters of at least one camera (e.g., focal length, principal point, pixel size, etc.), and the pixel coordinates of the target (e.g., toll station, traffic sign, etc.) in the image data of at least one camera, the depth of the camera coordinate system can be determined based on the principle of similar triangles. The camera coordinate system is then converted into the vehicle coordinate system, and the accurate 3D position of the target relative to the vehicle can be obtained in the vehicle coordinate system. Thus, the traffic attribute information of at least one lane of the toll station can be obtained.

[0247] In this embodiment of the application, when sketching from a distance, the traffic attribute information of at least one lane of the toll station can be obtained based on map information and image data from at least one camera. Either an AI model can be used to quickly obtain the traffic attribute information of at least one lane to improve efficiency, or a white-box algorithm can be used, which is easy to optimize.

[0248] In one possible implementation, the above-mentioned method of obtaining the traffic attribute information of at least one lane of the toll station based on map information, image data from at least one camera, and point cloud data from at least one ranging device can be implemented in ways including but not limited to the following:

[0249] By inputting map information, image data from at least one camera, and point cloud data from at least one ranging device into the AI ​​model, traffic attribute information for at least one lane of the toll station can be obtained; or

[0250] The image data from at least one camera and the point cloud data from at least one ranging device are used to perform 3D reconstruction to obtain a first 3D reconstruction result; the first 3D reconstruction result is fused with map information to obtain a fusion result; at least one image detection result is determined based on the image data from at least one camera; the fusion result is matched with at least one image detection result to obtain at least one first matching result; and multi-camera fusion processing is performed based on at least one first matching result to obtain traffic attribute information for at least one lane.

[0251] In one possible example, an AI model can be used to obtain traffic attribute information for at least one lane of the toll station. For instance, if the distance between the toll station and the vehicle is less than a second threshold, map information, image data from at least one camera, and point cloud data from at least one ranging device can be input into the AI ​​model to obtain traffic attribute information for at least one lane of the toll station.

[0252] In another possible example, data fusion and 3D reconstruction are performed using image data from at least one camera and point cloud data from at least one ranging device. This leverages the complementary advantages of the cameras and the ranging device to generate a high-precision, high-information-density first 3D reconstruction result. Fusing this first 3D reconstruction result with map information (e.g., for high-precision maps, importing coordinates of lane lines, traffic signs, toll booths, etc.) (e.g., matching and deduplication) aligns the first 3D reconstruction result with the map, resulting in a fusion result with high-precision target localization. For each camera's image data, one image detection result can be obtained, which may include the category and bounding box of the detected target in the image data. Matching the fusion result with each image detection result yields at least one first matching result. Multi-camera fusion processing is then performed on this at least one first matching result to obtain accurate traffic attribute information for at least one lane.

[0253] In this embodiment of the application, when the accuracy is accurate at close range, the geometric and semantic fusion of multi-source data (camera, ranging device, map) is combined with the redundant detection and complementary perspective of at least one camera to finally output a more robust and accurate environmental perception result, thereby obtaining accurate traffic attribute information for at least one lane.

[0254] In one possible implementation, the above-mentioned method of obtaining traffic attribute information for at least one lane of the toll station based on image data from at least one camera and point cloud data from at least one ranging device can be implemented in ways including but not limited to the following:

[0255] By inputting image data from at least one camera and point cloud data from at least one ranging device into an AI model, traffic attribute information for at least one lane of the toll station can be obtained; or

[0256] The image data from at least one camera and the point cloud data from at least one ranging device are used to perform 3D reconstruction to obtain a second 3D reconstruction result; at least one image detection result is determined based on the image data from at least one camera; the second 3D reconstruction result is matched with the at least one image detection result to obtain at least one second matching result; and multi-camera fusion processing is performed based on the at least one second matching result to obtain traffic attribute information for at least one lane.

[0257] In one possible example, an AI model can be used to obtain traffic attribute information for at least one lane of the toll station. For instance, if the distance between the toll station and the vehicle is less than a second threshold, image data from at least one camera and point cloud data from at least one ranging device can be input into the AI ​​model to obtain traffic attribute information for at least one lane of the toll station.

[0258] In another possible example, data fusion and 3D reconstruction are performed using image data from at least one camera and point cloud data from at least one ranging device. This combines the complementary advantages of the cameras and the ranging device to generate a high-precision, high-information-density second 3D reconstruction result. For each camera's image data, one image detection result can be obtained, which may include the category and bounding box of the detected target in the image data. The second 3D reconstruction result is then matched with each image detection result to obtain at least one second matching result. This second matching result is then subjected to multi-camera fusion processing to obtain accurate traffic attribute information for at least one lane.

[0259] In this embodiment, for near-field precision, geometric and semantic fusion of multi-source data (cameras, ranging devices) is used, combined with redundant detection and complementary perspectives from at least one camera, to ultimately output a more robust and accurate environmental perception result, thereby obtaining accurate traffic attribute information for at least one lane. This embodiment can be applied to scenarios where map information is unavailable.

[0260] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating a process for obtaining traffic attribute information of at least one lane through an AI model, as provided in an embodiment of this application. Figure 5 Taking a first threshold of 180 meters and a second threshold of 130 meters as an example. Figure 5 As shown, when the distance between the toll station and the vehicle is less than or equal to 180 meters and greater than 130 meters, the vehicle control device can acquire image data, map information, and road topology from at least one camera. The image data, map information, and road topology from the at least one camera are then input into an AI model to obtain the traffic attribute information for at least one lane of the toll station. When the distance between the toll station and the vehicle is less than or equal to 130 meters, the vehicle control device can acquire image data from at least one camera, point cloud data from at least one ranging device, traffic flow information, map information (optional), and road topology. The image data from at least one camera, point cloud data from at least one ranging device, traffic flow information, map information (optional), and road topology are then input into an AI model to obtain accurate traffic attribute information for at least one lane of the toll station.

[0261] Please see Figure 6 , Figure 6 This is a schematic diagram illustrating a process for obtaining traffic attribute information of at least one lane through three-dimensional reconstruction and data fusion, as provided in an embodiment of this application. Figure 6 Taking a first threshold of 180 meters and a second threshold of 130 meters as an example. Figure 6As shown, when the distance between the toll station and the vehicle is less than or equal to 180 meters and greater than 130 meters, the vehicle control device can determine the distance between the toll station and the vehicle based on map information, determine the camera depth of at least one camera based on the distance between the toll station and the vehicle, and determine the traffic attribute information of at least one lane of the toll station based on the camera depth of at least one camera and the image data of at least one camera. When the distance between the toll station and the vehicle is less than or equal to 130 meters, the vehicle control device can perform three-dimensional reconstruction using the image data of at least one camera and the point cloud data of at least one ranging device to obtain a first three-dimensional reconstruction result; fuse the first three-dimensional reconstruction result with map information to obtain a fusion result; determine at least one image detection result based on the image data of at least one camera; match the fusion result with the at least one image detection result to obtain at least one first matching result; and perform multi-camera fusion processing based on the at least one first matching result to obtain the traffic attribute information of at least one lane.

[0262] Please see Figure 7 , Figure 7 This is a schematic diagram illustrating another process for obtaining traffic attribute information of at least one lane through three-dimensional reconstruction and data fusion, as provided in an embodiment of this application. Figure 7 Taking a first threshold of 180 meters and a second threshold of 130 meters as an example. Figure 7 As shown, when the distance between the toll station and the vehicle is less than or equal to 180 meters and greater than 130 meters, the vehicle control device can determine the distance between the toll station and the vehicle based on map information, determine the camera depth of at least one camera based on the distance between the toll station and the vehicle, and determine the traffic attribute information of at least one lane of the toll station based on the camera depth of at least one camera and the image data of at least one camera. When the distance between the toll station and the vehicle is less than or equal to 130 meters, the vehicle control device can perform 3D reconstruction using the image data of at least one camera and the point cloud data of at least one ranging device to obtain a second 3D reconstruction result; determine at least one image detection result based on the image data of at least one camera; match the second 3D reconstruction result with the at least one image detection result to obtain at least one second matching result; and perform multi-camera fusion processing based on the at least one second matching result to obtain the traffic attribute information of at least one lane.

[0263] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0264] Output the first prompt message, which is used to indicate the traffic attribute information of at least one lane.

[0265] In this embodiment, the first prompt information can be output through at least one of the following methods: interface display, projection, and voice output. Please refer to... Figure 8 , Figure 8 This is a schematic diagram illustrating the display of a first prompt message via an interface, as provided in an embodiment of this application. The first prompt message can be displayed through a vehicle's display device, which may include one or more of the following: a central control screen, an integrated display screen, a head-up display (HUD) device, an instrument panel, a passenger-side screen, a rear-seat screen, or a projection screen.

[0266] like Figure 8 As shown, the display device can display a first interface, which displays first prompt information. The first interface shows three lanes: a left-hand lane, a middle lane, and a right-hand lane. The left-hand lane is the vehicle's driving lane. Each lane displays traffic attribute information such as toll type, passability status, height limit, width limit, and vehicle queuing information to assist the driver in choosing the appropriate lane. The traffic attribute information for each lane can be assigned to that lane on the first interface, providing the driver with more intuitive traffic attribute information for each lane.

[0267] In one possible implementation, the display of access attribute information has multiple display modes, including a three-dimensional element mode.

[0268] The above-mentioned output of the first prompt message can be implemented in ways including but not limited to the following:

[0269] The first interface displays a 3D element mode, which provides traffic attribute information for at least one lane in a 3D manner.

[0270] In this embodiment, the multiple display modes may include a three-dimensional display mode and a two-dimensional display mode. Figure 8 It is a two-dimensional display mode. Figure 9 It features a 3D display mode. Multiple display modes are also available, including single-lane and multi-lane display modes. Figure 10 For multi-lane display mode, Figure 11 This is a single-lane display mode.

[0271] Please see Figure 9 , Figure 9 This is another schematic diagram illustrating the display of the first prompt information via an interface, provided as an embodiment of this application. For example... Figure 9 As shown, lane traffic attribute information can be displayed in three dimensions, thereby improving the stereoscopic display effect, enhancing the sense of space, and improving the interactive experience.

[0272] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0273] In response to the user's mode switching operation input on the first interface, switch the display mode.

[0274] Switching display modes can involve changing from displaying traffic attribute information for multiple lanes to displaying traffic attribute information for a single lane (i.e., switching from a multi-lane display mode to a single-lane display mode), or vice versa. Furthermore, switching display modes can also involve changing from a 3D display mode to a 2D display mode, or from a 2D element mode to a 3D element mode.

[0275] Please see Figure 10 , Figure 10 This is a schematic diagram illustrating a display mode switching method via an interface, as provided in an embodiment of this application. Figure 10 As shown, an interactive button for "Switch Display Mode" can be displayed on the first interface. By touching this button, the user can switch the display mode for the traffic attribute information of at least one lane. Please refer to [link / reference]. Figure 11 , Figure 11 This is a schematic diagram illustrating another method of switching display modes via an interface, as provided in an embodiment of this application. Figure 11 As shown, when the user clicks the "Switch Display Mode" interactive button, they can switch from multi-lane display mode to single-lane display mode. For example, switching from multi-lane traffic attribute information to single-lane traffic attribute information of the lane in which the vehicle is located can intuitively display more traffic attribute information of that lane.

[0276] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0277] At least one recommended lane is determined based on the traffic attribute information of at least one lane and the traffic constraints of the vehicle. The recommended lane is the lane that satisfies the traffic constraints of the vehicle.

[0278] The display device outputs a second prompt message, which is used to indicate the location of at least one recommended lane and / or the recommended value for each recommended lane.

[0279] In this embodiment, the vehicle's traffic constraints can be the constraints on the vehicle's passage in the lane. Whether the lane meets the vehicle's traffic constraints can be determined based on the vehicle's traffic constraints and the lane's traffic attribute information. For example, the vehicle constraint information may include at least one of the following: vehicle type, vehicle height, vehicle width, and vehicle toll collection strategy.

[0280] A recommended lane is a lane that meets the traffic constraints for vehicles. There can be one or more lanes that meet the traffic constraints. The recommendation value is determined based on the lane's traffic attribute information. The higher the recommendation value of a lane, the better the lane is considered. For example, all other things being equal, the longer the queue length of a lane, the lower its recommendation value; the more vehicles are in the queue, the lower its recommendation value.

[0281] The second prompt message can be output through at least one of the following methods: interface display, projection, or voice output. Please refer to [link / reference]. Figure 12 , Figure 12 This is a schematic diagram illustrating a second prompt message displayed via an interface, as provided in an embodiment of this application. The second prompt message can be output through a vehicle's display device, which may include one or more of the following: a central control screen, an integrated display screen, a head-up display (HUD) device, an instrument panel, a passenger-side screen, a rear-seat screen, or a projection screen.

[0282] like Figure 12 As shown, the display device can display a second interface, through which second prompt information is displayed. The second interface displays three lanes: the left-hand lane, the middle lane, and the right-hand lane. The left-hand lane is the vehicle's driving lane, while the left-hand and middle lanes are recommended lanes. The "Recommended Lanes" can be displayed within the areas containing the left-hand and middle lanes, and each recommended lane can also display a corresponding recommendation value to assist the driver in selecting the appropriate lane. The right-hand lane is not a recommended lane, as its traffic attribute information does not meet the vehicle's traffic constraints.

[0283] The second interface also displays traffic attribute information for each lane, including toll type, passability status, height restriction, width restriction, and vehicle queuing information. Figure 12 (Not shown).

[0284] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0285] If the lane in which the vehicle is currently located does not meet the vehicle's traffic constraints, the control module outputs a third prompt message to indicate that the current lane does not meet the vehicle's traffic constraints; or,

[0286] If the lane in which the vehicle is currently located meets the traffic constraints, output a fourth prompt message. The fourth prompt message is used to indicate that the current lane meets the traffic constraints.

[0287] In this embodiment, the third prompt information can be output through at least one of the following methods: interface display, projection, and voice output. Please refer to [link / reference]. Figure 13 , Figure 13 This is a schematic diagram illustrating how a third prompt message is displayed via an interface, as provided in an embodiment of this application. Figure 13 As shown, the display device can display a third interface, through which a third prompt message is displayed. If the lane in which the vehicle is currently located does not meet the vehicle's toll type, the third prompt message "The current lane does not meet the vehicle's toll type" will be displayed through the third interface.

[0288] The fourth prompt message can be output through at least one of the following methods: interface display, projection, or voice. Please refer to [link / reference]. Figure 14 , Figure 14 This is a schematic diagram illustrating the display of a fourth prompt message via an interface, as provided in an embodiment of this application. Figure 14 As shown, the display device can display a fourth interface, through which a fourth prompt message is displayed. If the lane in which the vehicle is currently located meets the vehicle's traffic constraints, then the fourth prompt message "The current lane meets the vehicle's needs" will be displayed through the fourth interface.

[0289] The display methods for the first, second, third, and fourth prompt messages can include basic elements and enhanced forms. Basic elements can include at least one of text, icons, and colors, while enhanced forms can include at least one of dynamic blinking, fading, and animated loading.

[0290] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0291] Based on the traffic attribute information of at least one lane of the toll station, plan the first path for vehicles to pass through the toll station, and the first path passes through the first lane of at least one lane;

[0292] Navigate the vehicle based on the first route.

[0293] In this embodiment, the vehicle may include a positioning system, which is a device for acquiring location information and can be used to achieve real-time vehicle positioning and provide the vehicle's geographical location information. Examples of positioning systems include the Global Positioning System (GPS) or the BeiDou Navigation Satellite System. In some cases, the positioning system can achieve vehicle location through a positioning server (such as a navigation server, map server, etc.).

[0294] In this embodiment of the application, the first path passes through the first lane of at least one lane of the toll station. The path planning and navigation can be performed based on the traffic attribute information of at least one lane of the toll station, which can provide the driver with an accurate navigation path through the toll station.

[0295] In one possible implementation, the above-mentioned planning of a first path for a vehicle to pass through the toll station based on the traffic attribute information of at least one lane of the toll station can be achieved in ways including but not limited to the following:

[0296] The first lane is determined based on the traffic attribute information of at least one lane at the toll station;

[0297] The first lane is planned as the first path for vehicles to pass through the toll station.

[0298] In this embodiment, the first lane can be determined based on the traffic attribute information of at least one lane of the toll station. For example, the traffic attribute information of the first lane meets the needs of the vehicle. Planning a first path for the vehicle to pass through the toll station based on the first lane allows the vehicle to pass through the toll station from the first lane, thus planning a reasonable path for the vehicle.

[0299] In one possible implementation, the planned first route for vehicles to pass through the toll station can be achieved in ways including but not limited to the following:

[0300] The first lane is determined based on the traffic attribute information of at least one lane and the traffic constraints of the vehicle, and the first lane satisfies the traffic constraints of the vehicle.

[0301] In this embodiment, the vehicle's traffic constraints can be the constraints on the vehicle's passage in the lane. Whether the lane meets the vehicle's traffic constraints can be determined based on the vehicle's traffic constraints and the lane's traffic attribute information. For example, the vehicle constraint information may include at least one of the following: vehicle type, vehicle height, vehicle width, and vehicle toll collection strategy.

[0302] In one possible implementation, the planned first route for vehicles to pass through the toll station can be achieved in ways including but not limited to the following:

[0303] At least one recommended lane is determined based on the traffic attribute information of at least one lane and the traffic constraints of the vehicle, and each recommended lane satisfies the traffic constraints of the vehicle.

[0304] In response to a selection instruction from the user, a first lane is determined from at least one recommended lane.

[0305] In this embodiment of the application, the recommended lane is a lane that meets the vehicle traffic constraints, and there may be one or more lanes that meet the vehicle traffic constraints.

[0306] User selection instructions can be implemented through human-computer interaction, which can include voice interaction or user interface interaction. Please refer to [link / reference]. Figure 15 , Figure 15This is a schematic diagram illustrating the selection of a first lane via an interactive interface, as provided in an embodiment of this application. The fifth interface can be displayed on a vehicle's display device, which may include one or more of the following: a central control screen, an integrated display screen, a head-up display (HUD) device, an instrument panel, a passenger-side screen, a rear-seat screen, or a projection screen.

[0307] like Figure 15 As shown, the fifth interface displays three lanes: the left-hand lane, the middle lane, and the right-hand lane. The left-hand lane is the driving lane, while the left-hand and middle lanes are recommended lanes, which are displayed within the areas of the left-hand and middle lanes. A "Change Lane" button can be displayed on the fifth interface. Touching this button allows the user to select the middle lane from the left-hand and middle lanes, causing the vehicle to switch from the current left-hand lane to the middle lane. The fifth interface then displays the planned path for the vehicle to switch from the current left-hand lane to the middle lane, which can be the first path mentioned above. Figure 15 The system can also display a recommended value for each lane; the higher the recommended value, the better the lane.

[0308] The fifth interface also displays traffic attribute information for each lane, including toll type, passability status, height limit, width limit, and vehicle queuing information. Figure 15 (Not shown).

[0309] This application embodiment allows users to select a first lane through human-computer interaction. The first lane is determined by the user, satisfying the user's need for autonomous selection and improving the interactive experience of lane selection. The first lane can be any one of at least one recommended lane, or it can be the lane with the highest recommendation value.

[0310] In one possible implementation, the planned first route for vehicles to pass through the toll station can be achieved in ways including but not limited to the following:

[0311] At least one recommended lane and a recommended value for each recommended lane are determined based on the traffic attribute information of at least one lane and the traffic constraints of the vehicle. Each recommended lane satisfies the traffic constraints of the vehicle.

[0312] The lane with the highest recommendation value from at least one recommended lane is selected as the first lane.

[0313] In this embodiment, the vehicle control device can automatically select the lane with the highest recommended value as the first lane without user intervention. In intelligent driving mode, this can reduce the number of times the user needs to take over, thereby improving the user experience.

[0314] Please see Figure 16 , Figure 16This is a schematic diagram illustrating the planning of a first path, as provided in an embodiment of this application. Figure 16 As shown, a sixth interface can be displayed on the vehicle's display device. This sixth interface shows three lanes: a left-hand lane, a middle lane, and a right-hand lane. The left-hand lane is the vehicle's driving lane, while the right-hand and middle lanes are recommended lanes. The "recommended lanes" can be displayed within the areas containing the right-hand and middle lanes. The intelligent driving system can select the middle lane from the right-hand and middle lanes, causing the vehicle to switch from its current left-hand lane to the middle lane. The sixth interface displays the planned path for this switch, which can be the first path mentioned above. Figure 16 The system can also display a recommended value for each lane; the higher the recommended value, the better the lane.

[0315] The sixth interface also displays traffic attribute information for each lane, including toll type, passability status, height restriction, width restriction, and vehicle queuing information. Figure 16 (Not shown).

[0316] In one possible implementation, vehicle traffic constraints include vehicle type, height, width, and pre-set toll selection strategy.

[0317] In this embodiment, the pre-set toll selection strategy may include ETC priority or manual priority. When the lane's traffic attribute information meets all the conditions of the vehicle's traffic constraints (vehicle type, height, width, toll type, etc.), the lane meets the vehicle's traffic constraints; when the lane's traffic attribute information does not meet any of the vehicle's traffic constraints (vehicle type, height, width, toll type, etc.), the lane does not meet the vehicle's traffic constraints.

[0318] For example, if the lane width limit is greater than the vehicle width, the lane height limit is greater than the vehicle height, the vehicle type matches the lane type, and the lane's toll type matches a pre-set toll selection strategy, then the lane satisfies the vehicle's passage constraints. If the lane width limit is less than or equal to the vehicle width, or the lane height limit is less than or equal to the vehicle height, or the lane's toll type does not match a pre-set toll selection strategy, then the lane does not satisfy the vehicle's passage constraints.

[0319] In one possible implementation, the pre-set toll selection strategy includes: ETC priority, manual priority, or automatic mode.

[0320] Both ETC priority and manual priority modes can be preset by the user. If the user does not preset them, the system defaults to automatic mode. Automatic mode can be a condition-based toll collection method. For example, if the vehicle does not have ETC or the ETC is unavailable, the system will automatically select the manual lane; or if the vehicle's ETC is available, the system will select the ETC lane.

[0321] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0322] When the distance between the toll station and the vehicle is less than the third threshold, the gate status of the lane where the vehicle is located is detected based on the first perception information.

[0323] Output the gate status of the lane where the vehicle is located.

[0324] In this embodiment, the third threshold can be predefined, such as 20 meters, 10 meters, etc. The third threshold can be less than the second threshold.

[0325] The gate status can include whether the gate arm is raised or lowered. The gate status of the lane where the vehicle is located can be displayed through at least one of the following methods: interface display, projection, or voice output.

[0326] In one possible implementation, the vehicle control method described above may also perform the following steps, including but not limited to:

[0327] When the distance between the toll station and the vehicle is less than the fourth threshold, second sensing information from at least one sensing device is acquired;

[0328] The lane accessibility information is updated based on the obstacle information detected in the second perception information.

[0329] In this embodiment, the fourth threshold can be predefined, such as 100 meters, 50 meters, 20 meters, etc. The fourth threshold can be less than the second threshold. The second sensing information can be obtained through a camera and / or a ranging device.

[0330] Obstacle information may include one or more of the following: traffic cones, water-filled barriers, fallen rocks, mud, goods dropped by other vehicles, accident vehicles, signs placed on the lane (such as construction signs, accident signs), and other obstacles that affect the passage of vehicles.

[0331] In some situations, when an accident occurs in front of a vehicle, such as a traffic accident, falling rocks, or mud, the information displayed on the traffic signs (e.g., ETC passage is permitted) may not be updated in time, potentially leading to discrepancies between the vehicle's passage attribute information and the actual situation. When the distance to the toll station is close to the vehicle, the passage attribute information for the vehicle's lane can be updated after detecting obstacles in the vehicle's lane, providing more accurate lane passage attribute information. This helps the driver or intelligent driving system select a more suitable lane to pass through the toll station.

[0332] The foregoing has described the application scenarios and methods provided by the embodiments of this application. The apparatus of the embodiments of this application is provided below. It is understood that the various apparatuses provided in the embodiments of this application, such as vehicle control devices and vehicles, include hardware structures, software units, or combinations of hardware and software structures to perform the functions described in the above method embodiments. Those skilled in the art should readily recognize that the apparatus and its modules can be implemented in hardware or a combination of hardware and computer software in conjunction with the various functions described in the embodiments disclosed herein. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different apparatus implementations in different application scenarios to implement the aforementioned method embodiments, and different implementations of the apparatus should not be considered beyond the scope of the embodiments of this application.

[0333] Please see Figure 17 , Figure 17 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application.

[0334] like Figure 17 As shown, the vehicle control device 170 may include a communication unit 1701 and a processing unit 1702. The communication unit 1701 and the processing unit 1702 may be software, hardware, or a combination of software and hardware.

[0335] The communication unit 1701 can implement sending and / or receiving functions, and can also be described as a transceiver unit. The communication unit 1701 can also be a unit integrating an acquisition unit and a sending unit, wherein the acquisition unit is used to implement the receiving function, and the sending unit is used to implement the sending function. Optionally, the communication unit 1701 can be used to receive information sent by other devices, and can also be used to send information to other devices.

[0336] In one possible design, the vehicle control device 170 may correspond to the above. Figure 4The vehicle control device in the illustrated method embodiment, such as vehicle control device 170, can be an electronic device or a chip within an electronic device. Vehicle control device 170 may include components for performing the above-described... Figure 4 The method embodiment shown is a unit that performs the operation by the vehicle control device, and each unit in the vehicle control device 170 is respectively for implementing the above-mentioned... Figure 4 The operations performed by the vehicle control device in the illustrated method embodiment are as follows: The descriptions of each unit are as follows:

[0337] The communication unit 1701 is used to acquire map information, which includes information about the environmental elements of the toll station.

[0338] The communication unit 1701 is also used to acquire first sensing information from at least one sensing device of the vehicle, the first sensing information including detection data of the toll station;

[0339] The processing unit 1702 is used to obtain the traffic attribute information of at least one lane of the toll station based on map information and / or first perception information, and the traffic attribute information is used to indicate the traffic conditions of the lane.

[0340] The communication unit 1701 can communicate with a positioning server (such as a navigation server, map server, etc.) to obtain map information. The communication unit 1701 can also communicate with at least one sensing device to obtain first sensing information.

[0341] The execution steps of the communication unit 1701 and processing unit 1702 in this design can be referred to the steps described above. Figure 4 The implementation method of the vehicle control device shown in the method embodiment corresponds to the implementation method of the vehicle control device shown in the figure.

[0342] Regarding the technical effects of the implementation methods performed by the communication unit 1701 and the processing unit 1702 of this design, please refer to the description above. Figure 4 The technical effects of the illustrated method embodiments are described below.

[0343] exist Figure 17 The vehicle control device 170 described herein can obtain traffic attribute information for at least one lane of the toll station. The traffic attribute information is used to indicate the traffic conditions of the lane, thereby assisting the driver in selecting the appropriate lane.

[0344] For cases where the aforementioned vehicle control device 170 can be an electronic device, please refer to [reference needed]. Figure 18 The diagram shows the structure of the electronic device.

[0345] It should be understood that Figure 18 The electronic device 180 shown is merely an example; the electronic device in this application embodiment may also include other components, or include components related to... Figure 18 Components with similar functions, or not necessarily including Figure 18 All components.

[0346] Electronic device 180 includes a transceiver interface 1801 and at least one processor 1802.

[0347] The electronic device 180 can correspond to a vehicle control device. The transceiver interface 1801 is used to transmit and receive signals, and at least one processor 1802 executes program instructions, causing the electronic device 180 to implement the corresponding flow of the method executed by the corresponding device in the above method embodiment.

[0348] In one possible design, the electronic device 180 may correspond to the above. Figure 4 The vehicle control device in the illustrated method embodiment, such as the electronic device 180, can be a vehicle control device itself or a chip within the vehicle control device. The electronic device 180 may include components for performing the operations executed by the vehicle control device in the above method embodiment, and each component in the electronic device 180 is specifically designed to implement the operations executed by the vehicle control device in the above method embodiment. Specifically, it can be as follows:

[0349] The transceiver interface 1801 is used to obtain map information, which includes information about the environmental elements of the toll station.

[0350] The transceiver interface 1801 is also used to acquire first sensing information from at least one sensing device of the vehicle, the first sensing information including detection data of the toll station;

[0351] The processor 1802 is used to obtain traffic attribute information of at least one lane of the toll station based on map information and / or first perception information, the traffic attribute information being used to indicate the traffic conditions of the lane.

[0352] Regarding the transceiver interface 1801 and at least one processor 1802 of this design, the execution steps can be referred to the corresponding steps described above. Figure 4 The implementation method of the vehicle control device shown in the method embodiment corresponds to the implementation method of the vehicle control device shown in the figure.

[0353] Regarding the technical effects of the transceiver interface 1801 and the implementation methods executed by at least one processor 1802 in this design, please refer to the description above. Figure 4 The technical effects of the illustrated method embodiments are described below.

[0354] exist Figure 18 The described electronic device 180 can obtain traffic attribute information for at least one lane of the toll station. The traffic attribute information is used to indicate the traffic conditions of the lane, thereby assisting the driver in selecting the appropriate lane.

[0355] For cases where the aforementioned vehicle control device 170 can be a chip or a chip system, please refer to [reference needed]. Figure 19 The diagram shows the structure of the chip.

[0356] like Figure 19 As shown, chip 190 includes processor 1901 and interface 1902. The number of processors 1901 can be one or more, and the number of interfaces 1902 can be multiple. It should be noted that the functions of processor 1901 and interface 1902 can be implemented through hardware design, software design, or a combination of both; no restrictions are placed here.

[0357] Optionally, chip 190 may also include memory 1903 for storing necessary program instructions and data.

[0358] In this application, processor 1901 can be used to call the implementation program of the vehicle control method provided in one or more embodiments of this application in a vehicle control device from memory 1903, and execute the instructions included in the program. Interface 1902 can be used to output the execution result of processor 1901. In this application, interface 1902 can be specifically used to output various messages or information of processor 1901.

[0359] For vehicle control methods provided in one or more embodiments of this application, please refer to the foregoing. Figure 4 The various embodiments shown are not described in detail here.

[0360] The processor in this application embodiment can be a central processing unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0361] The memory in this application embodiment is used to provide storage space, in which data such as operating system and computer programs can be stored. The memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM).

[0362] According to the method provided in the embodiments of this application, the embodiments of this application also provide a computer-readable storage medium storing a computer program. When the computer program is run on one or more processors, it can implement the above-mentioned... Figure 4 The method shown.

[0363] According to the method provided in the embodiments of this application, the embodiments of this application also provide a computer program product, which includes a computer program. When the computer program runs on a processor, it can achieve the above-mentioned... Figure 4 The method shown.

[0364] This application also provides a vehicle that includes at least one vehicle control device 170, or electronic device 180, or chip 190.

[0365] Optionally, the vehicle may include commercial vehicles, passenger vehicles, industrial vehicles (such as forklifts, trailers, tractors, etc.), engineering vehicles (such as excavators, bulldozers, cranes, etc.), etc., and this application embodiment does not limit this.

[0366] Optionally, the vehicle is used to achieve the above. Figure 4 The method shown.

[0367] For cases where the aforementioned vehicle control device 170 can be an intelligent driving device, please refer to [reference needed]. Figure 20 The diagram shows the structure of an intelligent driving device. Figure 20 This is a schematic diagram of the structure of an intelligent driving device provided in an embodiment of this application. Figure 20As shown, the intelligent driving device 100 may include a perception system 120, a display device 130, and a computing platform 150. The perception system 120 may include several sensors for sensing information about the environment surrounding the intelligent driving device 100. For example, the perception system 120 may include a positioning system, which can be a global navigation satellite system (GNSS), such as the Global Positioning System (GPS) or the BeiDou system. Alternatively, the perception system 120 may also include one or more of the following: an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera.

[0368] The display devices 130 within the cockpit of the intelligent driving equipment 100 are mainly divided into two categories: the first is in-vehicle displays; the second is projection displays, such as head-up displays (HUDs). In-vehicle displays are physical displays and an important component of in-vehicle infotainment systems. Multiple displays can be installed in the cockpit, such as digital instrument cluster displays and central control screens. In some possible implementations, one or more of the aforementioned in-vehicle displays can be human-machine interfaces (HMIs), for example, the central control screen can be an HMI. Head-up displays, also known as head-up display systems, are mainly used to display driving information such as speed, navigation, and lane information on a display device in front of the driver (e.g., the windshield). This reduces driver eye movement time, avoids pupil changes caused by eye movement, and improves driving safety and comfort. HUDs include, for example, combiner-HUD (C-HUD) systems, windshield-HUD (W-HUD) systems, and augmented reality HUD (AR-HUD) systems.

[0369] Some or all of the functions of the intelligent driving device 100 can be controlled by the computing platform 150. The computing platform 150 may include processors 151 to 15n. A processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a central processing unit (CPU), microprocessor, graphics processing unit (GPU) (which can be understood as a type of microprocessor), or digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. These logical relationships are fixed or reconfigurable. For example, the processor may be a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as a field-programmable gate array (FPGA). In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. Furthermore, the processor can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), tensor processing unit (TPU), deep learning processing unit (DPU), etc. In addition, the computing platform 150 may also include a memory for storing instructions. Some or all of the processors 151 to 15n can call the instructions in the memory to implement the corresponding functions.

[0370] The computing platform 150 can control the operation of the intelligent driving system, which may include an advanced driving assistance system (ADAS) and an autonomous driving system (ADS). The intelligent driving system utilizes various sensors on the vehicle (including but not limited to: LiDAR, millimeter-wave radar, cameras, ultrasonic sensors, GPS, and inertial measurement units) to acquire information from the vehicle's surroundings, and analyzes and processes this information to achieve functions such as obstacle perception, target recognition, vehicle localization, path planning, and driver monitoring / alerts, thereby improving the safety, automation, and comfort of driving.

[0371] At different levels of autonomous driving (or intelligent driving), based on artificial intelligence algorithms and information acquired by multiple sensors, intelligent driving systems can achieve different levels of driver assistance. These driving levels are based on the classification standards of the Society of Automotive Engineers (SAE). Level L0 is no automation; Level L1 is driver assistance; Level L2 is partial automation; Level L3 is conditional automation; Level L4 is high automation; and Level L5 is full automation. At Levels L1 to L3, the task of monitoring road conditions and reacting is jointly completed by the driver and the system, requiring the driver to take over dynamic driving tasks. Levels L4 and L5 allow the driver to completely transform into a passenger. Currently, the functions that intelligent driving systems can achieve mainly include, but are not limited to: adaptive cruise control, automatic emergency braking, automatic parking, blind spot monitoring, forward cross-traffic alert / braking, rear cross-traffic alert / braking, forward collision warning, lane departure warning, lane keeping assist, rear collision warning, traffic sign recognition, traffic jam assist, and highway assist. It should be understood that the above-mentioned functions can have specific modes at different driving levels (L0-L5), and the higher the driving level, the more intelligent the corresponding mode.

[0372] Figure 21 A schematic diagram of the architecture of an intelligent driving system provided in an embodiment of this application is shown. Figure 21 As shown, the intelligent driving system 200 includes a perception module 210, a human-machine interaction module 220, a display module 230, and a control module 240.

[0373] The sensing module 210 may include Figure 20The perception system 120 shown includes one or more camera devices or one or more radar sensors for collecting environmental information about the area where the vehicle is located, such as parking line information and obstacle information. The perception module 210 can also process the collected environmental information to build a world model of roads, obstacles, etc., for downstream modules (such as the human-machine interaction module 220 and the control module 240). The perception module 210 can send the collected and / or determined information to the control module 240. The perception module 210 may include at least one of the aforementioned perception devices.

[0374] The human-computer interaction module 220 may include Figure 20 One or more of the display devices 130 shown may include, for example, an HMI; the human-computer interaction module 220 may also include a sound-emitting device (such as a speaker, audio jack, etc.) and a sound-receiving device (such as a microphone). The display module 230 may include... Figure 20 One or more of the display devices 130 shown are configured to display the vehicle infotainment system interface. The human-machine interface module 220 can receive user commands (including voice commands, touch screen commands, etc.) and then control the changes of the interface displayed by the display module 230 according to the commands.

[0375] Control module 240 may include Figure 20 One or more processors in the computing platform 150 shown.

[0376] It should be understood that the above module is only an example, and in actual applications, the above module may be added or deleted according to actual needs.

[0377] Optionally, the above Figure 4 The vehicle control method shown can be carried in the form of an executable file within the vehicle operating system (VOS).

[0378] This application also provides a processing apparatus, including a processor and an interface; the processor is used to execute the method in any of the above method embodiments.

[0379] It should be understood that the above-described processing device can be a chip. The units in the various device embodiments and the electronic devices in the method embodiments correspond completely, with corresponding modules or units executing corresponding steps. For example, the communication unit (transceiver) executes the receiving or sending steps in the method embodiments, while other steps besides sending and receiving can be executed by the processing unit (processor). The specific functions of each unit can be found in the corresponding method embodiments. There can be one or more processors.

[0380] It is understood that in the embodiments of this application, the electronic device may perform some or all of the steps in the embodiments of this application. These steps or operations are merely examples, and the embodiments of this application may also perform other operations or variations thereof. Furthermore, the steps may be performed in different orders as presented in the embodiments of this application, and it is not necessarily necessary to perform all the operations in the embodiments of this application.

[0381] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0382] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0383] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0384] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the contributing part, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0385] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A vehicle control method, characterized in that, The method includes: Obtain map information, including information about environmental elements of the toll station; Acquire first sensing information from at least one sensing device of the vehicle, the first sensing information including detection data of the toll station; Based on the map information and / or the first sensing information, the traffic attribute information of at least one lane of the toll station is obtained, and the traffic attribute information is used to indicate the traffic conditions of the lane.

2. The method according to claim 1, characterized in that, The traffic attribute information includes one or more of the following: lane toll type, lane passability status, lane vehicle type, lane height limit, lane width limit, lane speed limit, and lane vehicle queuing information.

3. The method according to claim 2, characterized in that, The toll types for the lanes include any of the following: Electronic Toll Collection (ETC) lanes, manual lanes, and a hybrid ETC and manual lane; The passability status of the lanes includes both impassable lanes and passable lanes.

4. The method according to any one of claims 1 to 3, characterized in that, The at least one sensing device includes at least one camera and / or at least one ranging device.

5. The method according to claim 4, characterized in that, The image data provided by the at least one camera includes one or more of the following information: the toll type of at least one lane of the toll station, the type of vehicles passing through the lane, the height limit of the lane, the width limit of the lane, the speed limit of the lane, the vehicle queuing information of the lane, the size information of the vehicle in front of the vehicle, and the vehicle type of the vehicle in front of the vehicle.

6. The method according to claim 5, characterized in that, The size information of the vehicle ahead of the vehicle and / or the vehicle type of the vehicle ahead of the vehicle are used to indicate the possibility of the vehicle's passage.

7. The method according to claim 4 or 5, characterized in that, The first sensing information also includes traffic flow information, which comes from point cloud information collected by at least one ranging device and / or image data collected by at least one camera. The traffic flow information includes traffic flow information between the vehicle and the toll station.

8. The method according to any one of claims 1 to 7, characterized in that, The map information includes at least one of the following: information from the vehicle-mounted navigation map and map information from a remote server.

9. The method according to any one of claims 1 to 8, characterized in that, The step of obtaining the traffic attribute information of at least one lane of the toll station based on the map information and / or the first sensing information includes: The map information and / or the first perception information are input into at least one artificial intelligence (AI) model to obtain the traffic attribute information of the at least one lane.

10. The method according to claim 9, characterized in that, The AI ​​model includes a trained deep learning model.

11. The method according to any one of claims 1 to 8, characterized in that, The at least one sensing device includes at least one camera and at least one ranging device; The step of obtaining the traffic attribute information of at least one lane of the toll station based on the map information and / or the first sensing information includes: If the distance between the toll station and the vehicle is less than a first threshold and greater than a second threshold, traffic attribute information for at least one lane of the toll station is obtained based on the map information and image data from the at least one camera; or If the distance between the toll station and the vehicle is less than the second threshold, based on the map information, image data from the at least one camera, and point cloud data from the at least one ranging device, the traffic attribute information of at least one lane of the toll station is obtained, or When the distance between the toll station and the vehicle is less than the second threshold, the traffic attribute information of at least one lane of the toll station is obtained based on image data from the at least one camera and point cloud data from the at least one ranging device.

12. The method according to claim 11, characterized in that, The first threshold is related to one or more of the camera's detection range, the camera's image quality, weather conditions, and vehicle speed.

13. The method according to claim 11 or 12, characterized in that, The second threshold is related to one or more of the following: the detection range of the ranging device, the detection accuracy, the weather conditions, and the vehicle speed.

14. The method according to any one of claims 11 to 13, characterized in that, The step of obtaining traffic attribute information for at least one lane of the toll station based on the map information and image data from the at least one camera includes: Input the map information and image data from the at least one camera into the AI ​​model to obtain the traffic attribute information of at least one lane of the toll station; or The distance between the toll station and the vehicle is determined based on the map information. The camera depth of the at least one camera is determined based on the distance between the toll station and the vehicle. The traffic attribute information of at least one lane of the toll station is determined based on the camera depth of the at least one camera and the image data of the at least one camera.

15. The method according to any one of claims 11 to 14, characterized in that, The step of obtaining traffic attribute information for at least one lane of the toll station based on the map information, image data from the at least one camera, and point cloud data from the at least one ranging device includes: The map information, image data from the at least one camera, and point cloud data from the at least one ranging device are input into the AI ​​model to obtain the traffic attribute information of at least one lane of the toll station; or The image data from the at least one camera and the point cloud data from the at least one ranging device are used to perform three-dimensional reconstruction to obtain a first three-dimensional reconstruction result; the first three-dimensional reconstruction result is fused with the map information to obtain a fusion result; at least one image detection result is determined based on the image data from the at least one camera; the fusion result is matched with the at least one image detection result to obtain at least one first matching result; multi-camera fusion processing is performed based on the at least one first matching result to obtain the traffic attribute information of the at least one lane.

16. The method according to any one of claims 11 to 15, characterized in that, The step of obtaining traffic attribute information for at least one lane of the toll station based on image data from the at least one camera and point cloud data from the at least one ranging device includes: Image data from the at least one camera and point cloud data from the at least one ranging device are input into the AI ​​model to obtain traffic attribute information for at least one lane of the toll station; or The image data from the at least one camera and the point cloud data from the at least one ranging device are used to perform three-dimensional reconstruction to obtain a second three-dimensional reconstruction result; at least one image detection result is determined based on the image data from the at least one camera; the second three-dimensional reconstruction result is matched with the at least one image detection result to obtain at least one second matching result; multi-camera fusion processing is performed based on the at least one second matching result to obtain the traffic attribute information of the at least one lane.

17. The method according to any one of claims 1 to 16, characterized in that, The method further includes: Output a first prompt message, which is used to indicate the traffic attribute information of the at least one lane.

18. The method according to claim 17, characterized in that, The access attribute information can be displayed in multiple display modes, including a three-dimensional element mode. The output of the first prompt information includes: The first interface displays a three-dimensional element mode, which prompts the traffic attribute information of the at least one lane in a three-dimensional manner.

19. The method according to claim 18, characterized in that, The method further includes: In response to a mode switching operation input by the user on the first interface, the display mode is switched.

20. The method according to any one of claims 1 to 19, characterized in that, The method further includes: At least one recommended lane is determined based on the traffic attribute information of the at least one lane and the traffic constraints of the vehicle, wherein the recommended lane is a lane that satisfies the traffic constraints of the vehicle. The display device outputs a second prompt message, which is used to indicate the location of the at least one recommended lane and / or the recommended value for each recommended lane.

21. The method according to claim 19, characterized in that, The method further includes: If the lane in which the vehicle is currently located does not meet the vehicle's traffic constraints, a third prompt message is output, which indicates that the current lane does not meet the vehicle's traffic constraints; or, If the lane in which the vehicle is currently located meets the vehicle's traffic constraints, a fourth prompt message is output, which is used to indicate that the current lane meets the vehicle's traffic constraints.

22. The method according to any one of claims 1 to 21, characterized in that, The method further includes: Based on the traffic attribute information of at least one lane of the toll station, a first path is planned for the vehicle to pass through the toll station, and the first path passes through the first lane of the at least one lane; Navigate the vehicle according to the first path.

23. The method according to claim 22, characterized in that, The step of planning a first path for the vehicle to pass through the toll station based on the traffic attribute information of at least one lane of the toll station includes: The first lane is determined based on the traffic attribute information of at least one lane of the toll station; The vehicle is planned to take a first path through the toll station based on the first lane.

24. The method according to claim 22 or 23, characterized in that, The planned first path for the vehicle to pass through the toll station includes: The first lane is determined based on the traffic attribute information of the at least one lane and the traffic constraints of the vehicle, and the first lane satisfies the traffic constraints of the vehicle.

25. The method according to claim 22 or 23, characterized in that, The planned first route for the vehicle to pass through the toll station includes: At least one recommended lane is determined based on the traffic attribute information of the at least one lane and the traffic constraints of the vehicle, and each of the recommended lanes satisfies the traffic constraints of the vehicle. In response to a selection instruction from the user, the first lane is determined from the at least one recommended lane.

26. The method according to claim 22 or 23, characterized in that, The planned first route for the vehicle to pass through the toll station includes: At least one recommended lane and a recommended value for each recommended lane are determined based on the traffic attribute information of the at least one lane and the traffic constraints of the vehicle, wherein each recommended lane satisfies the traffic constraints of the vehicle. The lane with the highest recommendation value among the at least one recommended lane is determined as the first lane.

27. The method according to any one of claims 20, 21, 24 to 26, characterized in that, The vehicle's traffic constraints include at least one of the vehicle's type, height, width, and pre-set toll selection strategy.

28. The method according to claim 27, characterized in that, The pre-set toll selection strategies include: ETC priority, manual priority, or automatic mode.

29. The method according to any one of claims 1 to 28, characterized in that, The method further includes: If the distance between the toll station and the vehicle is less than a third threshold, the gate status of the lane where the vehicle is located is detected based on the first perception information. Output the gate status of the lane where the vehicle is located.

30. The method according to any one of claims 1 to 29, characterized in that, The method further includes: If the distance between the toll station and the vehicle is less than a fourth threshold, second sensing information from the at least one sensing device is acquired. The passage attribute information of the vehicle's lane is updated based on the obstacle information detected by the second perception information.

31. A vehicle control device, characterized in that, Includes units for performing the method as described in any one of claims 1 to 30.

32. A vehicle control device, characterized in that, Includes a processor for performing the method as described in any one of claims 1 to 30.

33. A chip, characterized in that, The chip includes logic circuitry and an interface, wherein the logic circuitry and the interface are coupled. The interface is used for inputting and / or outputting information, and the logic circuit is used for performing the method as described in any one of claims 1 to 30.

34. A vehicle, characterized in that, This includes the vehicle control device as described in claim 31, or the vehicle control device as described in claim 32, or the chip as described in claim 33.

35. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the method described in any one of claims 1 to 30 to be performed.

36. A computer program product, characterized in that, The computer program product includes a computer program, which, when executed, performs the method as described in any one of claims 1 to 30.

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