Intelligent driving method and apparatus, and vehicle

Abstract

Provided in the present application are an intelligent driving method and apparatus, and a vehicle, which are applied to the field of intelligent driving. The method comprises: detecting a first obstacle, wherein the first obstacle is a dynamic obstacle; on the basis of first information of the first obstacle and first traveling information of a vehicle, determining a first risk level, wherein the first risk level is used for representing the possibility of the first obstacle colliding with the vehicle under the first information and the first traveling information; acquiring a first cruise speed matching the first risk level, wherein the first cruise speed is the lowest traveling speed at which the vehicle avoids a collision with the first obstacle under the first risk level; and controlling the vehicle to travel at the first cruise speed. By means of the present application, the flexibility of a vehicle avoiding a dynamic obstacle in an autonomous driving cruise state can be improved, the safety of the vehicle is guaranteed, and the traveling efficiency, passage efficiency and assertive-merging capability of the vehicle are also improved, thereby improving the driving and riding experience.

Description

Intelligent driving method, device and vehicle

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on March 27, 2024, with application number 202410366424.3 and application name “Intelligent Driving Method, Device and Vehicle”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of intelligent driving, and in particular to an intelligent driving method, device and vehicle. Background Art

[0003] When a vehicle is in autonomous cruising mode and detects a dynamic obstacle ahead (such as a pedestrian or moving vehicle), it typically adopts a more conservative approach, such as emergency braking and proactively avoiding the dynamic obstacle to reduce the possibility of a collision. However, this approach lacks flexibility, resulting in a poor driving experience.

[0004] Therefore, how to improve the vehicle's flexibility in avoiding dynamic obstacles in the autonomous driving cruise state and enhance the driving experience has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The present application provides an intelligent driving method, device and vehicle, which can improve the flexibility of the vehicle in avoiding dynamic obstacles in the automatic driving cruise state, improve the vehicle's driving efficiency and traffic efficiency, and enhance the driving experience.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] In a first aspect, the present application provides an intelligent driving method, the method comprising: detecting a first obstacle; the first obstacle being a dynamic obstacle; determining a first risk level based on first information of the first obstacle and first driving information of a vehicle; the first risk level being used to indicate the likelihood of a collision between the first obstacle and the vehicle under the first information and the first driving information; obtaining a first cruising speed that matches the first risk level, the first cruising speed being the minimum driving speed of the vehicle at the first risk level to avoid a collision with the first obstacle; and controlling the vehicle to travel at the first cruising speed.

[0008] In this application, a matching cruising speed is determined based on the risk between the vehicle and the obstacle, and the vehicle's driving speed is planned based on the cruising speed to flexibly avoid obstacles. This improves the vehicle's flexibility in avoiding dynamic obstacles in the autonomous cruising state, while ensuring vehicle safety, enhancing the driving experience, and improving the vehicle's driving efficiency, traffic efficiency, and ability to overtake, allowing the vehicle to better adapt to complex traffic environments.

[0009] In some embodiments, the vehicle is in automatic driving mode or intelligent driving mode, and the vehicle is in a cruising state.

[0010] In some embodiments, the first obstacle is a dynamic obstacle around the vehicle that may cause a collision risk with the vehicle.

[0011] In some embodiments, when a vehicle detects an obstacle around the vehicle, it also obtains information about the obstacle. Based on the information about each obstacle, the vehicle analyzes whether there is a risk of collision between the obstacle and the vehicle, and whether the obstacle is a dynamic obstacle. Dynamic obstacles that pose a collision risk are identified as first obstacles.

[0012] In this application, by screening the obstacles around the vehicle to determine the first obstacle, potential collision threats can be identified more accurately, so as to subsequently evaluate the risk level of the obstacle, efficiently adjust the vehicle speed planning according to the risk level, reduce unnecessary braking or avoidance operations, thereby improving the driving experience and increasing vehicle traffic rate and driving efficiency.

[0013] According to the first aspect, or any implementation of the first aspect above, the method also includes: determining a second risk level based on second information of the first obstacle and second driving information of the vehicle; the second risk level is higher than the first risk level, and the second risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the second information and the second driving information; obtaining a second cruising speed that matches the second risk level; the second cruising speed is lower than the first cruising speed, and the second cruising speed is the minimum driving speed of the vehicle to avoid a collision with the first obstacle under the second risk level; and controlling the vehicle to travel at the second cruising speed.

[0014] According to the first aspect, or any implementation of the first aspect above, the method also includes: determining a third risk level based on the third information of the first obstacle and the third driving information of the vehicle; the third risk level is lower than the first risk level, and the third risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the third information and the third driving information; obtaining a third cruising speed that matches the third risk level; the third cruising speed is higher than the first cruising speed, and the third cruising speed is the minimum driving speed of the vehicle to avoid a collision with the first obstacle under the first risk level; and controlling the vehicle to travel at the third cruising speed.

[0015] In this application, the vehicle detects information about a first obstacle and its driving information in real time, determines the collision risk corresponding to each moment based on the obstacle and vehicle information at that moment, and dynamically changes the cruise speed based on the collision risk at that moment. This allows the vehicle speed planned based on the changed cruise speed to better adapt to changes in the vehicle's surrounding environment, helping to better ensure vehicle safety. By dynamically adjusting the cruise speed based on this information at each moment, the vehicle improves its flexibility in avoiding dynamic obstacles while in autonomous cruising mode, allowing the vehicle to better adapt to complex traffic environments.

[0016] According to the first aspect, or any implementation method of the first aspect above, a first risk level is determined based on the first information of the first obstacle and the first driving information of the vehicle, including: predicting risk information based on the first information of the first obstacle and the first driving information of the vehicle; the risk information includes one or more of the following: the first collision point, the collision time, the distance between the current position of the vehicle and the first collision point, and the current distance between the first obstacle and the first collision point; the first collision point is the intersection of the future motion trajectory of the first obstacle and the future motion trajectory of the vehicle predicted based on the first information and the first driving information; and evaluating the lateral and longitudinal risks based on the risk information to determine the first risk level.

[0017] In some embodiments, the vehicle processes the first information about the first obstacle and the first driving information of the vehicle according to a predefined algorithm or model to determine risk information. The vehicle then assesses the lateral direction (also described as lateral risk) and the longitudinal direction (also described as longitudinal risk) based on the risk information, and determines a risk level based on the lateral risk and the longitudinal risk.

[0018] The predefined algorithms include, but are not limited to, motion prediction algorithms, trajectory generation algorithms, collision detection algorithms, etc. The predefined models may be trained machine learning models or deep learning models (neural network models), etc. For example, the neural network model is a convolutional neural network.

[0019] In some embodiments, the vehicle makes a prediction based on the first information of the first obstacle and the first driving information of the vehicle to obtain prediction information, and then performs short-term deduction and collision detection based on the prediction information to determine risk information.

[0020] In some embodiments, the vehicle processes risk information based on a longitudinal risk assessment algorithm or model and a lateral risk assessment algorithm or model to determine longitudinal and lateral risks. The vehicle comprehensively analyzes the longitudinal and lateral risks, considering the combined impact of different risks on driving, and determines the risk level.

[0021] In some embodiments, a mapping table between risk information and risk levels is pre-configured in the vehicle, and the vehicle determines the risk level corresponding to the current risk information based on the mapping table.

[0022] In this application, the vehicle comprehensively considers obstacle information and vehicle information to accurately determine the risk level of collision between the obstacle and the vehicle, so that the vehicle can make more reasonable decisions based on the risk level, improve the vehicle's traffic efficiency, and enhance driving safety.

[0023] In some embodiments, a mapping table between risk levels and cruising speeds is pre-configured in the vehicle. The vehicle can determine a first cruising speed that matches the first risk level based on the mapping relationship.

[0024] In other embodiments, the vehicle calculates a first cruising speed corresponding to the first risk level through a predefined algorithm or model based on the first risk level, the first information, and the first driving information.

[0025] According to the first aspect, or any implementation of the first aspect above, controlling a vehicle to travel at a first cruising speed includes: generating a speed planning curve based on the current speed of the vehicle and the first cruising speed; adjusting the vehicle's travel speed to the first cruising speed according to the speed planning curve, and controlling the vehicle to travel at the first cruising speed.

[0026] In this application, a speed planning curve is generated based on the first cruising speed related to the current vehicle state and obstacle information to guide the vehicle's driving speed, optimize the vehicle's driving efficiency, make the vehicle speed more adapted to the surrounding environment, and improve driving safety and comfort.

[0027] According to the first aspect, or any implementation of the first aspect above, controlling the vehicle to travel at a first cruising speed includes: if the distance between the current vehicle position and the first collision point is greater than a first threshold, controlling the vehicle to travel at the first cruising speed; the first collision point is the intersection of the future motion trajectory of the first obstacle and the future motion trajectory of the vehicle predicted based on the first information and the first driving information, and the first threshold is the minimum safe distance between the vehicle and the first obstacle.

[0028] According to the first aspect, or any implementation of the first aspect above, there is a second obstacle on the future motion trajectory of the vehicle, the second obstacle is a static obstacle, and the distance between the current position of the vehicle and the second obstacle is greater than the distance between the current position of the vehicle and the first collision point. Controlling the vehicle to travel at a first cruising speed includes: if the distance between the current position of the vehicle and the second obstacle is greater than a second threshold, controlling the vehicle to travel at the first cruising speed; the second threshold is the minimum safe distance between the vehicle and the second obstacle.

[0029] In this application, before the vehicle adopts the first cruising speed for speed planning, it is determined whether there is enough space around the vehicle and whether the safety guarantee distance is met, which helps to prevent potential collision risks and improve driving safety.

[0030] According to the first aspect, or any implementation of the first aspect above, the method also includes: determining a fourth risk level based on the fourth information of the first obstacle and the fourth driving information of the vehicle; the fourth risk level is higher than the first risk level, and the fourth risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the fourth information and the fourth driving information; obtaining a fourth cruising speed that matches the fourth risk level; the fourth cruising speed is lower than the first cruising speed, and the fourth cruising speed is the minimum driving speed of the vehicle to avoid a collision with the first obstacle under the fourth risk level; if the distance between the current vehicle position and the first collision point is less than or equal to a first threshold, the vehicle's driving speed is reset to zero; the first collision point is the intersection of the future motion trajectory of the first obstacle and the future motion trajectory of the vehicle predicted based on the first information and the first driving information, and the first threshold is the minimum safe distance between the vehicle and the first obstacle.

[0031] According to the first aspect, or any implementation of the first aspect above, there is a second obstacle on the future motion trajectory of the vehicle, and the second obstacle is a static obstacle. The distance between the current vehicle position and the second obstacle is greater than the distance between the current vehicle position and the first collision point. The method also includes: determining a fifth risk level based on fifth information of the first obstacle and fifth driving information of the vehicle; the fifth risk level is higher than the first risk level, and the fifth risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the fifth information and the fifth driving information; obtaining a fifth cruising speed that matches the fifth risk level; the fifth cruising speed is lower than the first cruising speed, and the fifth cruising speed is the minimum driving speed of the vehicle to avoid a collision with the first obstacle under the fifth risk level; if the distance between the current vehicle position and the second obstacle is less than or equal to a second threshold, resetting the vehicle's driving speed to zero; the second threshold is the minimum safe distance between the vehicle and the second obstacle.

[0032] In this application, when the vehicle is cruising to avoid obstacles, it determines whether the safety distance is met based on the real-time distance information. When the real-time distance is less than the safety distance, it exits the cruise smoothly to ensure the safety of the vehicle and the comfort of the user.

[0033] In a second aspect, the present application provides an intelligent driving device, which includes: a processor and a memory, the memory being coupled to the processor, the memory being used to store computer-readable instructions, and when the processor reads the computer-readable instructions from the memory, the intelligent driving device executes the method as in the first aspect and any one of the embodiments of the first aspect.

[0034] In a third aspect, the present application provides a vehicle comprising the intelligent driving device as described in the second aspect.

[0035] Exemplary vehicles include cars, trucks, motorcycles, buses, lawn mowers, recreational vehicles, amusement park vehicles, construction equipment, trams, golf carts, trains, etc., which are not particularly limited in this application. The power of the above-mentioned vehicles can be provided by gasoline, diesel, electricity, solar energy, or hydrogen energy.

[0036] In a fourth aspect, the present application provides a chip system comprising at least one processor and at least one interface circuit, wherein the at least one interface circuit is used to perform transceiver functions, and the at least one processor is used to execute the method of the first aspect and any one of the embodiments of the first aspect.

[0037] In a fifth aspect, the present application provides a computer-readable storage medium, which includes a computer program. When the computer program is executed by a processor, it implements the method of the first aspect and any one of the embodiments of the first aspect.

[0038] In a sixth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, implements the method of the first aspect and any one of the embodiments of the first aspect.

[0039] The technical effects corresponding to the second to sixth aspects and any implementation method of each aspect can be referred to the technical effects corresponding to the above-mentioned first aspect and any implementation method of the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] FIG1 is a schematic diagram of a vehicle driving scenario provided by an embodiment of the present application;

[0041] FIG2 is a schematic structural diagram of a vehicle provided in an embodiment of the present application;

[0042] FIG3 is a schematic diagram of the structure of a mobile smart device provided in an embodiment of the present application;

[0043] FIG4 is a schematic diagram of the architecture of the intelligent driving system provided in an embodiment of the present application;

[0044] FIG5 is a second schematic diagram of the architecture of the intelligent driving system provided in an embodiment of the present application;

[0045] FIG6 is a schematic diagram of a flow chart of an intelligent driving method provided in an embodiment of the present application;

[0046] FIG7 is a first schematic diagram of an intelligent driving scenario provided by an embodiment of the present application;

[0047] FIG8 is a second schematic diagram of an intelligent driving scenario provided by an embodiment of the present application;

[0048] FIG9 is a third schematic diagram of an intelligent driving scenario provided in an embodiment of the present application;

[0049] FIG10 is a fourth schematic diagram of an intelligent driving scenario provided by an embodiment of the present application;

[0050] FIG11 is a schematic diagram of the structure of an intelligent driving device provided in an embodiment of the present application;

[0051] FIG12 is a schematic structural diagram of a chip system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in this article is merely a way to describe the association relationship of associated objects, indicating that three relationships can exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0053] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the quantity of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features.

[0054] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more. In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0055] In some examples, a cruising vehicle's handling strategy for dynamic obstacles involves actively braking to avoid potential collisions and ensure vehicle safety. This strategy only considers the vehicle's behavior (such as the distance from the current vehicle to the predicted collision point) and does not account for interactions between the vehicle and the dynamic obstacle (such as the dynamic obstacle's proactive yielding). This results in low vehicle traffic efficiency and weak overtaking capabilities.

[0056] For example, as shown in Figure 1, vehicle 11 is in autonomous cruising mode and moving forward. Vehicle 12 (a dynamic obstacle) is moving to the left. Vehicle 11 determines collision point 13 based on information obtained from both vehicle 11 (such as speed and position) and vehicle 12 (such as speed and position). Vehicle 11 detects a potential collision with vehicle 12, actively brakes, and waits for vehicle 12 to pass before restarting. Vehicle 11 only considers its own driving safety and actively avoids vehicle 12, without considering vehicle 12's handling method. If vehicle 12 also actively brakes, vehicle 11 needs to reassess the situation after braking. Vehicle 11's handling method for avoiding vehicle 12 (a dynamic obstacle) is conservative, rigid, and inflexible. Vehicle 11 brakes as soon as it detects a dynamic obstacle, resulting in a poor driving experience. Moreover, vehicle 11's abrupt braking causes the braking point to be far from the collision point, resulting in low traffic efficiency and weak overtaking ability.

[0057] In order to solve the technical problems mentioned above, an embodiment of the present application provides an intelligent driving method, which includes: detecting a first obstacle; determining a first risk level based on first information of the first obstacle and first driving information of the vehicle; the first risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the first information and the first driving information; obtaining a first cruising speed that matches the first risk level, the first cruising speed being the minimum driving speed for the vehicle to avoid a collision with the first obstacle under the first risk level; and controlling the vehicle to travel at the first cruising speed. The method provided in the embodiment of the present application determines a matching cruising speed based on the risk between the vehicle and the obstacle, controls the vehicle at the cruising speed, optimizes the intelligent driving strategy, improves the flexibility of the vehicle in avoiding dynamic obstacles in the automatic driving cruise state, and while ensuring vehicle safety, enhances the driving experience, improves the vehicle's driving efficiency, traffic efficiency, and overtaking ability, so that the vehicle can better adapt to complex traffic environments.

[0058] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0059] The intelligent driving method in the embodiments of this application can be applied to any obstacle avoidance scenario. For example, obstacle avoidance scenarios include robot obstacle avoidance scenarios, vehicle obstacle avoidance scenarios (such as vehicle obstacle avoidance in autonomous driving scenarios), and autonomous mobile device obstacle avoidance scenarios (such as drone obstacle avoidance, etc.).

[0060] The technical solutions provided in the embodiments of the present application can be applied to various mobile smart devices. For example, the mobile smart device may include but is not limited to vehicles, artificial intelligence (AI) devices (such as robots), etc. Or it can be applied to other devices (such as servers, mobile terminals, etc.) that have the function of controlling the aforementioned mobile smart devices. The mobile smart device or other device can implement the intelligent driving method provided in the embodiments of the present application through the components (including hardware and software) it contains.

[0061] Taking the mobile smart device as a vehicle as an example, FIG2 is a structural diagram of a vehicle 200 provided in an embodiment of the present application.

[0062] In the embodiment of the present application, vehicle 200 may include various subsystems, including but not limited to intelligent driving system 210. Optionally, vehicle 200 may include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and component of vehicle 200 may be connected via wired or wireless communication technology.

[0063] Intelligent driving system 210 can obtain first obstacle information (including but not limited to the location of the first obstacle) and driving information of vehicle 200 (including but not limited to various vehicle information such as driving speed and acceleration). Intelligent driving system 210 can also determine a risk level based on this information, obtain a cruising speed that matches the risk level, and control vehicle 200 to travel at the cruising speed.

[0064] Optionally, the intelligent driving system 210 may include, but is not limited to, one or more of an advanced driver system (ADS), an advanced driver assistance system (ADAS), etc. Alternatively, as future driving technologies evolve, the intelligent driving system 210 may also be a driving system of other levels.

[0065] The above-mentioned vehicle 200 can be a new energy vehicle, an electric vehicle, an intelligent vehicle, a car, a truck, a motorcycle, a bus, a boat, a lawn mower, an entertainment vehicle, an amusement park vehicle, construction equipment, a tram, a golf cart, and a train, etc., and the embodiments of the present application do not make any special limitations.

[0066] For example, the above only uses a vehicle as an example to illustrate the structure of the mobile smart device in the embodiment of the present application, but does not constitute a limitation on the structure and form of the mobile smart device.

[0067] FIG3 is a schematic diagram of the structure of another mobile smart device provided in an embodiment of the present application. For example, the mobile smart device is an intelligent robot. The mobile smart device includes at least one processor 301, a communication circuit 302, a memory 303, and at least one communication interface 304.

[0068] The processor 301 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present application.

[0069] Communication link 302 may include a path or bus to transmit information between the aforementioned components.

[0070] Communication interface 304 is used to communicate with other devices. In the embodiments of the present application, communication interface 304 can be a module, circuit, bus, interface, transceiver, or other device capable of implementing communication functions. Optionally, when the communication interface is a transceiver, the transceiver can be a standalone transmitter that can be used to send information to other devices, or a standalone receiver that can be used to receive information from other devices. The transceiver can also be a component that integrates the functions of sending and receiving information.

[0071] Memory 303 can be a read-only memory (ROM), random access memory (RAM), or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer. Memory 303 can be independent and connected to processor 301 via communication line 302. Memory 303 can also be integrated with processor 301.

[0072] The memory 303 is used to store computer-executable instructions for implementing the solution of the present application. The processor 301 is used to execute the computer-executable instructions stored in the memory 303, thereby implementing the methods provided in the following embodiments of the present application.

[0073] In other embodiments of the present application, the mobile smart device may include more or fewer components than those shown in Figures 2 and 3, or may combine or separate certain components, replace certain components, or arrange the components differently. The components shown in the figures may be implemented in hardware, software, or a combination of software and hardware.

[0074] Taking the mobile smart device as a vehicle and the vehicle in an autonomous driving scenario as an example, the intelligent driving method of this application is explained.

[0075] Referring to Figure 4, a schematic diagram of the system architecture of an intelligent driving system provided in an embodiment of the present application is shown. The intelligent driving system includes a perception system, a planning system, and a control system. The perception system, the planning system, and the control system are communicatively connected.

[0076] In an embodiment of the present application, the perception system is used to detect a first obstacle and obtain information about the first obstacle (such as the current position, velocity, acceleration, shape, etc. of the first obstacle). For example, the first obstacle includes a dynamic obstacle, which includes a walking pedestrian or a moving vehicle. The perception system is also used to obtain driving information of the vehicle. The perception system is also used to perceive surrounding environmental information, such as road information and traffic signs. The perception system is also used to transmit the various types of information obtained to the planning system.

[0077] In the embodiment of the present application, the planning system and the control system are used to execute the intelligent driving method provided by the present application. The planning system includes a prediction module, a decision module, and a planning module.

[0078] In some embodiments, the prediction module is configured to make predictions based on various types of information sent by the perception system and determine prediction information. Specifically, the prediction module is configured to determine the prediction information based on information about the first obstacle and driving information of the vehicle; the prediction module is further configured to send the prediction information to the decision module.

[0079] The prediction information includes the prediction information of obstacles (such as the future speed, future position, future motion trajectory, etc. of the obstacle) and the prediction information of the vehicle (such as the future speed, future position, future motion trajectory, etc. of the vehicle).

[0080] For example, if the first obstacle is a dynamic obstacle, the future motion trajectory of the dynamic obstacle, the future position of the dynamic obstacle, the future speed of the dynamic obstacle, and other prediction information are predicted based on the information of the dynamic obstacle (such as position, speed, acceleration, motion direction, shape, etc.). The future motion trajectory of the vehicle, the future position of the vehicle, the future speed of the vehicle, and other prediction information are predicted based on the driving information of the vehicle.

[0081] In some embodiments, the decision module is configured to perform calculations based on the prediction information sent by the prediction module to determine risk information. The decision module is further configured to integrate various risk information to assess horizontal and vertical risks and determine a risk level. The decision module is further configured to determine a corresponding control decision based on the risk level. The decision module is further configured to transmit the risk level to the planning module; alternatively, the decision module transmits the risk information, risk level, and control decision to the planning module.

[0082] The risk information includes but is not limited to the collision point, the collision time, the distance from the vehicle to the collision point, and the distance from the first obstacle to the collision point.

[0083] For example, the first obstacle is a dynamic obstacle, and the collision point is the intersection of the future motion trajectory of the dynamic obstacle and the future motion trajectory of the vehicle.

[0084] Optionally, risk information may also include environmental information, such as weather, road conditions, and traffic conditions. It should be understood that when determining risk information, the vehicle should comprehensively consider the vehicle's surroundings to assist in making a more accurate risk assessment and enabling the vehicle to adopt a more appropriate cruising speed.

[0085] Among them, lateral and longitudinal risks include lateral risks and longitudinal risks. Lateral risks are risks in the lateral direction faced by the vehicle during driving, that is, risks in the left and right directions of the vehicle, including the possibility of avoiding obstacles or colliding with obstacles. For example, the risk of collision caused by other vehicles changing lanes, overtaking, merging, etc., as well as the threats that lateral obstacles from different sources such as roadside obstacles, pedestrians, and animals may pose to vehicle driving. Longitudinal risks are risks in the longitudinal direction faced by the vehicle during driving, that is, risks in the front and rear directions of the vehicle, including the possibility of slowing down and braking or accelerating to avoid collisions. For example, the risk of rear-end collision caused by insufficient following safety distance from the vehicle in front, and the situation where the vehicle needs to brake urgently or change lanes to avoid sudden braking, deceleration or obstacles in front.

[0086] The risk level is used to indicate the probability of collision between the first obstacle and the vehicle, for example, low risk, medium risk, and high risk.

[0087] Control decisions include yielding and overtaking. A yielding decision involves slowing down to avoid an obstacle, while an overtaking decision involves accelerating to overtake. For example, when a vehicle faces a dynamic obstacle with a potential collision risk, it typically slows down to yield to the obstacle and then continues driving after the collision risk is resolved. Alternatively, if the vehicle is closer to the collision point and the collision risk is low, it may choose to accelerate and overtake the obstacle.

[0088] Specifically, the decision module calculates and deduces risk information based on the predicted information. Based on this risk information, the decision module assesses both horizontal and vertical risks. Combining these assessments, the decision module determines the risk level. Based on predefined rules, the decision module then determines the control strategy corresponding to the risk level.

[0089] For example, the control strategy corresponding to low risk is the rush-to-pass strategy, and the control strategy corresponding to medium risk and high risk is the yield strategy.

[0090] In some embodiments, the planning module is used to perform speed planning based on the various information sent by the decision module and generate a speed planning result (such as cruising speed). The planning module is also used to send the speed planning result (such as cruising speed) to the control system.

[0091] Specifically, the planning module determines a cruising speed that matches the risk level based on the risk information. Alternatively, the planning module determines a cruising speed that matches the risk level based on the control strategy, risk information, and risk level. The planning module can also plan the vehicle's driving speed based on the cruising speed and generate a speed planning curve.

[0092] Cruising speed can be understood as the minimum speed to avoid collision with a dynamic obstacle, calculated based on current vehicle information, obstacle information, and collision risk, when the vehicle detects a dynamic obstacle and there is a risk of collision with the vehicle. Cruising speed can be understood as the minimum critical speed that the vehicle should maintain based on the current scenario. The vehicle speed should smoothly change from the current speed to the cruising speed, maintaining steady forward movement at the cruising speed. As can be understood, since the current scenario is an obstacle avoidance scenario, if the control strategy is determined to be a yield strategy, the cruising speed will be a lower value, and the vehicle will be instructed to decelerate based on this cruising speed. Ultimately, the vehicle may move forward slowly, dynamically adjusting the cruising speed based on real-time risk detection until the obstacle passes the collision point first, eliminating the collision risk. If the control strategy is determined to be a preemptive strategy, the cruising speed will be a higher value, and the vehicle will be instructed to accelerate based on this cruising speed, increasing its speed to quickly pass the collision point and eliminating the collision risk.

[0093] In an embodiment of the present application, the control system controls the vehicle to travel at the speed planning result (such as cruising speed) according to the speed planning result (such as cruising speed) sent by the planning module, thereby realizing intelligent driving of the vehicle and successfully avoiding obstacles.

[0094] For example, referring to FIG5 , FIG5 shows a schematic diagram of the architecture of another intelligent driving system 50 provided in an embodiment of the present application. As shown in FIG5 , the intelligent driving system 50 may include a sensing device 51 and an intelligent driving device 52 . The sensing device 51 and the intelligent driving device 52 are communicatively connected.

[0095] In an embodiment of the present application, the sensing device 51 is used to detect a first obstacle. The sensing device 51 is also used to sense information about the first obstacle and driving information of the vehicle. The sensing device 51 is also used to send the information about the first obstacle and driving information of the vehicle to the intelligent driving device 52.

[0096] Optionally, the sensing device 51 includes a vehicle sensor, which is typically located inside the vehicle. The vehicle sensor can be used to sense information about the first obstacle around the vehicle and the vehicle's driving information. The vehicle sensor can be an accelerometer, a gyroscope, a wheel speed sensor, an air pressure sensor, an ultrasonic sensor, a camera sensor, a positioning sensor, a radar sensor, or the like.

[0097] It can be understood that since the vehicle sensor is set in the vehicle, when the sensing device 51 is a vehicle sensor, the sensing device 51 moves with the vehicle, and the sensing device 51 and the intelligent driving device 52 can communicate through the wireless communication network.

[0098] It should be understood that the above description of the sensing device 51 is only for illustrative purposes.

[0099] In the embodiment of the present application, the intelligent driving device 52 is used to execute the intelligent driving method provided herein. Specifically, the intelligent driving device 52 can be used to determine the current risk level based on obstacle information and vehicle driving information. The risk level indicates the probability of a collision between the obstacle and the vehicle. The intelligent driving device 52 can also be used to obtain a cruising speed that matches the current risk level. The cruising speed is the minimum speed at which the vehicle can avoid colliding with the obstacle under the current risk level. The intelligent driving device 52 is also used to control the vehicle to travel at the cruising speed.

[0100] Alternatively, intelligent driving device 52 may be an intelligent driving computing platform. This platform implements intelligent driving, decision-making, planning, and control functions and is a core component of the vehicle. The intelligent driving computing platform interacts with various vehicle components, acquiring real-time data from each component and controlling its operation.

[0101] Optionally, the intelligent driving device 52 may be a server. The server may be a Linux server, a Windows server, or other server device that can provide simultaneous access to multiple devices. It may also be a server cluster consisting of multiple regions, multiple computer rooms, and multiple servers. As an example, the intelligent driving device 52 may be a server of an intelligent transportation system, such as a physical server or a cloud server, which is not limited in this embodiment of the present application.

[0102] It is understood that the sensing device 51 and intelligent driving device 52 in the intelligent driving system 50 are independent components that interact with each other to achieve intelligent driving. In actual applications, the intelligent driving system 50 may also include only the intelligent driving device 52, with other components controlled by other systems in the vehicle, and these systems interact to achieve intelligent driving.

[0103] It should be understood that the above description of the intelligent driving system is only an example. The various modules in the above-mentioned intelligent driving system are divided according to functional logic, and other division methods may be used in practice. In addition, the above-mentioned modules can be named by other names. In addition, each module can be implemented by hardware, software, or a combination of hardware and software. Whether a specific module is implemented in hardware, software, or a combination of hardware and software depends on the specific application and design constraints of the technical solution. Different modules can be implemented by different hardware, and multiple modules can also be implemented by the same hardware.

[0104] It can be understood that the system architecture and business scenarios described in this application are intended to more clearly illustrate the technical solutions of this application, and do not constitute the sole limitation on the technical solutions provided by this application. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solutions provided by this application are also applicable to similar technical problems.

[0105] For example, FIG6 shows a flow chart of an intelligent driving method provided by an embodiment of the present application. The execution subject of the method can be, for example, the mobile intelligent device described above, or a processor in the mobile intelligent device. In the embodiment of the present application, the execution subject is a mobile intelligent device, and the mobile intelligent device is a vehicle. As shown in FIG6, the method includes the following steps:

[0106] S601: A vehicle detects a first obstacle, which is a dynamic obstacle.

[0107] Among them, the vehicle is in automatic driving mode or intelligent driving mode, and the vehicle is in cruising state.

[0108] It can be understood that the automatic driving mode or intelligent driving mode is used to perceive the road environment through the on-board sensing system (which can also be described as a perception system), and control the steering and speed of the vehicle based on the road, vehicle information and obstacle information obtained through perception, so that the vehicle can travel safely and reliably on the road and reach the predetermined location.

[0109] According to the standards published by the Society of Automotive Engineers (SAE), autonomous driving is divided into multiple levels, from L0 to L5. Specifically, L0: No Automation, which means the vehicle is fully operated by the human driver without any support from any automated system; L1: Driver Assistance, which means the vehicle provides driving support for one of the following operations: steering and acceleration and deceleration; the rest is operated by the driver; L2: Partial Automation, which means the vehicle provides driving support for several operations: steering and acceleration and deceleration; the rest is operated by the driver (the driver needs to maintain an observation of road conditions and be ready to take over control at any time); L3: Conditional Automation, which means the vehicle can complete most driving operations and achieve autonomous driving under certain conditions. The driver needs to stay attentive in case of emergency; L4: High Automation, which means the vehicle can complete all driving operations in most situations and drive independently. The driver only needs to intervene in rare circumstances, such as extreme weather or complex traffic conditions; L5: Full Automation, which means the vehicle can completely drive autonomously on any road and under all conditions without driver intervention.

[0110] In the embodiments of this application, the autonomous driving mode is applicable to levels L2-L4. The vehicle can assist the driver, controlling acceleration and deceleration to avoid dynamic obstacles. Alternatively, the vehicle can perform most driving operations and avoid obstacles through sensors, computer vision, artificial intelligence, and other technologies. Alternatively, the vehicle can sense its surroundings and independently perform all driving operations and avoid obstacles.

[0111] Cruise control means that after the vehicle activates cruise control mode, the vehicle maintains a predetermined speed and moves forward without the driver having to step on the accelerator or brake to maintain the speed. In this state, the vehicle automatically adjusts the accelerator and brake to maintain a stable speed.

[0112] In some embodiments, the first obstacle may be a dynamic obstacle around the vehicle that could cause a collision risk. A dynamic obstacle is an obstacle that remains in motion, such as a moving vehicle, a running pedestrian, a walking pedestrian, or a running animal. The first obstacle includes, but is not limited to, moving objects, vehicles, pedestrians, and the like.

[0113] In some embodiments, a vehicle detects obstacles around it using various sensors. Specifically, a vehicle can use an onboard camera to capture images of its surroundings and identify obstacles on the road through image processing and computer vision technologies. A vehicle can also detect obstacles by using a radar system to emit radio waves and receive reflected waves to detect objects in the surrounding environment. A vehicle can also use a lidar system to emit laser beams and determine the location and shape of obstacles based on the reflection time of the laser beams. A vehicle can also detect obstacles using ultrasonic sensors.

[0114] In some embodiments, when a vehicle detects an obstacle around it, it also obtains information about the obstacle (such as the obstacle's location, speed, direction, shape, etc.). Based on the information about each obstacle, the vehicle analyzes whether there is a risk of collision between the obstacle and the vehicle, and whether the obstacle is a dynamic obstacle, and determines dynamic obstacles that pose a collision risk as first obstacles.

[0115] In some embodiments, the vehicle may detect its own vehicle status information (such as driving information, etc.) in real time.

[0116] In some embodiments, the vehicle uses various sensors to obtain information about surrounding obstacles (such as the obstacle's location, speed, direction, acceleration, shape, size, and other information), as well as vehicle driving information (such as the vehicle's location, speed, acceleration, and direction). Based on the obstacle information and vehicle driving information, the vehicle predicts the future behavior of the obstacle and the vehicle to determine whether there is a risk of collision. Based on the obstacle information, the vehicle determines whether the obstacle is dynamic.

[0117] For example, based on the information of the obstacle (such as a pedestrian), it is determined to be a dynamic obstacle. The vehicle predicts the future motion trajectory of the vehicle based on the driving information of the vehicle. The vehicle predicts the future motion trajectory of the obstacle based on the information of the obstacle. If the future motion trajectory of the vehicle and the future motion trajectory of the obstacle intersect at the same time point, it is determined that there is a risk of collision with the dynamic obstacle, and the dynamic obstacle is determined to be the first obstacle.

[0118] For example, based on the example in FIG1 above, vehicle 11 obtains information about vehicle 12, and vehicle 12 is traveling to the left at a certain speed. There is a possibility that the future motion trajectory of vehicle 11 and the future motion trajectory of vehicle 12 intersect at the same time point, and vehicle 12 is determined to be the first obstacle.

[0119] For example, as shown in Figure 7, the vehicle turns left. The future trajectory determined by the vehicle's state is shown by the solid line in Figure 7. The vehicle detects a pedestrian walking in front of it on the left, approaching the vehicle. The future trajectory of the pedestrian determined by the detected pedestrian information is shown by the dashed line in Figure 7. If the pedestrian's future trajectory intersects the vehicle's future trajectory, the vehicle will determine that the pedestrian is the first obstacle.

[0120] In other embodiments, the vehicle uses various sensors to obtain information about obstacles around the vehicle (such as the obstacle's location, speed, direction, acceleration, shape, size, etc.), as well as vehicle driving information (such as the vehicle's location, speed, acceleration, direction, etc.). Based on this information, the vehicle determines whether the current scene is a dynamic obstacle interaction scene. If so, the dynamic obstacle in the scene is determined as the first obstacle.

[0121] Among them, the dynamic obstacle interaction scenario can be understood as a scenario in which the vehicle interacts with dynamic obstacles to avoid obstacles in an autonomous driving or intelligent driving scenario.

[0122] Specifically, the vehicle detects surrounding obstacles and first determines whether there is a dynamic obstacle; if so, it determines whether the obstacle poses a risk of collision with the vehicle. If so, it determines that the current scene is a dynamic obstacle interaction scene, and determines the obstacle in the scene as the first obstacle.

[0123] For example, a vehicle traveling straight ahead detects a pedestrian running to the right from its left front. The pedestrian's future trajectory is likely to intersect with the vehicle's, and the distance between the vehicle and pedestrian is decreasing. This scenario is considered a dynamic obstacle interaction scenario, with the pedestrian identified as the primary obstacle. Vehicle-pedestrian interactions can occur in the following ways: the vehicle detects the pedestrian and slows down to yield; the pedestrian sees the vehicle and slows down to yield; or the pedestrian sees the vehicle but the distance between them is too far, causing the pedestrian to speed past.

[0124] In other embodiments, the vehicle also detects the vehicle's surrounding environment information (such as road information, weather information, traffic light information, etc.) in real time. The vehicle screens the obstacles around the vehicle based on the surrounding environment information, obstacle information, and vehicle driving information to determine the first obstacle.

[0125] It should be understood that the above embodiment uses a single dynamic obstacle as an example. In actual applications, multiple dynamic obstacles may exist around the vehicle. When multiple dynamic obstacles exist, the obstacle closest to the vehicle and posing a collision risk is determined as the primary obstacle. Alternatively, the vehicle can treat each dynamic obstacle as a primary obstacle and combine information from multiple primary obstacles for speed planning.

[0126] It is understood that in the embodiment of the present application, obstacles around the vehicle are screened and obstacles with which the vehicle has a collision risk are identified as the first obstacle. This allows the vehicle to more accurately identify potential collision threats, subsequently assess the risk level of the obstacle, efficiently adjust the vehicle's speed based on the risk level, flexibly control the vehicle, reduce unnecessary braking or avoidance maneuvers, improve vehicle traffic flow and driving efficiency, and ultimately achieve intelligent driving and an enhanced driving experience.

[0127] S602: The vehicle determines a first risk level based on the first information about the first obstacle and the first driving information of the vehicle. The first risk level is used to indicate the probability of a collision between the first obstacle and the vehicle based on the first information and the first driving information.

[0128] The first information includes, but is not limited to: the current position of the first obstacle (such as the coordinate information of the first obstacle relative to the vehicle), the current distance of the first obstacle (such as the distance between the first obstacle and the vehicle, so as to evaluate the spatial relationship between the first obstacle and the vehicle), the current speed of the first obstacle (such as determining the movement speed of the first obstacle by continuously detecting the position change of the first obstacle), the current acceleration of the first obstacle, the current direction of the first obstacle, the shape of the first obstacle, the size of the first obstacle, the attributes of the first obstacle (such as vehicle, pedestrian, building, etc.), the current state of the first obstacle (such as stationary, moving, accelerating, decelerating, etc.), etc.

[0129] The first driving information includes but is not limited to: the current position of the vehicle (such as the coordinate information of the vehicle), the current speed of the vehicle, the current acceleration of the vehicle, the current direction of the vehicle, etc.

[0130] In some embodiments, the vehicle predicts risk information based on the first information of the first obstacle and the first driving information of the vehicle, and then evaluates the lateral and longitudinal risks based on the risk information to determine a first risk level.

[0131] Risk information includes, but is not limited to, the first collision point, collision time, the current distance between the vehicle and the first collision point, and the current distance between the first obstacle and the first collision point. For example, the first collision point is the intersection of the future trajectory of the first obstacle predicted based on the first information and the future trajectory of the vehicle predicted based on the first driving information. Risk information may also include information about the obstacle, the vehicle, and information indicating the relative relationship between the obstacle and the vehicle, such as the obstacle's speed, the vehicle's speed, the speed difference between the obstacle and the vehicle, and the distance between the obstacle and the vehicle.

[0132] In some embodiments, the vehicle processes the first information about the first obstacle and the first driving information of the vehicle according to a predefined algorithm or model to determine risk information. The vehicle then assesses the lateral direction (also described as lateral risk) and the longitudinal direction (also described as longitudinal risk) based on the risk information, and determines a risk level based on the lateral risk and the longitudinal risk.

[0133] Among them, lateral risk refers to the risk in the left and right directions of the vehicle, including the possibility of avoiding obstacles or colliding with obstacles. Longitudinal risk refers to the risk in the front and back directions of the vehicle, including the possibility of slowing down and braking or accelerating to avoid collisions.

[0134] The predefined algorithms include, but are not limited to, motion prediction algorithms, trajectory generation algorithms, collision detection algorithms, and the like. For example, the vehicle processes first information about a first obstacle using a trajectory generation algorithm to determine a future motion trajectory of the first obstacle. The vehicle processes first driving information of the vehicle using a trajectory generation algorithm to determine a future motion trajectory of the vehicle. The vehicle processes the future motion trajectory of the first obstacle and the future motion trajectory of the vehicle using a collision detection algorithm, analyzes the intersection of the two trajectories, and determines risk information.

[0135] It is understandable that the predefined algorithms may also include machine learning algorithms for predicting obstacle behavior (such as the future motion trajectory of the first obstacle), and Bayesian filtering algorithms for fusing data detected by various sensors to accurately estimate the distance and speed information between the first obstacle and the vehicle.

[0136] The predefined model may be a trained machine learning model or a deep learning model (neural network model). For example, the neural network model may be a preconfigured neural network model in the vehicle, or a neural network model obtained from an open source platform or cloud service. The neural network model may be a convolutional neural network (CNN).

[0137] For example, a predefined model is a CNN model for assessing collision risk. The CNN model training process includes: collecting a large amount of data from real driving scenarios as input data, including but not limited to vehicle driving information and obstacle information. The collected input data is preprocessed by features extraction and other preprocessing operations. Based on historical data or experience, the preprocessed data is labeled to obtain labels corresponding to the input data (for example, each set of input data (obstacle information and vehicle information) is labeled with corresponding risk information). An appropriate machine learning algorithm or deep learning model is selected to construct the CNN model. The labeled dataset is input into the CNN model, and prediction results are obtained through training using the forward propagation algorithm. A loss function is calculated based on the prediction results and labels, and a backpropagation algorithm is initiated to adjust the model parameters of the CNN model based on the loss function. After the model parameters are adjusted, the CNN model repeats the above forward propagation, loss calculation, backpropagation, and parameter adjustment process until the CNN model converges or reaches the set number of iterations. Training ends and the CNN model is output. The trained CNN model can be applied to actual prediction, recognition, and other tasks.

[0138] In practical applications, the vehicle determines risk information through a CNN model. For example, first information about a first obstacle and first driving information of the vehicle are input into the CNN model, and the CNN model inputs the risk information.

[0139] It is understood that the model in the embodiments of the present application can be trained based on a large amount of real driving data, which can be understood as relevant data when the driver responds to obstacles. This allows the vehicle to make decisions more consistent with those of a human driver, thereby improving the vehicle's intelligence.

[0140] In some embodiments, the vehicle makes a prediction based on the first information of the first obstacle and the first driving information of the vehicle to obtain prediction information, and then performs short-term deduction and collision detection based on the prediction information to determine risk information.

[0141] Predicted information can be understood as the future behavior of obstacles and vehicles. This includes, but is not limited to, the future speed, position, state, acceleration, and trajectory of obstacles, and the future speed, position, state, acceleration, and trajectory of vehicles.

[0142] Specifically, the first obstacle is a dynamic obstacle. The vehicle determines predicted information about the first obstacle, such as the future trajectory and future speed of the first obstacle, based on the first information about the first obstacle and a predefined algorithm or model. The vehicle determines predicted information about the vehicle, such as the future trajectory and future speed of the vehicle, based on the first driving information and a predefined algorithm or model. The vehicle determines a first collision point based on the future trajectory of the first obstacle and the future trajectory of the vehicle. Based on the first collision point, the first information, and the first driving information, the vehicle determines risk information such as the collision time, the current distance between the vehicle and the first collision point, and the current distance between the first obstacle and the first collision point.

[0143] Predefined algorithms include, but are not limited to, motion prediction algorithms and trajectory generation algorithms, and predefined models include trained machine learning models.

[0144] After receiving the prediction information, the vehicle performs a short-term simulation based on the prediction information, simulating the relative motion of the vehicle and obstacles within a short period of time. Based on the short-term simulation, the vehicle performs collision detection, comparing the speed, position, and other information of the vehicle and obstacle to determine the risk.

[0145] Among them, collision detection includes but is not limited to geometric collision detection, time arrival analysis, etc.

[0146] In other embodiments, when determining risk information, the vehicle may also refer to the vehicle's surrounding environment information for judgment. For example, the risk information may be adaptively adjusted based on road curvature, real-time vehicle conditions, weather conditions, etc.

[0147] After the vehicle determines the risk information, it determines the risk level. In some embodiments, the vehicle processes the risk information based on a longitudinal risk assessment algorithm or model and a lateral risk assessment algorithm or model to determine the longitudinal and lateral risks. The vehicle comprehensively analyzes the longitudinal and lateral risks, considering the combined impact of different risks on driving, to determine the risk level.

[0148] In other embodiments, a mapping table of risk information and risk levels is pre-configured in the vehicle. The mapping table includes risk information of different dimensions and their corresponding risk levels. The vehicle determines the risk level corresponding to the current risk information based on the mapping table. For example, by performing big data analysis and evaluation on historical data and simulation data, the relationship between risk information of different dimensions (such as distance, speed, track intersection, etc.) and risk levels is determined, a mapping table of risk information and risk levels is developed, and the mapping table is integrated into the vehicle. It is understandable that the mapping table can be continuously updated and improved in line with technological development and actual usage to adapt to more traffic environments.

[0149] For example, based on the example of FIG. 1 , vehicle 11 executes the intelligent driving method of the present application. While driving, vehicle 11 detects vehicle 12, determines that there is a risk of collision between the vehicle 12 and the vehicle 12, and identifies vehicle 12 as the first obstacle. Vehicle 11 obtains information such as vehicle 12's current speed, position, direction, and acceleration. Vehicle 11 also obtains its own current driving information, such as its own position, speed, acceleration, and direction. Based on this information, vehicle 11 determines its own future trajectory, vehicle 12's future trajectory, the collision point, the time it takes for vehicle 11 to reach the collision point to be greater than the time it takes for vehicle 12 to reach the collision point, the distance it takes for vehicle 11 to reach the collision point to be greater than the distance it takes for vehicle 12 to reach the collision point, the speed of vehicle 11 to be less than the speed of vehicle 12, and the distance between vehicle 11 and vehicle 12 to be greater. Based on this information, it can be determined that the time it takes for vehicle 11 to reach the collision point is less than the time it takes for vehicle 12 to reach the collision point, indicating a low risk of collision between the two vehicles. Therefore, the risk level is determined to be medium.

[0150] Exemplarily, based on the example of FIG. 7 above, the vehicle executes the intelligent driving method of the present application, detects pedestrian information during vehicle driving, and determines that there is a risk of collision between the pedestrian and the vehicle. Based on the vehicle information and the pedestrian information, the vehicle determines that the pedestrian's speed is less than the vehicle's speed, and the distance from the pedestrian to the collision point is less than the distance from the vehicle to the collision point. The time it takes for the pedestrian to reach the collision point is less than the time it takes for the vehicle to reach the collision point, but the two are relatively close, and the pedestrian is still walking. Based on the above information, it can be determined that if the pedestrian and vehicle are both moving at their current speeds, the risk of collision between the two is greater, so the risk level is high.

[0151] Optionally, after determining the risk level, the vehicle may also determine a control strategy corresponding to the risk level according to preset rules. The preset rules include a correspondence between the risk level and the control strategy, and the control strategy includes a yielding strategy or a cutting-over strategy.

[0152] It should be understood that the vehicle predicts the collision risk based on the information of the first obstacle and the vehicle, determines the corresponding control strategy based on the collision risk, plans the vehicle speed based on the control strategy and collision risk, efficiently adjusts the vehicle's driving speed, controls the vehicle to take a more appropriate approach to avoid collisions, improves the vehicle's traffic rate, reduces unnecessary braking, and improves the driving experience.

[0153] In this application, the vehicle comprehensively considers obstacle information and vehicle information to accurately determine the risk level of collision between the obstacle and the vehicle, so that the vehicle can make more reasonable decisions based on the risk level, control the vehicle's driving speed, improve the vehicle's traffic efficiency, and enhance the driving experience and vehicle safety.

[0154] S603: The vehicle acquires a first cruising speed that matches the first risk level; the first cruising speed is the minimum driving speed of the vehicle at the first risk level for avoiding collision with the first obstacle.

[0155] In some embodiments, a mapping table between risk levels and cruising speeds is pre-configured in the vehicle. The vehicle can determine a first cruising speed that matches the first risk level based on the mapping relationship.

[0156] In other embodiments, the vehicle calculates a first cruising speed corresponding to the first risk level through a predefined rule or model based on the first risk level, the first information, and the first driving information.

[0157] For example, the predefined rules can be a set of rules regarding risk level and cruising speed determined through logical reasoning, analysis of historical data, or empirical data. The predefined model can be a machine learning model or deep learning model obtained by training historical data on risk level and cruising speed.

[0158] It is understandable that the first cruising speed should be a valid value. For example, the first cruising speed is greater than or equal to 0.

[0159] For example, based on the examples shown in FIG1 and FIG7 in step S602 above, the collision risk between vehicles 11 and 12 in FIG1 is low, with a medium risk level. Based on the preconfigured mapping table, the first cruising speed of vehicle 11 is determined to be speed 1. The collision risk between the vehicle and the pedestrian in FIG7 is high, with a high risk level. Based on the preconfigured mapping table, the first cruising speed of the vehicle is determined to be speed 2. Speed ​​1 should be greater than speed 2. A higher risk level indicates a greater likelihood of collision between the vehicle and an obstacle. Therefore, the vehicle's speed should be reduced as quickly as possible, allowing the vehicle to cruise at a lower speed.

[0160] S604: The vehicle controls the vehicle to travel at a first cruising speed.

[0161] The first cruising speed can be understood as a target driving speed planned by the vehicle based on the current risk level to avoid collisions between the vehicle and dynamic obstacles in the future. Therefore, the vehicle needs to adjust its current driving speed to the first cruising speed.

[0162] Controlling the vehicle to travel at the first cruising speed should be understood as controlling the vehicle's travel speed to adjust from the current speed to the first cruising speed, and then controlling the vehicle to travel stably at the first cruising speed. This control process includes a speed adjustment process and a speed maintenance process.

[0163] In some embodiments, the vehicle generates a speed planning curve based on the current speed of the vehicle and the first cruising speed, so that the vehicle adjusts the vehicle's driving speed to the first cruising speed according to the speed planning curve, thereby allowing the vehicle to travel at the first cruising speed.

[0164] Specifically, the speed planning model incorporates the first cruising speed as a constraint to ensure that the vehicle's speed within the speed planning curve does not fall below the set first cruising speed. The speed planning model treats speed planning as an optimization problem, finding the optimal speed curve while satisfying various constraints. The speed planning model generates a smooth speed planning curve connecting the current speed and the first cruising speed, ensuring a smooth transition from the current speed to the first cruising speed. The vehicle adjusts its speed based on the information on the speed planning curve, gradually adjusting it to the first cruising speed.

[0165] Exemplarily, as shown in FIG8 , based on the example in the above step S603, the speed planning curve of the vehicle 11 in FIG1 is speed planning curve 1. The speed planned by speed planning curve 1 is: the vehicle 11 smoothly drops from the current speed V1 to speed 1, and then maintains speed 1 to continue driving. The speed planning curve of the vehicle in FIG7 is speed planning curve 2. The speed planned by speed planning curve 2 is: the vehicle smoothly drops from the current speed V2 to speed 2, and then maintains speed 2 to continue driving. The risk level of the vehicle 11 in FIG1 is medium risk, and the risk level of the vehicle in FIG7 is high risk, and speed 1 is greater than speed 2. Compared with the vehicle in FIG7 , the vehicle 11 in FIG1 does not need to drop to speed 1 quickly, so the vehicle deceleration time is greater than the vehicle deceleration time in FIG7 .

[0166] It can be understood that in the above example, the vehicle's driving speed is smoothly adjusted to the first cruising speed, which can avoid problems such as vehicle loss of control and reduced comfort caused by sudden changes in vehicle driving speed, thereby ensuring the safety of the vehicle and driving experience.

[0167] In some embodiments, before controlling the vehicle to travel at the first cruising speed, the vehicle needs to determine whether the first cruising speed can be used for speed planning.

[0168] It is understandable that when cruising, the vehicle needs to maintain a certain safe distance from the object in front to facilitate vehicle control. Therefore, before using the cruise obstacle avoidance strategy, it is necessary to determine whether the current vehicle is at a safe distance from the collision point.

[0169] Optionally, the vehicle determines whether the distance between the current vehicle position and the first collision point is greater than a first threshold; if so, the vehicle adopts a first cruising speed to plan the vehicle's driving speed; otherwise, the vehicle does not adopt the first cruising speed to plan the vehicle's driving speed.

[0170] The first threshold is the minimum safe distance between the vehicle and the first obstacle. The first threshold is the minimum safe distance between the vehicle and the first obstacle, which is pre-calculated based on dynamic characteristics and safety considerations.

[0171] It is understandable that based on the above example, such as vehicle 11 and vehicle 12 in Figure 1, vehicle 11 and vehicle 12 are traveling toward collision point 13. Vehicle 12 may pass the collision point first, but if vehicle 12 suddenly stalls when passing the collision point, the distance between vehicle 11 and collision point 13 is less than the first threshold value. The distance is too short to provide vehicle 11 with sufficient reaction time to deal with the emergency. Even if vehicle 11 brakes suddenly, there is still a risk of collision. Therefore, before the vehicle enters cruise control, it is necessary to ensure that there is enough space around the vehicle to provide sufficient reaction time to respond to various traffic situations in a timely manner.

[0172] Optionally, the vehicle determines whether there is a second obstacle on the vehicle's future motion trajectory, where the second obstacle is a static obstacle and the distance between the current vehicle position and the second obstacle is greater than the distance between the current vehicle position and the first collision point; if so, it determines whether the distance between the current vehicle position and the second obstacle is greater than a second threshold; if so, the vehicle adopts the first cruising speed to plan the vehicle's driving speed; otherwise, the first cruising speed is not adopted to plan the vehicle's driving speed.

[0173] The second threshold is the minimum safe distance between the vehicle and the second obstacle, and the first threshold is the minimum safe distance between the vehicle and the second obstacle that is pre-calculated based on dynamic characteristics and safety considerations.

[0174] As shown in Figure 9 , vehicle 91 is traveling forward and vehicle 92 is traveling left. Vehicle 91 is executing the intelligent driving method of the present application. Vehicle 91 detects vehicle 92 as a dynamic obstacle. Based on information from both vehicles 91 and 92, it determines a risk level of 1 and sets a first cruising speed of 3 for vehicle 91 based on this risk level. However, vehicle 91 also detects that a rock 94 is located after collision point 93 on its predicted future trajectory, and the distance between rock 94 and vehicle 91 is less than a second threshold. In this case, vehicle 91 does not use the planned speed of 3 and actively brakes to a stop. It is understandable that if the vehicles were to slow down and drive forward along the planned curve for speed 3, when vehicle 92 passes the obstacle, it would be at position 95. Although it has not yet reached the collision point, it is very close to rock 94. Even if the vehicles actively brake to a stop, a collision risk could still occur. Therefore, a cruising obstacle avoidance strategy cannot be used; instead, active braking should be used to leave sufficient space for the vehicles to react.

[0175] It is understandable that in the above example, in order to provide sufficient reaction time for the vehicle in the cruising state and ensure vehicle safety, it is determined whether the surrounding environment can use the cruising state before entering the cruising state.

[0176] Optionally, the vehicle determines whether the distance between the current vehicle position and the first collision point is greater than a first threshold; if so, it continues to determine whether there is a second obstacle on the vehicle's future motion trajectory, the second obstacle is a static obstacle, and the distance between the current vehicle position and the second obstacle is greater than the distance between the current vehicle position and the first collision point; if so, it determines whether the distance between the current vehicle position and the second obstacle is greater than a second threshold; if so, the vehicle adopts the first cruising speed to plan the vehicle's driving speed; otherwise, the first cruising speed is not adopted to plan the vehicle's driving speed.

[0177] It's understood that dynamic obstacles are movable, while static obstacles are immovable. While avoiding an obstacle, the vehicle must also maintain a sufficient safe distance between itself and the obstacle. The first threshold is lower than the second threshold. Therefore, before using the first cruising speed to plan the vehicle's driving speed, it is necessary to determine whether there is sufficient space between the current vehicle and the first collision point, as well as between the vehicle and the static obstacle, to avoid the obstacle through cruising. If there is insufficient space, cruising should not be used to avoid the obstacle. Instead, emergency braking can be used to reserve more room for the vehicle to maneuver. For example, if neither the vehicle nor the first obstacle slows down, and the distance between the vehicle and the first obstacle approaches the first collision point, when the distance between the vehicle and the first collision point is less than or equal to the first threshold, the risk of collision between the vehicle and the first obstacle is very high. Even if the vehicle brakes suddenly, it will not be able to stop before the collision point, and a collision cannot be avoided. Therefore, the vehicle must ensure a sufficient safe distance from the collision point to allow it to stop before the collision point, ensuring sufficient room for maneuver.

[0178] In the present application, determining whether the vehicle can adopt the first cruising speed to plan the vehicle's driving speed based on the safety distance can better ensure vehicle safety.

[0179] It can be understood that after determining that the vehicle can plan the vehicle's driving speed according to the first cruising speed, the vehicle changes its driving speed according to the first cruising speed and the speed planning curve of the current speed plan. While the vehicle changes the driving speed, it also collects information about the first obstacle and the vehicle in real time, determines the corresponding risk level based on the information collected in real time, and adaptively adjusts the cruising speed according to the risk level.

[0180] It is understandable that after the vehicle determines the cruising speed corresponding to the risk level, it first plans the speed based on the cruising speed, and then determines whether the cruising speed can be adopted (that is, whether a sufficient safety distance is left based on the cruising speed so that the vehicle can respond in time). If it can be adopted, the vehicle's driving speed is adjusted according to the speed planning curve determined by the first cruising speed and the current speed.

[0181] In some embodiments, when the vehicle uses the first cruising speed and the speed planning curve determined by the current speed to adjust the vehicle's driving speed, if the risk level corresponding to the first obstacle information and vehicle information collected by the vehicle in real time has not changed, the cruising speed will not be changed. After the vehicle's driving speed is adjusted to the first cruising speed, the vehicle travels at the first cruising speed.

[0182] In other embodiments, when the vehicle is adjusting the vehicle's driving speed using a speed planning curve determined by a first cruising speed and a current speed, if the risk level corresponding to the first obstacle information and vehicle information collected by the vehicle in real time changes, the vehicle determines its matching cruising speed based on the changed risk level, and replans the vehicle's driving speed based on the cruising speed so that the vehicle travels according to the replanned cruising speed.

[0183] Exemplarily, after the vehicle plans its driving speed according to the first cruising speed, the vehicle detects the first obstacle information and the vehicle information in real time. If a second risk level is determined based on the second information of the first obstacle and the second driving information of the vehicle; the second risk level is higher than the first risk level, and the second risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the second information and the second driving information; then a second cruising speed matching the second risk level is obtained; the second cruising speed is lower than the first cruising speed, and the second cruising speed is the minimum driving speed of the vehicle to avoid a collision with the first obstacle under the second risk level; and the vehicle is controlled to travel at the second cruising speed.

[0184] Exemplarily, after the vehicle plans its driving speed according to the first cruising speed, the vehicle detects the first obstacle information and the vehicle information in real time. If a third risk level is determined based on the third information of the first obstacle and the third driving information of the vehicle; the third risk level is lower than the first risk level, and the third risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the third information and the third driving information; then a third cruising speed matching the third risk level is obtained; the third cruising speed is higher than the first cruising speed, and the third cruising speed is the minimum driving speed for the vehicle to avoid a collision with the first obstacle under the first risk level; and the vehicle is controlled to travel at the third cruising speed.

[0185] The first information described above can be understood as the obstacle information collected at the first moment, and the second / third information can be understood as the obstacle information collected at the Nth moment. N is an integer greater than 1. The first driving information can be understood as the vehicle driving information collected at the first moment, and the second / third driving information can be understood as the vehicle driving information collected at the Nth moment. The second risk level is the collision risk between the first obstacle and the vehicle determined based on the second information and the second driving information. The third risk level is the collision risk between the first obstacle and the vehicle determined based on the third information and the third driving information. Both the second risk level and the second risk level are different from the first risk level.

[0186] The second risk level being higher than the first risk level can be understood as indicating that the collision risk between the first obstacle and the vehicle at time N has increased compared to the first time (e.g., if the first obstacle does not slow down and cuts in front of the vehicle), so the vehicle should reduce its cruising speed to avoid the first obstacle and allow it to pass the collision point first. Therefore, the second cruising speed corresponding to time N is lower than the first cruising speed at the first time, and the vehicle uses the newly determined second cruising speed for speed planning, allowing the vehicle to travel at this newly determined second cruising speed.

[0187] The third risk level being higher than the first risk level can be understood as meaning that the collision risk between the first obstacle and the vehicle at time N has decreased compared to the collision risk at time one (e.g., the first obstacle has slowed down and given way). Therefore, the vehicle should be able to increase its cruising speed and pass the collision point first. Therefore, the third cruising speed corresponding to time N is lower than the first cruising speed at time one. The vehicle uses the newly determined third cruising speed for speed planning, allowing the vehicle to travel at this newly determined third cruising speed.

[0188] Exemplarily, based on the example of FIG1 above, after planning the speed of vehicle 11 according to speed 1, the vehicle continues to detect information of vehicle 11 and vehicle 12. If it is detected that vehicle 12 is decelerating, the speed of vehicle 12 is less than the speed of vehicle 11, and the time taken for vehicle 12 to reach the collision point is not much different from the time taken for vehicle 11 to reach the collision point, it is determined that the risk of collision between the two has increased. The risk level at this time is high risk, and vehicle 11 should reduce the cruising speed, such as speed 4, and plan the vehicle speed based on speed 4 so that the vehicle can travel according to speed 4.

[0189] Exemplarily, based on the example of Figure 7 above, after the vehicle plans the speed according to speed 2, the vehicle continues to detect information about vehicles and pedestrians. If it is detected that the pedestrian stops or retreats, it can be determined that the risk of collision between the vehicle and the pedestrian is reduced. At this time, the risk level is low risk, and the vehicle can increase the cruising speed, such as speed 5, and plan the vehicle speed based on speed 5 so that the vehicle can travel according to speed 5.

[0190] It can be understood that the specific implementation methods of controlling the vehicle to travel at the second cruising speed and controlling the vehicle to travel at the third cruising speed are described above and will not be repeated here.

[0191] It can be understood that the prerequisite for the vehicle to plan the vehicle's driving speed according to the second cruising speed and the vehicle's driving speed according to the third cruising speed should be: the distance between the vehicle position at the Nth moment and the first collision point is greater than the first threshold; or, the distance between the vehicle position at the Nth moment and the first collision point is greater than the first threshold, and there is a static second obstacle on the vehicle's future motion trajectory, the distance between the vehicle position at the Nth moment and the second obstacle is greater than the distance between the vehicle position at the Nth moment and the first collision point, and the distance between the vehicle position at the Nth moment and the second obstacle is also greater than the second threshold.

[0192] It is understood that the above embodiments all use the example of a vehicle successfully planning its driving speed based on its cruising speed. However, in actual applications, there are also strategies for actively exiting cruise control. The following details the specific implementation of a vehicle actively exiting cruise control.

[0193] In other embodiments, after the vehicle plans the driving speed of the vehicle according to the first cruising speed, the vehicle detects information of the obstacle and the vehicle in real time, and determines a fourth risk level based on fourth information of the first obstacle and fourth driving information of the vehicle; the fourth risk level is higher than the first risk level, and the fourth risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the fourth information and the fourth driving information; a fourth cruising speed matching the fourth risk level is obtained; the fourth cruising speed is lower than the first cruising speed, and the fourth cruising speed is the minimum driving speed of the vehicle to avoid a collision with the first obstacle under the fourth risk level; it is determined that the distance between the current position of the vehicle and the first collision point is less than or equal to a first threshold; the first collision point is the intersection of the future motion trajectory of the obstacle and the future motion trajectory of the vehicle predicted based on the first information and the first driving information, and the first threshold is the minimum safe distance between the vehicle and the first obstacle; the driving speed of the vehicle is reset to zero.

[0194] For example, based on the example of Figure 1 above, after planning the speed of vehicle 11 based on speed 1, the vehicle continues to detect information about vehicles 11 and 12. If vehicle 12 is detected to be decelerating, and its speed is less than that of vehicle 11, and the time it takes for vehicle 12 to reach the collision point is not much different from that of vehicle 11, then the risk of collision between the two vehicles may increase, and the risk level at this time is high. Vehicle 11 should reduce its cruising speed, such as to speed 4, and plan its speed based on speed 4. However, if it is detected that the distance between the current vehicle position and the collision point is less than a first threshold, the vehicle cannot respond to the unexpected situation in a timely manner, so the vehicle should exit the cruising strategy and gradually reduce its driving speed to zero.

[0195] In other embodiments, if there is a second obstacle on the future motion trajectory of the vehicle, the second obstacle is a static obstacle, and the distance between the current vehicle position and the second obstacle is greater than the distance between the current vehicle position and the first collision point, after planning the vehicle's driving speed according to the first cruising speed, the vehicle detects information of the obstacle and the vehicle in real time, and determines a fifth risk level based on fifth information of the first obstacle and fifth driving information of the vehicle; the fifth risk level is higher than the first risk level, and the fifth risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the fifth information and the fifth driving information; obtains a fifth cruising speed that matches the fifth risk level; the fifth cruising speed is lower than the first cruising speed, and the fifth cruising speed is the minimum driving speed for the vehicle to avoid a collision with the first obstacle under the fifth risk level; determines that the distance between the current vehicle position and the second obstacle is less than or equal to a second threshold; the second threshold is the minimum safe distance between the vehicle and the second obstacle; and resets the vehicle's driving speed to zero.

[0196] It is understandable that returning the vehicle's speed to zero is a gradual process, where the vehicle decelerates from its current speed until it reaches zero, thereby ensuring user comfort and vehicle stability.

[0197] Based on the descriptions of the second information, third information, second driving information, and third driving information above, the fourth information / fifth information can be understood as obstacle information collected at time N, and the fourth driving information / fifth driving information can be understood as vehicle driving information collected at time N. The fourth risk level is the collision risk between the first obstacle and the vehicle determined based on the fourth information and the fourth driving information. The fifth risk level is the collision risk between the first obstacle and the vehicle determined based on the fifth information and the fifth driving information.

[0198] The fourth risk level is higher than the first. The vehicle should reduce its cruising speed to avoid the first obstacle, allowing it to pass the collision point first. However, the vehicle also detects that the distance between the vehicle's position at time N and the first collision point is less than or equal to the first threshold. This means that the distance between the vehicle's position at time N and the first collision point is less than or equal to the minimum safe distance. Continuing to use the cruising strategy cannot guarantee safety. Therefore, the vehicle should smoothly exit the cruising strategy, transitioning smoothly from the vehicle's speed at time N to zero, allowing the vehicle to brake before the collision point and leaving sufficient space.

[0199] The fifth risk level is lower than the first risk level. The vehicle should increase its cruising speed and pass the obstacle first. However, the vehicle detects a second, static obstacle in its future trajectory. The distance between the vehicle's position at moment N and the second obstacle is greater than the distance between the vehicle's position at moment N and the first collision point, and the distance between the vehicle's position at moment N and the second obstacle is less than or equal to the second threshold. In this case, the distance between the vehicle's position at moment N and the second obstacle is less than or equal to the minimum safe distance. Even if the vehicle adopts a cruising strategy to prioritize passing the collision point and then slowing down and braking to a stop, the distance between the vehicle and the second obstacle is too close, restricting its movement and failing to ensure safety. Therefore, the vehicle should smoothly exit the cruising strategy, transitioning smoothly from the vehicle's speed at moment N to 0, allowing the vehicle to stop before the collision point and leaving sufficient space for maneuvering.

[0200] It is understandable that after the vehicle adopts the cruise strategy, the vehicle continues to move forward, the distance between the vehicle and the collision point is getting closer and closer, and the distance between the vehicle and the obstacle is also getting closer and closer. When the traffic conditions do not meet the safe distance that should be maintained in the cruise state, the vehicle actively exits the cruise state to ensure the safe driving of the vehicle so that the vehicle can respond to various traffic conditions in a timely manner.

[0201] It can be understood that the application scenario of this application is an autonomous driving scenario, where the vehicle autonomously perceives the surrounding environment and makes corresponding decisions. The decisions made by the vehicle should be close to the behavior and decisions of human drivers.

[0202] In this application, when the vehicle avoids obstacles according to the cruising speed corresponding to the current risk level, it determines whether the safety distance is met based on the real-time distance information. When the real-time distance is less than the safety distance, it exits the cruise smoothly to ensure the safety of the vehicle and the comfort of the user.

[0203] It is understandable that in the existing technology, vehicles actively brake to avoid dynamic obstacles. In this way, the vehicle will brake every time it encounters a dynamic obstacle, resulting in low vehicle driving efficiency and traffic efficiency, and the vehicle is not intelligent, which affects the user experience.

[0204] In the technical solution of the present application, when a vehicle faces a dynamic obstacle, it collects real-time information of the dynamic obstacle and the vehicle, determines the collision risk between the dynamic obstacle and the vehicle, determines the minimum cruising speed of the vehicle based on the collision risk, plans the vehicle's driving speed based on the minimum cruising speed, and allows the vehicle to travel to the collision point at the minimum cruising speed. The vehicle detects the behavior of the dynamic obstacle in real time and conducts a game. If the dynamic obstacle is not avoided, the vehicle can travel at a lower speed, brake at a position close to the collision point, and wait for the dynamic obstacle to pass the collision point before driving again. Alternatively, if the dynamic obstacle is not avoided, the vehicle can travel at an even lower speed. When the dynamic obstacle passes the collision point, the vehicle is still a certain distance away from the collision point. After the dynamic obstacle passes the collision point, the vehicle can accelerate to pass the collision point. Alternatively, if the dynamic obstacle is avoided, the speed can be gradually increased according to the risk level, and the collision point can be passed first.

[0205] For example, as shown in Figure 10, vehicle 1001 detects that vehicle 1002 will cross its future driving path, and the collision point of the two vehicles is 1003. In the prior art, vehicle 1001 actively brakes and the braking point is 1004. In the technical solution of this application, vehicle 1001 adopts a cruising strategy and cruises at a lower speed. The final braking point is 1005, which is closer to the collision point.

[0206] It can be understood that the technical solution of the present application reduces or increases the vehicle's cruising speed according to the real-time collision risk when the vehicle faces a dynamic obstacle, and flexibly controls the vehicle based on the cruising speed to negotiate with the dynamic obstacle, avoiding unnecessary braking or braking operations, so that the vehicle outputs a human-like speed planning strategy, increases the vehicle's bargaining ability and human-likeness, improves the vehicle's traffic efficiency and driving efficiency, and enhances the driving experience.

[0207] Optionally, the first obstacle can be a static obstacle, such as an immovable object or building. After the vehicle detects the first obstacle, it can perform a risk assessment and slowly approach the obstacle at the minimum cruising speed until it brakes to a stop. Alternatively, after detecting the first obstacle, the vehicle can slowly exit the cruise state. This adds a method for handling static obstacles while the vehicle is cruising.

[0208] In this application, the vehicle determines a matching cruising speed based on the risk between the vehicle and the obstacle, plans the vehicle's driving speed based on the cruising speed, and controls the vehicle to travel at the cruising speed. When facing a dynamic obstacle, the vehicle does not brake directly, but instead drives slowly at a lower speed, playing a game with the dynamic obstacle. The vehicle is flexibly controlled based on real-time risk, optimizing the intelligent driving strategy and increasing the vehicle's flexibility in avoiding dynamic obstacles in the autonomous cruising state. While ensuring vehicle safety, it also improves the vehicle's traffic efficiency and ability to overtake, enhancing the driving experience, and allowing the vehicle to better adapt to complex traffic environments.

[0209] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily appreciate that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0210] As shown in Figure 11, it is a schematic diagram of the structure of another intelligent driving device provided in an embodiment of the present application. The intelligent driving device 1100 includes a detection module 1101 and a processing module 1102. The intelligent driving device 1100 is used to execute the above-mentioned intelligent driving method, for example, for executing the intelligent driving method shown in Figures 6 to 10. Of course, the intelligent driving device 1100 may also include other modules, or the intelligent driving device 1100 may include fewer modules. The embodiments of the present application do not specifically limit the specific form and implementation of the intelligent driving device.

[0211] The detection module 1101 is configured to detect a first obstacle; the first obstacle is a dynamic obstacle.

[0212] The processing module 1102 is used to determine a first risk level based on the first information about the first obstacle and the first driving information of the vehicle; the first risk level is used to indicate the possibility of a collision between the first obstacle and the vehicle under the first information and the first driving information.

[0213] The processing module 1102 is further configured to obtain a first cruising speed that matches the first risk level, where the first cruising speed is the minimum speed at which the vehicle can avoid colliding with the first obstacle under the first risk level.

[0214] The processing module 1102 is further configured to control the vehicle to travel at a first cruising speed.

[0215] The operations and / or functions of each module in the intelligent driving device 1100 are respectively for realizing the corresponding processes of the intelligent driving method described in the above method embodiment. All relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional unit. For the sake of brevity, they will not be repeated here.

[0216] Optionally, the intelligent driving device 1100 shown in FIG11 may further include a storage module (not shown in FIG11 ) storing a program or instruction. When the detection module 1101 and the processing module 1102 execute the program or instruction, the intelligent driving device 1100 shown in FIG11 may perform the intelligent driving method described in the above method embodiment.

[0217] The technical effects of the intelligent driving device 1100 shown in FIG11 may refer to the technical effects of the intelligent driving method described in the above method embodiment, and will not be repeated here.

[0218] An embodiment of the present application also provides a chip system, as shown in Figure 12, the chip system 1200 includes at least one processor 1201 and at least one interface circuit 1202. As an example, when the chip system 1200 includes one processor and one interface circuit, the one processor may be the processor 1201 shown in the solid box in Figure 12 (or the processor 1201 shown in the dotted box), and the one interface circuit may be the interface circuit 1202 shown in the solid box in Figure 12 (or the interface circuit 1202 shown in the dotted box). When the chip system 1200 includes two processors and two interface circuits, the two processors include the processor 1201 shown in the solid box in Figure 12 and the processor 1201 shown in the dotted box, and the two interface circuits include the interface circuit 1202 shown in the solid box in Figure 12 and the interface circuit 1202 shown in the dotted box. This is not limited.

[0219] The processor 1201 and the interface circuit 1202 can be interconnected via a line. For example, the interface circuit 1202 can be used to receive signals. For another example, the interface circuit 1202 can be used to send signals to other devices (such as the processor 1201). Exemplarily, the interface circuit 1202 can read instructions stored in the memory and send the instructions to the processor 1201. When the instructions are executed by the processor 1201, the intelligent driving device can perform the various steps in the above embodiment. Of course, the chip system can also include other discrete devices, which is not specifically limited in the embodiment of the present application.

[0220] Exemplarily, the chip system can be a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system on a chip (SoC), a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD) or other integrated chip.

[0221] It should be understood that each step in the above method embodiment can be completed by hardware integrated logic circuits in a processor or by software instructions. The method steps disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware processor, or by a combination of hardware and software modules in a processor.

[0222] An embodiment of the present application also provides a computer-readable storage medium storing one or more computer programs, wherein the one or more computer programs include instructions that, when executed by a computer, enable the computer to execute the corresponding process of the intelligent driving method in the above embodiment.

[0223] In some embodiments, the disclosed methods may be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or on other non-transitory media or articles of manufacture.

[0224] An embodiment of the present application also provides a computer program product. When the computer program product is run on a computer, it enables the computer to execute the above-mentioned related steps to implement the intelligent driving method in the above-mentioned embodiment.

[0225] The apparatus, computer-readable storage medium, computer program product, or chip provided in the embodiments of the present application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods provided above and will not be repeated here.

[0226] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An intelligent driving method, characterized in that: include: A first obstacle is detected; The first obstacle is a dynamic obstacle; determining a first risk level based on first information about the first obstacle and first driving information of the vehicle; The first risk level is used to indicate the probability of a collision between the first obstacle and the vehicle under the first information and the first driving information; Obtaining a first cruising speed that matches the first risk level, where the first cruising speed is a minimum speed at which the vehicle can avoid colliding with the first obstacle at the first risk level; The vehicle is controlled to travel at the first cruising speed.

2. The method according to claim 1, characterized in that The method further comprises: determining a second risk level based on second information about the first obstacle and second driving information of the vehicle; the second risk level being higher than the first risk level, the second risk level being used to indicate a probability of a collision between the first obstacle and the vehicle given the second information and the second driving information; Obtaining a second cruising speed that matches the second risk level; the second cruising speed is lower than the first cruising speed, and the second cruising speed is a minimum driving speed of the vehicle to avoid collision with the first obstacle at the second risk level; The vehicle is controlled to travel at the second cruising speed.

3. The method according to claim 1, characterized in that The method further comprises: determining a third risk level based on third information about the first obstacle and third driving information of the vehicle; the third risk level being lower than the first risk level, and indicating a probability of a collision between the first obstacle and the vehicle based on the third information and the third driving information; Obtaining a third cruising speed that matches the third risk level; the third cruising speed being higher than the first cruising speed, and being the minimum speed at which the vehicle avoids collision with the first obstacle at the first risk level; The vehicle is controlled to travel at the third cruising speed.

4. The method according to any one of claims 1 to 3, characterized in that The determining the first risk level according to the first information about the first obstacle and the first driving information of the vehicle includes: Predicting risk information based on first information about the first obstacle and first driving information about the vehicle; the risk information including one or more of the following: a first collision point, a collision time, a distance between the current position of the vehicle and the first collision point, and a current distance between the first obstacle and the first collision point; the first collision point being an intersection of a future motion trajectory of the first obstacle and a future motion trajectory of the vehicle predicted based on the first information and the first driving information; Evaluate the horizontal and vertical risks according to the risk information to determine the first risk level.

5. The method according to any one of claims 1 to 4, characterized in that The controlling the vehicle to travel at the first cruising speed includes: generating a speed planning curve according to the current speed of the vehicle and the first cruising speed; adjusting the driving speed of the vehicle to the first cruising speed according to the speed planning curve; The vehicle is controlled to travel at the first cruising speed.

6. The method according to any one of claims 1 to 5, characterized in that The controlling the vehicle to travel at the first cruising speed includes: If the distance between the current vehicle position and the first collision point is greater than a first threshold, the vehicle is controlled to travel at the first cruising speed; the first collision point is the intersection of the future motion trajectory of the first obstacle and the future motion trajectory of the vehicle predicted based on the first information and the first driving information, and the first threshold is the minimum safe distance between the vehicle and the first obstacle.

7. The method according to claim 6, characterized in that There is a second obstacle on the future motion trajectory of the vehicle, the second obstacle is a static obstacle, and a distance between the current position of the vehicle and the second obstacle is greater than a distance between the current position of the vehicle and the first collision point. Controlling the vehicle to travel at the first cruising speed includes: If the distance between the current vehicle position and the second obstacle is greater than a second threshold, the vehicle is controlled to travel at the first cruising speed; the second threshold is the minimum safe distance between the vehicle and the second obstacle.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: determining a fourth risk level based on fourth information about the first obstacle and fourth driving information of the vehicle; the fourth risk level being higher than the first risk level, the fourth risk level being used to indicate a probability of a collision between the first obstacle and the vehicle based on the fourth information and the fourth driving information; Obtaining a fourth cruising speed that matches the fourth risk level; the fourth cruising speed being lower than the first cruising speed, and being a minimum driving speed for the vehicle to avoid collision with the first obstacle at the fourth risk level; If the distance between the current vehicle position and the first collision point is less than or equal to a first threshold, the vehicle's driving speed is reset to zero; the first collision point is the intersection of the future motion trajectory of the first obstacle and the future motion trajectory of the vehicle predicted based on the first information and the first driving information, and the first threshold is the minimum safe distance between the vehicle and the first obstacle.

9. The method according to any one of claims 1 to 8, characterized in that There is a second obstacle on the future motion trajectory of the vehicle, the second obstacle is a static obstacle, and the distance between the current position of the vehicle and the second obstacle is greater than the distance between the current position of the vehicle and the first collision point. The method further includes: determining a fifth risk level based on fifth information about the first obstacle and fifth driving information of the vehicle; the fifth risk level being higher than the first risk level, and indicating a probability of a collision between the first obstacle and the vehicle based on the fifth information and the fifth driving information; Obtaining a fifth cruising speed that matches the fifth risk level; the fifth cruising speed being lower than the first cruising speed, and being the minimum driving speed of the vehicle at the fifth risk level for avoiding a collision with the first obstacle; If the distance between the current vehicle position and the second obstacle is less than or equal to a second threshold, the vehicle's speed is reset to zero; the second threshold is the minimum safe distance between the vehicle and the second obstacle.

10. An intelligent driving device, characterized in that: include: A processor and a memory, the memory being coupled to the processor, the memory being used to store computer-readable instructions, and when the processor reads the computer-readable instructions from the memory, the intelligent driving device executes the method according to any one of claims 1 to 9.

11. A vehicle, characterized in that: The vehicle includes the intelligent driving device according to claim 10.

12. A chip system, characterized in that: The method comprises at least one processor and at least one interface circuit, wherein the at least one interface circuit is used to perform transceiver functions, and the at least one processor is used to perform the method according to any one of claims 1 to 9.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

14. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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