Vehicle navigation method, vehicle navigation device and vehicle

Abstract

The invention provides a vehicle navigation method, a vehicle navigation device and a vehicle, and the method comprises the steps: determining a plurality of driving routes leading to a vehicle navigation destination based on map information, vehicle positioning information and environmental perception information, and determining navigation information according to the plurality of driving routes leading to the vehicle navigation destination, the navigation information is used for providing driving suggestions for the driver, and the navigation information is output through the vehicle-mounted man-machine interaction device. By fusing the map information, the vehicle positioning information and the environmental perception information, the navigation system can dynamically respond to changes of surrounding traffic participants, the real-time performance and safety of path planning are improved, and more reliable navigation guidance is realized and the driving assistance effect and the user experience are enhanced in the presence of obstacles, jam or congestion scenes.

Description

Technical Field

[0001] This application relates to the field of driving, and in particular to a vehicle navigation method, a vehicle navigation device, and a vehicle. Background Technology

[0002] With the development of intelligent transportation systems and driver assistance technologies, navigation functions play a crucial role in improving driving safety and traffic efficiency. Existing lane-level navigation solutions are typically based on high-precision positioning information and high-precision map data. By matching the vehicle's current location with the lane geometry and road topology in the map, they provide refined navigation guidance for lane changes, turns, and other maneuvers. Some technologies also incorporate data collected by onboard sensors such as cameras and radar to assist in lane line identification or correct positioning deviations, thereby enhancing navigation continuity in scenarios with limited satellite signals or partial map gaps.

[0003] However, in actual use, the recommended driving route may not be consistent with the surrounding traffic flow. Furthermore, when encountering complex traffic environments such as heavy traffic, frequent lane-changing, or obstacles ahead, existing navigation systems struggle to provide real-time traffic alerts, limiting the usability of the navigation information provided in certain scenarios. Summary of the Invention

[0004] This application provides a vehicle navigation method, a vehicle navigation device, and a vehicle. By integrating environmental perception information, the navigation system can dynamically respond to changes in surrounding traffic participants, improve the real-time performance and safety of route planning, and achieve more reliable navigation guidance in scenarios with obstacles, lane cutting, or congestion, thereby enhancing the effectiveness of driving assistance and user experience.

[0005] In a first aspect, this application provides a vehicle navigation method, which includes: determining multiple driving routes leading to the vehicle navigation destination based on map information, vehicle positioning information and environmental perception information; determining navigation information based on the multiple driving routes leading to the vehicle navigation destination; using the navigation information to provide driving suggestions to the driver; and outputting the navigation information through an in-vehicle human-machine interaction device.

[0006] By integrating map information, vehicle positioning information, and environmental perception information, the vehicle navigation method provided in this application can identify the status of surrounding traffic participants in real time and dynamically generate and optimize driving routes. Compared with the shortcomings of existing lane-level navigation that relies solely on static road data and cannot avoid dynamic risks, this application can provide timely lane-changing suggestions, risk warnings, and alternative route guidance when obstacles, lane-cutting, or congestion are detected. By integrating dynamic environmental perception into navigation decision-making, the real-time performance, safety, and adaptability of route planning can be significantly improved, enhancing the practicality of driver assistance functions and user experience.

[0007] As one possible implementation, environmental perception information includes the location and motion information of traffic participants, which include at least one of the following: vehicles, non-motorized vehicles, pedestrians, animals, obstacles, and traffic facilities.

[0008] By acquiring the location and action information of traffic participants, it is possible to identify vehicles, non-motorized vehicles, pedestrians, animals, obstacles, and traffic facilities in the surrounding dynamic environment, thereby improving the completeness and accuracy of environmental perception, providing reliable decision-making basis for navigation systems, and enhancing the adaptability to complex traffic scenarios.

[0009] As one possible implementation, the method further includes: when outputting navigation information on the vehicle-mounted human-machine interaction device, marking the target traffic participants on the display screen of the vehicle-mounted human-machine interaction device, the marking method includes highlighting borders, different color markings, dynamic arrow pointing or text label markings, and the target traffic participants include at least one of the following: vehicles, non-motorized vehicles, pedestrians, animals, obstacles and traffic facilities that affect the driving safety or continuity of the vehicle.

[0010] By visually identifying target traffic participants that affect the safety or continuity of driving on the in-vehicle human-machine interface through highlighted borders, color markings, arrow pointing, or text labels, key risk objects can be presented intuitively, improving the driver's perception ability, effectively reducing misjudgment and operation delays, and enhancing driving safety.

[0011] As one possible implementation, multiple driving routes to the vehicle navigation destination are updated based on changes in the status of the target traffic participants.

[0012] Multiple driving routes are dynamically updated based on changes in the status of target traffic participants, ensuring the timeliness and safety of route planning, avoiding traffic obstruction or risky operations caused by static planning, and improving the intelligence of the navigation system and the driving experience.

[0013] As one possible implementation, the method further includes: conducting collision risk assessment and driver operation complexity assessment on multiple driving routes leading to the vehicle navigation destination; determining a target driving route from the multiple driving routes leading to the vehicle navigation destination based on the assessment results; and determining navigation information based on the target driving route.

[0014] By conducting collision risk assessments and driver operational complexity assessments, high-risk driving behaviors can be avoided, the rationality and feasibility of navigation recommendations can be improved, and driving safety and comfort can be enhanced.

[0015] As one possible implementation, if the environmental perception information includes the detection of other vehicles within the turning path, the navigation information includes turning prompt information, which is used to provide the driver with suggested turning lanes and safe turning times.

[0016] By outputting turning prompts when other vehicles are detected in the turning path, the system can provide drivers with suggested turning lanes and safe times, avoiding collision risks caused by blind spots or misjudgments, and improving the safety of turning operations and the accuracy of navigation guidance.

[0017] As one possible implementation, when the environmental perception information includes the detection of a vehicle ahead slowing down or braking, the navigation information includes a deceleration prompt, which is used to provide the driver with a suggested deceleration range and timing.

[0018] By outputting a deceleration warning when a vehicle ahead is detected slowing down or braking, the system can provide the driver with suggested deceleration ranges and timings, giving early warning of potential rear-end collision risks, avoiding emergency braking, and improving driving safety.

[0019] As one possible implementation, when the environmental perception information includes detecting that a vehicle ahead is accelerating and increasing its distance, the navigation information includes acceleration prompts, which are used to provide the driver with suggested acceleration ranges and timing.

[0020] By providing acceleration prompts when a vehicle ahead accelerates and increases its distance, the system can offer drivers suggested acceleration ranges and timings, helping them seize opportunities to pass, improve driving efficiency and smoothness, and avoid prolonged low-speed driving.

[0021] As one possible implementation, when the environmental perception information includes the detection of congestion on the road ahead, the navigation information includes lane change guidance prompts, which are used to suggest to the driver the timing of a lane change and the target lane.

[0022] By providing lane-change guidance prompts when congestion is detected ahead, the system can offer drivers a target lane and suggested lane-change timing, helping them proactively avoid congestion, improve traffic efficiency and the accuracy of driving decisions, and enhance the practicality and intelligence of the navigation system.

[0023] As one possible implementation, when the environmental perception information includes the presence of vehicles with collision risk in the vehicle's lane or adjacent lanes, the navigation information includes driving prompts, which are used to suggest driving routes and avoidance routes to the driver.

[0024] By providing driving prompts when a vehicle or a vehicle in an adjacent lane is detected to pose a collision risk, the system can offer drivers suggested routes and avoidance paths, provide early warnings of potential dangers, enhance emergency response capabilities, effectively reduce collision risks, and improve driving safety.

[0025] As one possible implementation, navigation information is output through an in-vehicle human-machine interface device, specifically including: configuring the in-vehicle human-machine interface device to display driving suggestions graphically in front of the driver's field of vision or on the vehicle's central control display device. The graphical method includes displaying lane boundaries, traffic participant identification, predicted trajectories of traffic participants, and driving suggestion identification indicating lane changing, turning, or deceleration.

[0026] By displaying lane boundaries, traffic participant identification, predicted trajectories, and driving suggestions graphically in front of the driver's field of vision or on the central control display device, the driver's understanding of the surrounding environment and system intent can be improved, cognitive load can be reduced, and the visibility and operational safety of navigation guidance can be enhanced.

[0027] As one possible implementation, environmental perception information is used to determine based on data collected from one or more of cameras, lidar, millimeter-wave radar, or infrared sensors.

[0028] By utilizing data collected from one or more of cameras, lidar, millimeter-wave radar, or infrared sensors to determine environmental perception information, the perception results can be fused from multiple dimensions to improve the accuracy and robustness of identifying traffic participants and the road environment, and enhance adaptability under complex lighting and weather conditions.

[0029] Secondly, this application provides a vehicle navigation device, which includes: a route determination module for determining multiple routes to the vehicle navigation destination based on map information, vehicle positioning information and environmental perception information; a navigation information determination module for determining navigation information based on the multiple routes to the vehicle navigation destination, the navigation information being used to provide driving suggestions to the driver; and a navigation display module for outputting the navigation information through an in-vehicle human-machine interaction device.

[0030] Thirdly, this application provides a control device, which includes: a memory for storing programs, instructions or code, and a processor for executing the programs, instructions or code in the memory to perform the vehicle navigation method as provided in the first aspect.

[0031] Fourthly, this application provides a computer-readable storage medium storing a program or instructions that, when executed, implement the vehicle navigation method provided in the first aspect.

[0032] Fifthly, this application provides a computer program product, including computer program code, which, when run on a computer, causes the computer to perform the vehicle navigation method as provided in the first aspect. Attached Figure Description

[0033] Figure 1 This is a functional schematic diagram of a vehicle provided in an embodiment of this application;

[0034] Figure 2 This application provides a schematic diagram of the vehicle navigation method steps.

[0035] Figure 3A A diagram illustrating the identification of traffic participants in an in-vehicle human-machine interface. Figure 1 ;

[0036] Figure 3B A diagram illustrating the identification of traffic participants in an in-vehicle human-machine interface. Figure 2 ;

[0037] Figure 4A A diagram illustrating the dynamic update of driving routes based on changes in the state of traffic participants. Figure 1 ;

[0038] Figure 4B A diagram illustrating the dynamic update of driving routes based on changes in the state of traffic participants. Figure 2 ;

[0039] Figure 5 A schematic diagram is generated for turning prompts based on environmental perception.

[0040] Figure 6 Generate a schematic diagram for deceleration prompts based on the deceleration behavior of vehicles ahead;

[0041] Figure 7 Generate an illustration for acceleration prompts based on the acceleration behavior of vehicles ahead;

[0042] Figure 8 Generate a schematic diagram for lane change guidance prompts based on the traffic congestion ahead;

[0043] Figure 9 A schematic diagram is generated for driving prompts based on collision risk identification;

[0044] Figure 10 This is a schematic diagram of the structure of a vehicle navigation device;

[0045] Figure 11 This is a schematic diagram of another control device provided in an embodiment of this application. Detailed Implementation

[0046] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0047] In the description of the embodiments of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In this application, "at least one" means one or more, and "more" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0048] The use of prefixes such as "first" and "second" in this application embodiment is solely for distinguishing different descriptive objects and does not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes to distinguish descriptive objects in this application embodiment does not constitute a limitation on the described objects. The description of the described objects is found in the claims or the context of the embodiments, and the use of such prefixes should not constitute unnecessary restrictions.

[0049] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0050] Figure 1 This is a functional schematic diagram of the vehicle 100 provided in the embodiments of this application.

[0051] Vehicle 100 may include multiple subsystems, such as perception system 120 and computing platform 130. Optionally, vehicle 100 may include more or fewer subsystems, and each subsystem may include one or more components. In addition, each subsystem and component of vehicle 100 may be interconnected via wired or wireless means.

[0052] The perception system 120 may include several sensors for sensing information about the environment surrounding the vehicle 100. For example, the perception system 120 may include a positioning system, which may be a Global Positioning System (GPS), a BeiDou system, or another positioning system. The perception system 120 may include one or more of the following: an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device. The perception system 120 is used to acquire environmental perception information, including the location and movement information of traffic participants, which include at least one of the following: vehicles, non-motorized vehicles, pedestrians, animals, obstacles, and traffic facilities.

[0053] Some or all of the functions of vehicle 100 can be controlled by computing platform 130. Computing platform 130 may include processors 131 to 13n (n being a positive integer). A processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a central processing unit (CPU), microprocessor, graphics processing unit (GPU) (which can be understood as a type of microprocessor), or digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. These logical relationships are fixed or reconfigurable. For example, the processor may be a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In reconfigurable hardware circuits, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement some or all of the functions of the aforementioned units. Furthermore, the processor can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), tensor processing unit (TPU), deep learning processing unit (DPU), etc. In addition, the computing platform 130 may also include a memory for storing instructions. Some or all of the processors 131 to 13n can call the instructions in the memory to implement the corresponding functions.

[0054] The computing platform 130 can control the functions of the vehicle 100 based on inputs received from various subsystems (e.g., the sensing system 120). In some embodiments, the computing platform 130 can be used to provide control over many aspects of the vehicle 100 and its subsystems.

[0055] Optionally, the above components are just an example. In actual applications, the components in each of the above modules may be added or deleted as needed.

[0056] The vehicle 100 in this application may include: road vehicles, water vehicles, air vehicles, industrial equipment, agricultural equipment, or entertainment equipment, etc. For example, the mobile device 100 may be a vehicle (such as a commercial vehicle, passenger car, motorcycle, flying car, train, etc.), an industrial vehicle (such as a forklift, trailer, tractor, etc.), an engineering vehicle (such as an excavator, bulldozer, crane, etc.), an agricultural equipment (such as a lawnmower, harvester, etc.), an amusement device, a toy vehicle, etc.; or the mobile device 100 may include a wheeled device, which may be a robot, a mobile medical device, or an experimental platform. The embodiments of this application do not specifically limit the type of mobile device.

[0057] The following uses vehicle 100 as an example of a new energy vehicle to illustrate the technical problems that this application needs to solve and the technical solutions adopted.

[0058] Current lane-level navigation systems only plan based on static road data and do not perceive surrounding traffic participants. This results in guidance that only points you in the right direction but cannot avoid risks. In dynamic scenarios such as obstacles, lane cutting, or congestion, using lane-level navigation can easily cause safety hazards. The lack of early warning affects the practicality and safety of navigation.

[0059] This application provides a vehicle navigation method that integrates environmental perception information to enable the navigation system to dynamically respond to changes in surrounding traffic participants, improve the real-time performance and safety of route planning, and achieve more reliable navigation guidance in scenarios involving obstacles, lane cutting, or congestion, thereby enhancing the effectiveness of driving assistance and user experience.

[0060] See Figure 2 As shown, Figure 2 A schematic diagram illustrating the steps of the vehicle navigation method provided in this application.

[0061] Step S201: Based on map information, vehicle positioning information, and environmental perception information, determine multiple driving routes leading to the vehicle's navigation destination.

[0062] Map information is the foundational data for route planning, providing the structure of the road network and enabling the identification of the road's shape and traffic rules. Map information refers to road attribute and structural relationship data stored in data form. It can be acquired by professional surveying agencies using high-precision data collection vehicles and processed to form a database usable in in-vehicle navigation systems. In this embodiment, map information includes at least the following: the number of lanes, lane line types (e.g., solid lines indicate no crossing, dashed lines indicate lane changing is permitted), lane width, lane function (e.g., straight, left turn, right turn, straight + right turn, bus lane, emergency lane, etc.), road speed limits, the geographical location and control range of traffic signs and signals, the turning connections at intersections (i.e., road topology), road gradient, etc.

[0063] For example, the map information clearly distinguishes the leftmost lane as "left turn + straight", the two middle lanes as "straight", and the rightmost lane as "straight + right turn", and identifies the markings between each lane to determine whether vehicles are allowed to change lanes in this section.

[0064] The map information can originate from a high-precision map database stored locally on-board, or from online navigation data acquired in real-time from a remote cloud map service platform via a wireless communication module (which acquires information from elsewhere using wireless communication technology, such as cellular-based communication or dedicated short-range communications (DSRC). Cellular-based communication includes, for example, Long Term Evolution (LTE), 5th Generation (5G), or more advanced evolution technologies. Cellular-based communication includes vehicle-to-everything (V2X) communication, which includes vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, vehicle-to-pedestrian (V2P) communication, or vehicle-to-network (V2N) communication, etc.). Local map data is stored in the vehicle's controller or storage unit, offering advantages such as low access latency and no network dependence, making it suitable for scenarios with weak satellite signals or communication interruptions, such as tunnels, mountainous areas, and underground passages. Cloud-based maps, on the other hand, have a higher update frequency and wider coverage, and can promptly reflect dynamic changes such as road construction, temporary detours, and new intersections, improving the timeliness and accuracy of navigation.

[0065] Based on the vehicle's current network status, positioning accuracy requirements, and application scenario, the system selects whether to use local map data or combine it with cloud data to ensure the integrity and reliability of map information. For example, before entering a city, high-precision map fragments of that area can be downloaded in advance, and an update request can be automatically sent if the local map version is detected as outdated.

[0066] In practical applications, map information is used to determine the theoretical set of drivable routes for a vehicle under conditions of no external interference. For example, after a driver sets a navigation destination, multiple candidate routes from the current location to the target location are first calculated based on the road connections in the map information.

[0067] Map information reflects the design or planning status of roads and is static data, unable to reflect real-time traffic conditions. Therefore, relying solely on map information for route planning has limitations in complex traffic environments. For example, if an accident or obstacle appears ahead in a lane, even if the map shows the lane as open, it may actually be unsafe to pass. Without combining this with other sensing methods, the system might still recommend that vehicles continue in that lane, potentially leading to drivers facing the risk of emergency braking or forced lane changes.

[0068] Furthermore, in certain special scenarios, such as road closures during large-scale events, sudden road closures, or temporary bridge closures, failure to update map information in a timely manner can also lead to the generation of incorrect navigation instructions. Even if the map data itself is accurate, without real-time awareness of the surrounding environment, the navigation system cannot determine whether a theoretically feasible lane is already occupied or if there is a potential conflict.

[0069] Vehicle positioning information refers to the precise location, direction of travel, and speed of a vehicle in a geodetic or road coordinate system. This information is typically provided by the vehicle's positioning system, including fusion data from a Global Navigation Satellite System (GNSS), an inertial navigation system, and vehicle motion sensors such as wheel speed sensors and gyroscopes.

[0070] In areas with weak satellite signals, such as underground tunnels, under viaducts, or tunnels, the positioning accuracy of a single positioning system may decrease, failing to meet lane-level navigation requirements. Therefore, a fusion positioning scheme that combines global navigation satellite systems, inertial navigation systems, and map matching can be adopted to match the vehicle's trajectory with lane lines in a high-precision map, thereby improving positioning accuracy.

[0071] While navigation systems provide necessary support, the static nature of their data makes it difficult to adapt to changes in dynamic traffic environments. It is necessary to combine map information with the vehicle's own positioning information and environmental perception information from sensors in order to achieve route planning that fits the actual traffic conditions.

[0072] Therefore, this application introduces environmental perception information on the basis of traditional map-based path navigation, which can generate safer and more reasonable driving routes after taking into account factors such as obstacles, the behavior of vehicles in front, and the risk of cutting in.

[0073] Environmental perception information refers to data about the traffic environment around a vehicle that is collected and processed in real time by onboard sensors. It is used to identify dynamic traffic participants and their behavioral states, enabling route planning to perceive traffic flow and avoid risks.

[0074] In this embodiment, the environmental perception information can come from the advanced driving assistance system (ADAS) in the vehicle. The ADAS includes a camera for recognizing lane lines, traffic lights, traffic signs, pedestrians, non-motorized vehicles and other vehicles; millimeter-wave radar for detecting the distance and relative speed of vehicles in front or to the side, especially with strong penetration capability in rainy or foggy weather; lidar for providing high-precision three-dimensional point cloud data for accurately identifying the outline and position of obstacles; and ultrasonic radar for detecting near-range obstacles in low-speed scenarios.

[0075] Among them, environmental perception information includes the location information and action information of traffic participants, and traffic participants include at least one of the following: vehicles, non-motorized vehicles, pedestrians, animals, obstacles, and traffic facilities.

[0076] Traffic participants are various objects in the road environment that may interact spatially with the vehicle or influence the vehicle's driving decisions. These objects include not only entities participating in traffic activities but also other moving or stationary entities that may affect driving safety. Specifically, traffic participants can refer to individuals or objects existing in the environment surrounding the vehicle, detectable by onboard sensors, and potentially influencing the vehicle's driving path, timing of operations, or safety status.

[0077] Among them, the location information of traffic participants refers to the spatial coordinates of traffic participants relative to the vehicle or road coordinate system, including lateral offset (such as being in an adjacent lane), longitudinal distance, and height information (such as being used to distinguish between elevated bridges and under elevated bridges). The location information is used to determine the relative spatial relationship between other traffic elements and the vehicle and to identify potential conflict areas.

[0078] Traffic participant motion information refers to the motion status and behavioral trends of traffic participants, including speed, acceleration, direction of travel, turn signal status, trajectory prediction, etc. For example, radar can detect a vehicle accelerating towards the side and rear, and combined with its turn signal status, it can be determined that the vehicle may cut into the lane; or a camera can identify a pedestrian standing at the edge of an intersection and showing a forward movement, predicting that the pedestrian will soon cross the road.

[0079] For example, if a vehicle is detected stopped in the right lane with pedestrians nearby using a fusion of camera and millimeter-wave radar information, the environmental perception system will mark the vehicle as a "stationary vehicle" and the pedestrians as "pedestrians," and will combine this with map information to determine whether the lane is an emergency lane. If the original plan recommended changing lanes to the right, the route suggestion will be adjusted due to the presence of an obstacle in that lane.

[0080] After acquiring map information, vehicle location information, and environmental perception information, these data can be used as input to generate multiple feasible driving routes from the vehicle's current location to the navigation destination using path search algorithms.

[0081] The navigation destination can be entered by the driver through the in-vehicle human-machine interface to determine the destination's location. By accessing the road topology relationships in the map information, the system determines the connecting path from the current location to the destination. The connecting path describes the connection between roads, including the merging points of main roads and auxiliary roads, turning connections at intersections, ramp entrances and exits, and restrictions such as no-turning rules. Based on this information, all possible routes to the destination are identified. For example, in an urban road environment, there are multiple accessible routes, such as going straight along the main road and then detouring, crossing via auxiliary roads, or using elevated ramps. These routes are considered feasible driving routes.

[0082] Each road-level path can be further decomposed into a driving sequence, which includes the geometry of the lanes, lane line type (e.g., solid lines indicate no lane changing, dashed lines indicate lane changing is allowed), lane function (e.g., straight, left turn, right turn, straight and right turn, bus lane, emergency lane, etc.), lane width, and the start and end range of the lanes. Based on these attributes, the lane position that the vehicle should be in within each road segment is determined, and driving operations are planned, including lane changing, turning, maintaining straight, merging into the main road, or exiting the ramp, etc.

[0083] During the generation of lane-level driving sequences, the feasibility of the path is dynamically evaluated in conjunction with environmental perception information. If a candidate path requires the vehicle to change lanes to the right after 200 meters, but the environmental perception information shows that a vehicle in the right lane is accelerating and approaching, and its lateral movement trend indicates an intention to merge into the vehicle's lane, then the lane change operation is determined to be of high risk at the current moment, and the path can be excluded.

[0084] If the sensor detects a disabled vehicle, construction cone, or other obstacle in a lane ahead, that lane will be marked as a temporarily impassable area, and a driving route that requires passing through that lane will be avoided. For turning maneuvers, if the camera detects severe congestion at the intersection and heavy oncoming straight traffic, resulting in insufficient opportunities to turn left, the path requiring a left turn at that location may be excluded, and a route that can be bypassed through other intersections may be retained instead.

[0085] By integrating static road information (map information, vehicle positioning information) with dynamic traffic conditions (environmental perception information), multiple feasible paths can be generated that both comply with road rules and adapt to the current traffic flow, thus providing a basis for subsequent navigation recommendations.

[0086] Step S202: Determine navigation information based on multiple driving routes leading to the vehicle's navigation destination. The navigation information is used to provide driving suggestions to the driver.

[0087] The system comprehensively evaluates each candidate route using multiple generated feasible routes leading to the vehicle's navigation destination, and determines the navigation information to provide driving advice to the driver based on the evaluation results.

[0088] Navigation information refers to the guidance content used to assist drivers in completing the journey from their current location to their destination. It is a driving suggestion provided to the driver. This information includes not only recommended driving routes, but also dynamic operation prompts, risk warnings, environmental prompts, and vehicle status-related information associated with the route, so that the driver understands the driving behavior that should be taken.

[0089] First, multiple feasible routes can be comprehensively evaluated. Evaluation dimensions include, but are not limited to: total route length, number of lane changes required, turning complexity, distance to obstacles ahead, traffic flow speed matching, collision risk level, driver workload, and route continuity. For example, each route can be calculated with a comprehensive score based on the above factors; the lower the score, the safer and easier the route is to execute in the current traffic environment.

[0090] Based on the scoring results, the optimal driving route is determined from multiple feasible routes as the recommended path. The recommended path is the driving plan that best meets the requirements of safety and operability under the current conditions. For example, among three candidate paths, if a path is shorter but requires two consecutive lane changes and has dense traffic on the right, it will have a higher score; while another path, although a slightly longer detour, only requires one lane change and has stable traffic ahead, and will be selected as the optimal path.

[0091] After determining the recommended route, navigation information is generated based on the recommended route. This information may include: the recommended driving lane, the timing of the operation (such as lane change point, turning point), the operation type (such as "change lane to the left", "keep straight", "prepare to turn right"), the triggering conditions of the operation (such as "start changing lanes when 150 meters from the intersection"), and the operation time window (such as "complete the lane change within the next 20 seconds").

[0092] Furthermore, navigation information also includes dynamic prompts related to the current traffic environment. For example, if the system detects that a vehicle ahead is slowing down, or that a vehicle is rapidly approaching from the side or rear, the navigation information may include warnings such as "Caution: Vehicle approaching from the right" or "Traffic congestion may occur ahead; please maintain a safe distance." This type of information is presented visually, audibly, or tactilely through the in-vehicle human-machine interface, enhancing the driver's awareness of the surrounding environment.

[0093] Navigation information can also be dynamically adjusted based on the vehicle's real-time status. For example, when the vehicle's current speed is lower than the recommended speed, the system can add a "suggest accelerating to smoothly merge into the main road" message to the navigation information; when the vehicle does not follow the recommended route, such as failing to change lanes within the specified distance, a prompt "Missed the opportunity to change lanes, will be replanned at the next exit" can be generated, and subsequent guidance can be updated simultaneously.

[0094] Step S203: Output navigation information through the in-vehicle human-machine interaction device.

[0095] The navigation information is output through the in-vehicle human-machine interaction device to provide the driver with intuitive and timely driving suggestions. This process is used to transform the route planning results and environmental perception information generated in the previous stage into multimodal guidance signals that are easy for the driver to understand, so as to ensure that the driver can accurately perform the recommended operations in complex traffic environments.

[0096] In-vehicle human-machine interaction devices include various information display and prompting devices equipped in the vehicle, specifically including a central control display screen, a full LCD instrument panel, a head-up display (HUD), an augmented reality head-up display (AR-HUD), an in-vehicle audio system, and haptic feedback devices (such as steering wheel vibration). Depending on the vehicle configuration, one or more output methods can be selected to achieve the coordinated presentation of navigation information.

[0097] On the central control display or instrument panel, navigation information is presented in a graphical interface. The displayed content includes not only recommended driving routes, lane change prompts, and turn guidance, but can also be integrated with other vehicle status information. For example, the interface simultaneously displays the current vehicle speed, remaining mileage, estimated arrival time, current speed limit for the current road segment, traffic light status, and the relative positions of surrounding road users. By integrating navigation information into a single view, drivers can obtain comprehensive information without switching interfaces, reducing eye distraction and improving driving safety.

[0098] In vehicles supporting augmented reality head-up displays (HUDs), navigation information can be overlaid onto the windshield area in the driver's field of vision, achieving a seamless integration with the real-world road scene. Using vehicle positioning and environmental perception data, virtual graphical guidance elements are generated on the road surface in front of the driver. For example, recommended lanes can be indicated by projecting virtual lane lines, dynamic light strips, or highlighted borders; floating arrows indicate the turning direction when a turn is needed; and gradient light strips guide the vehicle to the target lane when a lane change is required, thereby improving the intuitiveness and responsiveness of the guidance.

[0099] When a potential risk is detected, the augmented reality head-up display can dynamically adjust its content to enhance the warning effect. For example, if the vehicle in front suddenly brakes, it will be marked with a flashing red border in the AR screen, with the text "Caution: Vehicle in front, slow down" superimposed. If a vehicle is rapidly approaching from the side or rear and poses a risk of cutting in, a yellow warning area can be displayed in the corresponding direction to remind the driver to maintain a safe distance or postpone lane changes. This type of dynamic visual feedback based on environmental perception enables drivers to detect dangers earlier and improves their ability to respond.

[0100] In addition to visual cues, navigation information also includes voice prompts, which are broadcast to the driver via the car's audio system, providing operational suggestions. Voice prompts are prioritized based on information priority: high-priority prompts (such as "Obstacle ahead, please change lanes immediately") are played first to avoid conflict with other prompts; routine guidance (such as "Turn right in 300 meters, please prepare to change lanes to the right") can be broadcast at appropriate times without distracting the driver.

[0101] In specific scenarios, multiple feasible paths can be compared and displayed side-by-side. For example, two candidate routes to the destination can be shown: one is the shortest but requires frequent lane changes and passes through congested sections, marked as "faster but more complex to navigate"; the other is a detour route, longer but with stable lanes and smooth traffic, marked as "smoother but takes longer." Users can choose based on their driving habits or preferences using icons, color coding (e.g., red for high risk, green for low risk), or text descriptions.

[0102] Alternatively, without driver intervention, the system can automatically rotate between multiple feasible routes according to preset time intervals or trigger conditions. Each frame presents only the complete path and corresponding information of one candidate route, such as travel distance, estimated time, number of lane changes, and risk level. For example, it can automatically switch to the next route every few seconds and dynamically display its trajectory on the map in a highlighted manner, allowing the driver to continuously view the overall situation of each route in a short period of time.

[0103] Those skilled in the art will understand that the navigation information output achieved in step S203 is a human-computer interaction process based on the actual vehicle configuration and the current driving scenario. By integrating multiple output methods such as vision and hearing, complex route planning results can be transformed into more intuitive driving suggestions, effectively reducing the driver's recognition difficulty and improving the practicality and safety of the navigation system.

[0104] As one possible implementation, the method further includes: when outputting navigation information on the vehicle-mounted human-machine interaction device, marking the target traffic participants on the display screen of the vehicle-mounted human-machine interaction device, the marking method includes highlighting borders, different color markings, dynamic arrow pointing or text label markings, and the target traffic participants include at least one of the following: vehicles, non-motorized vehicles, pedestrians, animals, obstacles and traffic facilities that affect the driving safety or continuity of the vehicle.

[0105] While outputting navigation information through the in-vehicle human-machine interaction device, this application will visually identify target traffic participants who may affect the driving safety or driving continuity of the vehicle on the display screen to enhance environmental perception and assist the driver in understanding navigation suggestions.

[0106] Target traffic participants refer to surrounding objects that, after assessment, may cause spatial conflicts with the vehicle or affect driving operations. These may include, but are not limited to: vehicles cutting into the vehicle's lane, obstructive vehicles moving slowly in front of the vehicle, non-motorized vehicles approaching from the side, pedestrians preparing to cross the road, animals on the road, and static or dynamic obstacles such as construction cones, disabled vehicles, and temporary roadblocks. Furthermore, certain movable or changing traffic facilities, such as temporary traffic lights and variable message signs, may also be included in the signage scope if their status affects traffic rules.

[0107] In the navigation interface of the vehicle's central control display or instrument panel, specific markers are applied to identified target traffic participants on the map view or camera fusion view. Marking methods include, but are not limited to: overlaying a highlighted border around the target object, pointing the target with a dynamic arrow to guide the driver's attention to its position and movement trend, or adding text labels such as "Vehicles ahead slow down," "Vehicles cutting in front," "Pay attention to pedestrians," etc., to indicate their type and potential impact.

[0108] The triggering of signs is based on environmental perception information and path planning results. For example, in vehicles that support augmented reality head-up displays, the signs of target traffic participants can be projected directly into the driver's field of vision, aligned with the real road scene. For example, a highlighted frame can be displayed around the actual vehicle outline, or a warning triangle can be projected in front of pedestrians to achieve more direct visual guidance.

[0109] See Figure 3A As shown, Figure 3A A diagram illustrating the identification of traffic participants in an in-vehicle human-machine interface. Figure 1 When it is recommended to keep driving in the current lane, but a vehicle is detected occupying part of the lane ahead, that vehicle is identified as a target affecting the continuity of driving. It is then highlighted on the screen along with the vehicle ahead, and the recommended lane to the left is marked.

[0110] See Figure 3B As shown, Figure 3B A diagram illustrating the identification of traffic participants in an in-vehicle human-machine interface. Figure 2 For example, when preparing to change lanes to the left, if the radar detects a vehicle accelerating towards the left and determines that there is a risk of it cutting in, a flashing arrow will be superimposed on the vehicle's location and marked "Vehicle approaching from the left" to remind the driver to postpone the operation.

[0111] This information is displayed along with the navigation guidance, providing a combined indication of route and risk, allowing drivers to see recommended driving routes. For example, if an alternate route is recommended instead of a right turn, and the screen also shows congested vehicles at the intersection, the driver can understand the rationale behind the suggestion.

[0112] By marking key traffic participants that affect the vehicle's driving safety or traffic continuity on the display screen of the in-vehicle human-machine interaction device, this application combines environmental perception information with navigation guidance information, enabling drivers to intuitively identify vehicles, obstacles, or other traffic participants in the surrounding area that may have an impact. The above marking method is based on a comprehensive judgment of the current driving route and the surrounding traffic conditions, which helps drivers to understand key risk points in the road environment in a timely manner, understand the basis for the generation of navigation suggestions, and thus perform driving operations more rationally, thereby improving driving safety and the effectiveness of the navigation system.

[0113] As one possible implementation, multiple driving routes to the vehicle navigation destination are updated based on changes in the status of the target traffic participants.

[0114] In this process, multiple routes leading to the vehicle's navigation destination are dynamically updated based on changes in the status of target traffic participants. This process continuously monitors environmental perception information, identifies changes in the behavior of traffic participants affecting the vehicle's travel path, and reassesses the safety and feasibility of candidate routes accordingly, ensuring that navigation suggestions are adapted to the current traffic environment.

[0115] After generating multiple initial feasible routes, these routes are not fixed. Instead, the target traffic participants are tracked. When sensors collect data and detect changes in the position, speed, direction of movement, or behavior pattern of a target traffic participant, a route update process is triggered. For example, if there is a slow-moving vehicle in the lane ahead of the original planned route, but the vehicle subsequently changes lanes and the original lane becomes clear again, the traffic conditions of the route are considered to have improved, and its priority may be increased or it may be added to the set of feasible routes.

[0116] For example, if the original recommended route is to continue straight in the current lane, and the perception information shows no obvious obstacles ahead, but then a disabled vehicle suddenly stops in the lane, the vehicle is immediately identified as a new target traffic participant and determined to be obstructing the current route. In this case, route planning is re-executed, routes that need to pass through the lane are excluded, and new candidate routes are generated, such as detours via the left or right lanes.

[0117] See Figure 4A As shown, Figure 4A A diagram illustrating the dynamic update of driving routes based on changes in the state of traffic participants. Figure 1 . Figure 4A The image shows a vehicle traveling in the middle lane of a city main road. Three candidate paths were initially planned: Path A is to maintain the current lane and continue straight; Path B is to change lanes to the right and then turn right. Currently, there are non-motorized vehicles traveling in the right lane, so Path A is the preferred option.

[0118] See Figure 4B As shown, Figure 4B A diagram illustrating the dynamic update of driving routes based on changes in the state of traffic participants. Figure 2 Subsequently, the camera and radar detected a vehicle stopped in the middle lane due to a malfunction, occupying the space of the straight lane. The vehicle was identified as a new target traffic participant, and route A was determined to be no longer safe and feasible. Route A and route B were re-evaluated. If the non-motorized vehicles on the right had already passed, route B was updated as the recommended route, and the information was simultaneously refreshed and displayed on the human-machine interface.

[0119] The path update process includes retrieving map information, location information, and the latest environmental perception data, and re-evaluating factors such as the number of lane changes, distance to obstacles, and traffic flow matching for each path.

[0120] Those skilled in the art will understand that, under the existing ADAS system architecture, the method provided in this application can quickly complete perception decision updates, meet the response requirements of actual driving scenarios, and provide safer and more flexible navigation suggestions by adjusting candidate paths in real time according to changes in the state of target traffic participants, thereby improving the adaptability and practicality of the driver assistance system.

[0121] As one possible implementation, the method further includes: conducting collision risk assessment and driver operation complexity assessment on multiple driving routes leading to the vehicle navigation destination; determining the target driving route from the multiple driving routes leading to the vehicle navigation destination based on the assessment results; and determining navigation information based on the target driving route.

[0122] Collision risk assessment and driver operation complexity assessment are performed on the multiple feasible driving routes to the vehicle navigation destination generated in step S201. This process is based on map information, vehicle positioning information and environmental perception information, combined with the current traffic status and vehicle driving intention, to analyze the safety and feasibility of each candidate route.

[0123] Collision risk assessment refers to predicting the likelihood of a vehicle colliding with surrounding vehicles, non-motorized vehicles, pedestrians, or other obstacles along a given route based on the position, speed, acceleration, and trajectory of traffic participants in environmental perception information. It calculates the collision risk value for each route at key nodes (such as lane change points, merging points, and intersections), using parameters such as time-to-collision time, minimum lateral / longitudinal distance, and relative speed for a comprehensive score. For example, a route requiring a right lane change under conditions of dense and rapidly approaching vehicles on the right will have a higher collision risk score; conversely, if the target lane is empty and there is sufficient distance between vehicles, the risk score will be lower.

[0124] Driver operational complexity assessment measures the operational burden required to execute a route. Assessment factors include: number of lane changes, consecutive lane change requirements, turning angle, timing requirements for merging into the main road, number of traffic signal waits, and route continuity. For example, a route that requires two consecutive lane changes and weaving through traffic within a short period of time has a higher operational complexity than a route that only requires one lane change and has smooth traffic flow.

[0125] By normalizing the collision risk score and operational complexity score, and combining them with preset weighting coefficients, a comprehensive evaluation score is calculated for each route. Adjustments are made based on driving mode (e.g., Comfort mode emphasizes low risk, while Sport mode allows for higher complexity) or user preferences. Based on the comprehensive score, the optimal driving route is selected from multiple candidate routes as the target driving route.

[0126] The target driving route is a recommended route that is safe, has a reasonable operating burden, and meets the requirements of the navigation destination under the current traffic conditions. Based on this route, corresponding navigation information is generated, including recommended lanes, lane change or turning prompts, operation timing suggestions, and related risk warnings.

[0127] As one possible implementation, if the environmental perception information includes the detection of other vehicles within the turning path, the navigation information includes turning prompt information, which is used to provide the driver with suggested turning lanes and safe turning times.

[0128] When environmental perception information includes the detection of other vehicles within the turning path, navigation information includes turning prompts that provide the driver with suggested turning lanes and safe turning times to improve the safety and efficiency of turning maneuvers.

[0129] When a vehicle approaches an intersection and prepares to make a left or right turn, the turning path at the current intersection is identified based on map information, and the status of the target turning lane and the oncoming and lateral traffic flows is determined in conjunction with environmental perception information. If the sensors detect other vehicles in the turning path, such as oncoming straight-ahead vehicles not yet having passed, non-motorized vehicles crossing in the lateral lane, or the target lane being occupied by the vehicle in front, this situation is included in the path feasibility assessment.

[0130] At this point, instead of directly issuing an immediate turn command, the system generates turn prompts based on the detection results, including timing considerations. For example, when it detects dense oncoming traffic and insufficient clearance to complete a left turn, the navigation information may include prompts such as "Please wait for the oncoming traffic to stop before turning left" or "There is currently no safe clearance; do not turn." Simultaneously, it is recommended that drivers remain in the left-turn waiting area or dedicated left-turn lane to avoid forcibly cutting in and causing a conflict.

[0131] See Figure 5 As shown, Figure 5 A schematic diagram is generated for turning prompts based on environment perception. Figure 5 The vehicle is currently on a curve and preparing to make a right turn. There is a vehicle in the lane ahead. Cameras and radar have detected traffic conditions in the lanes ahead and to the right, identifying other vehicles in the right-turn path that may affect a safe turn.

[0132] Based on sensor data, the distance to the vehicle ahead in the current lane is relatively short. Turning right immediately could pose a rear-end collision risk, especially on curves where visibility is limited and road curvature makes advance prediction and maintaining a safe distance crucial. Considering the presence of vehicles in the current lane, a direct right turn is complex, requiring the driver to control speed and steering angle to avoid collisions. Furthermore, when turning right on a curve, centrifugal force and road surface adhesion must be considered to ensure vehicle stability and safety. Based on the above assessment, turning prompts are generated, specifically as follows: Text prompt: "Please maintain your current lane, pay attention to vehicles ahead, and wait for vehicles in the right lane to pass before safely changing lanes." Visual prompt: Display a highlighted box indicating the vehicle ahead on the in-vehicle display or AR-HUD, labeled "Vehicle Ahead." Voice prompt: Issue a voice reminder: "Please note that there is a vehicle ahead. Please slow down and maintain a safe distance, and wait for vehicles in the right lane to pass before turning right."

[0133] In addition, if there are multiple turning routes (such as turning through different lanes), and one of the routes is blocked due to other vehicles, the unoccupied lane will be recommended first, and the navigation information will clearly indicate which lane to use to complete the turn.

[0134] The method provided in this application can generate turning prompts adapted to the actual traffic environment based on the intersection topology provided by high-precision maps, vehicle positioning information, and continuous monitoring data of surrounding traffic participants. This enables the assessment of turning path availability and safe timing, and when other vehicles or obstacles are detected within the turning path, it can promptly provide drivers with suggested turning lanes and safe operating times, avoiding traffic conflicts or driving misjudgments caused by improper guidance. Especially in high-risk scenarios such as unprotected left turns, complex intersections, or multi-lane turns, this method improves the accuracy and safety of navigation guidance.

[0135] As one possible implementation, when the environmental perception information includes the detection of a vehicle ahead slowing down or braking, the navigation information includes a deceleration prompt, which is used to provide the driver with a suggested deceleration range and timing.

[0136] The vehicle continuously monitors the road environment ahead using onboard sensors, acquiring information on the movement of vehicles ahead, including their speed, acceleration, and relative distance. When the environmental perception module detects a vehicle in the same lane ahead exhibiting a significant deceleration trend or braking behavior such as brake lights illuminating, it determines a potential rear-end collision risk or a change in traffic speed, thereby triggering a deceleration warning mechanism. This judgment can be based on continuous detection results across multiple frames, avoiding false warnings or missed warnings due to momentary misdetections.

[0137] After confirming that the vehicle ahead is indeed slowing down, the system calculates the recommended deceleration range and optimal deceleration timing based on the vehicle's current speed, relative distance to the vehicle ahead, road speed limits, and curvature information (such as curves and slopes). For example, if the vehicle is traveling at 110 km / h and the vehicle ahead begins braking at 300 meters, and it is determined that maintaining the current speed might result in an excessively close following distance, a deceleration warning is generated, suggesting that the driver gradually reduce speed within a range of 150 to 200 meters to achieve a smooth following distance.

[0138] See Figure 6 As shown, Figure 6 A schematic diagram is generated for a deceleration warning based on the deceleration behavior of the vehicle in front. Figure 6 The image shows the vehicle traveling along a straight road. A vehicle in the same lane ahead is braking and decelerating, its brake lights are illuminated. Radar and cameras detect that the vehicle's speed is gradually decreasing and the relative distance is gradually shortening, thus determining that it is in a state of continuous deceleration.

[0139] The recommended deceleration zone is graphically marked on the in-vehicle human-machine interface. For example, the road segment from the current location to 150 to 200 meters is highlighted on the navigation map, with the text "Recommended Deceleration" superimposed. Simultaneously, a prompt to the driver to begin gently applying the brakes is displayed on the instrument panel or AR-HUD. A voice prompt is simultaneously broadcast: "The vehicle ahead is slowing down; please decelerate smoothly to maintain a safe following distance."

[0140] Furthermore, if the vehicle ahead slows down in special road sections such as curves, ramp entrances, or construction zones, the alert priority will be further enhanced by combining map information. For example, if braking by the vehicle ahead is detected before entering a curve, in addition to prompting deceleration, supplementary information such as "Slow down before the curve, and maintain a safe distance" may be added.

[0141] The method provided in this application can integrate environmental perception information with vehicle driving status. When the vehicle in front is detected to be decelerating or braking, a navigation prompt containing a suggested deceleration range and timing can be generated in a timely manner. This method helps the driver to take braking action in advance, avoid emergency braking, reduce the risk of rear-end collisions, and improve driving comfort and safety.

[0142] As one possible implementation, when the environmental perception information includes detecting that a vehicle ahead is accelerating and increasing its distance, the navigation information includes acceleration prompts, which are used to provide the driver with suggested acceleration ranges and timing.

[0143] The vehicle continuously monitors the relative motion between itself and other vehicles in the same lane ahead using onboard sensors, acquiring information on their speed, acceleration, and relative distance. When the environmental perception module detects that a vehicle ahead begins to accelerate and the relative distance between them continues to increase beyond the normal following distance range, it determines that there is usable space ahead on the road. Based on this, and combined with the vehicle's current speed, road speed limit, overall traffic flow speed, and road attributes in the map information (such as straight roads, slopes, or main roads after exit ramps), it determines whether safe acceleration conditions are met.

[0144] After confirming the feasibility of accelerating ahead, the system calculates the suggested acceleration range and recommended acceleration timing. For example, if the vehicle is currently traveling at 60 km / h, and the vehicle ahead accelerates from 60 km / h to 110 km / h and gradually increases the distance, and the speed limit is 110 km / h, it determines that the vehicle can increase its speed while maintaining a safe following distance. In this case, an acceleration prompt is generated, suggesting that the driver accelerate smoothly within a distance of 200 to 300 meters from the vehicle ahead.

[0145] See Figure 7 As shown, Figure 7 A schematic diagram is generated for acceleration prompts based on the acceleration behavior of the vehicle in front. Figure 7 The image shows the vehicle traveling on an urban expressway. A vehicle in the same lane ahead has begun to accelerate, increasing its speed and gradually increasing the distance between it and the vehicle ahead. The radar and vision system continuously track the vehicle's movement, confirming that it is in a stable acceleration state and that there are no other obstacles on the road ahead.

[0146] The in-vehicle human-machine interface displays the suggested acceleration range graphically. For example, on the navigation map, the section of road from the current location to 200 to 300 meters is highlighted, with the text prompts "Smooth acceleration is possible" or "Acceleration recommended." Simultaneously, the instrument panel or AR-HUD displays a message indicating that the throttle can be increased appropriately. A voice prompt simultaneously announces: "The distance to the vehicle ahead has increased; the road is clear. Acceleration is recommended to maintain a proper following distance."

[0147] Furthermore, if the acceleration of the vehicle ahead occurs after merging onto the main road, exiting a tunnel, or in a congestion-relief zone, the prompt strategy can be further optimized by incorporating map information. For example, if the vehicle ahead is detected accelerating after exiting a tunnel, in addition to prompting acceleration, a voice explanation such as "Exited the tunnel, road conditions are good, acceleration is recommended" can be added.

[0148] The method provided in this application integrates environmental perception information and road condition data. When it detects that a vehicle ahead is accelerating and increasing its distance, it generates navigation prompts that include suggested acceleration ranges and timing. This helps drivers seize opportunities to pass through traffic in a timely manner, avoid prolonged low-speed driving, and improve traffic efficiency and driving experience. It enhances the proactive guidance capability of the navigation system, especially in scenarios such as traffic flow recovery, driving on main roads, or accelerating at exit ramps.

[0149] As one possible implementation, when the environmental perception information includes the detection of congestion on the road ahead, the navigation information includes lane change guidance prompts, which are used to suggest to the driver the timing of a lane change and the target lane.

[0150] The vehicle continuously monitors traffic flow ahead using onboard sensors, acquiring information on vehicles in the same and adjacent lanes, including speed, distance, acceleration, and queue length. When the environmental perception module detects that vehicles ahead in the vehicle's lane are slowing down, densely packed, and their average speed is below a preset threshold for a sustained period (e.g., over 300 meters), it determines that the lane is congested. Simultaneously, the traffic conditions in the adjacent lanes to the left and right are assessed to determine if there are any relatively smooth reversible lanes available.

[0151] After confirming that the lane ahead of the vehicle is congested and that adjacent lanes are passable, the system calculates a recommended target lane and suggested lane-changing timing based on the lane topology, lane function attributes (such as whether it is a bus lane, emergency lane, or a solid line prohibiting lane changes), current vehicle speed, and relative position to surrounding vehicles, combined with information from the map. For example, if the distance between vehicles in the left lane is large, the average speed is high, and the lane markings allow lane changes, the system identifies the left lane as the recommended target lane and determines a safe lane-changing window based on the speed of vehicles approaching from behind.

[0152] See Figure 8 As shown, Figure 8 A schematic diagram is generated to provide lane change guidance based on the traffic congestion ahead. Figure 8 The image shows the vehicle traveling in the middle lane of a main urban road. The vehicles in the same lane ahead are densely packed, and the radar detects that the vehicles ahead are traveling at a very low speed and there is a certain length of queue. The vehicles in the left lane are spaced far apart, and the lane lines are dashed, allowing lane changing.

[0153] On the display interface of the in-vehicle human-machine interface, the right lane is highlighted as the recommended target lane, and an arrow is superimposed on the navigation path to indicate the direction of lane change. Simultaneously, the suggested lane change section is displayed in the map view; for example, a lane change to the right from the current location is indicated with the text "Suggested lane change to the right". In the AR-HUD, a dynamic light strip can be projected to guide the vehicle to move to the right, with a voice prompt simultaneously broadcasting: "Congestion ahead, suggested lane change to the right, target lane is clear."

[0154] Furthermore, if potential congestion points or traffic restrictions (such as during bus-only hours) are detected ahead of the target lane, that lane will not be recommended. Lane change guidance prompts are generated only if they meet safety, compliance, and effectively improve traffic efficiency, avoiding misleading drivers into risky areas.

[0155] The method provided in this application can identify the congestion status of the road ahead based on environmental perception information, and dynamically generate lane change guidance prompts in combination with the traffic capacity of adjacent lanes. Under the premise of ensuring driving safety, it can provide drivers with reasonable lane change suggestions and help them actively avoid congestion.

[0156] As one possible implementation, when the environmental perception information includes the presence of vehicles with collision risk in the vehicle's lane or adjacent lanes, the navigation information includes driving prompts, which are used to suggest driving routes and avoidance routes to the driver.

[0157] By fusing data from environmental perception devices such as cameras, millimeter-wave radar, and ultrasonic sensors, the system continuously monitors the dynamic information of traffic participants around the vehicle, including their position, speed, acceleration, direction of travel, and trajectory prediction. When abnormal behavior is detected in a vehicle in front of or to the side of the vehicle, such as sudden deceleration, lane departure, acceleration to cut in, or a tendency to cut in, and the calculated time to collision is less than a preset threshold, and the minimum lateral or longitudinal distance is less than a safety threshold, the vehicle is determined to be a target traffic participant with collision risk.

[0158] After confirming a collision risk, the system assesses feasible driving strategies based on the lane-level topology provided by the high-precision map, the vehicle's current driving status (such as speed and steering angle), and the traffic conditions of adjacent lanes. If there is a vehicle braking suddenly or a stationary obstacle ahead in the lane where the vehicle is located, it is recommended to maintain the current lane and prepare to decelerate or stop as the primary driving route. At the same time, if the left or right lane meets the lane change conditions (such as the lane line is dashed, there are no high-risk vehicles in the target lane, and there is sufficient distance from vehicles approaching from behind), the lane is identified as an optional avoidance route, and corresponding driving prompts are generated.

[0159] See Figure 9 As shown, Figure 9A schematic diagram for generating driving prompts based on collision risk identification. Figure 9 The image shows the vehicle is traveling in the middle lane. A vehicle ahead in the same lane is moving slowly due to a malfunction and is occupying part of the lane space, posing a direct collision risk. Meanwhile, traffic in the left lane is smooth with no close-range vehicles, allowing for a safe lane change. However, a vehicle in the right lane is accelerating towards the vehicle, posing a risk of cutting in, and therefore does not meet the requirements for a safe lane change.

[0160] On the display of the in-vehicle human-machine interface, a low-speed vehicle with a malfunction ahead is highlighted with a "Collision Risk Ahead" text label overlaid. Simultaneously, the current recommended route (i.e., the main route) is marked with a solid line on the navigation path, while a dashed arrow points to the left lane, indicating the recommended avoidance route. In the AR-HUD, a light strip can be projected to guide the vehicle to change lanes to the left. A voice prompt simultaneously announces: "A vehicle ahead is traveling at low speed, posing a collision risk. It is recommended to maintain a safe distance and change lanes to the left to avoid collision."

[0161] For high-risk scenarios (such as a vehicle braking suddenly in front), the priority is to prompt the driver to slow down or stop; for low-risk scenarios (such as a vehicle approaching from the side), the primary focus is on warnings, prompting the driver to maintain the current route and be alert to the risk of changing lanes.

[0162] The method provided in this application embodiment can comprehensively assess the environmental conditions and available paths when a vehicle with collision risk is detected in the driver's lane or adjacent lane, and generate navigation information including suggested driving routes and avoidance routes, thereby enhancing the driver's awareness of potential dangers and helping to avoid traffic accidents.

[0163] Based on the same concept, this application provides a vehicle navigation device 1000, see reference. Figure 10 As shown, Figure 10 This is a schematic diagram of the structure of a vehicle navigation device.

[0164] The vehicle navigation device 1000 includes:

[0165] The route determination module 1001 is used to determine multiple routes to the vehicle's navigation destination based on map information, vehicle positioning information, and environmental perception information.

[0166] The navigation information determination module 1002 is used to determine navigation information based on multiple driving routes leading to the vehicle's navigation destination. The navigation information is used to provide driving suggestions to the driver.

[0167] The navigation display module 1003 is used to output navigation information through the vehicle-mounted human-machine interaction device.

[0168] Figure 11 This is a schematic diagram of another control device 1100 provided in an embodiment of this application.

[0169] The device 1100 includes a memory 1110, a processor 1120, and a communication interface 1130. The memory 1110, processor 1120, and communication interface 1130 are connected via an internal connection path. The memory 1110 stores instructions, and the processor 1120 executes the instructions stored in the memory 1110 to control the communication interface 1130 to acquire information, thereby enabling the device 1100 to implement the aforementioned control method. Optionally, the memory 1110 can be coupled to the processor 1120 via an interface, or it can be integrated with the processor 1120.

[0170] It should be noted that the communication interface 1130 described above uses a transceiver device, such as, but not limited to, a transceiver. The communication interface 1130 may also include an input / output interface.

[0171] The processor 1120 stores one or more computer programs, which include instructions. When the instructions are executed by the processor 1120, the control device 1100 performs the control methods described in the above embodiments.

[0172] In implementation, each step of the above method can be completed by the integrated logic circuitry of the hardware in the processor 1120 or by instructions in software form. The method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 1110, and the processor 1120 reads the information in memory 1110 and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0173] As one possible implementation, the control device 1100 can be a physical device, such as including one or more of the following modules: central processing unit (CPU), microprocessor unit (MPU), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), complex programmable logic device (CPLD), coprocessor (assisting the central processing unit in completing corresponding processing and applications), microcontroller unit (MCU), domain controller (DC), vehicle domain controller (VDC), electronic control unit (ECU), cockpit domain controller (CDC), vehicle integration unit (VIU), vehicle control unit (VCU), motor control unit (MCU), etc. Furthermore, the control device 1100 includes at least one processor integrated in the form of a system-on-chip (SOC), commonly referred to as an SOC by those skilled in the art. The SOC may include at least one processor, and when the SOC includes multiple processors, the types of processors may be different.

[0174] Optionally, the device 1000 or device 1100 may be located in Figure 1 Of the 100 vehicles in the list.

[0175] Optionally, the device 1000 or device 1100 can be Figure 1 The computing platform 130 in the vehicle.

[0176] This application also provides a computer-readable storage medium storing program code that, when run on a computer, causes the computer to perform any of the methods described in the above embodiments.

[0177] This application also provides a computer program product, which includes a computer program that, when run, causes the computer to perform any of the methods described in the above embodiments.

[0178] This application also provides a chip, including: a circuit for performing any of the methods in the above embodiments.

[0179] This application also provides a vehicle, including, as in the embodiments described above. Figure 10 or Figure 11 Any of the control devices shown.

[0180] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0181] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0182] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0185] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] It should be noted that the personal information and data processing (e.g., collection, storage, use, processing, transmission, provision and disclosure) involved in this application that are protected by the laws and regulations of the relevant countries and regions comply with the relevant laws and regulations of the relevant countries and regions.

[0187] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle navigation method, characterized in that, The method includes: Based on map information, vehicle location information, and environmental perception information, multiple driving routes leading to the vehicle's navigation destination are determined. Navigation information is determined based on the multiple driving routes leading to the vehicle's navigation destination, and the navigation information is used to provide driving suggestions to the driver; The navigation information is output through the in-vehicle human-machine interaction device.

2. The vehicle navigation method according to claim 1, characterized in that, The environmental perception information includes the location and action information of traffic participants, and the traffic participants include at least one of the following: vehicles, non-motorized vehicles, pedestrians, animals, obstacles, and traffic facilities.

3. The vehicle navigation method according to claim 2, characterized in that, The method further includes: When the navigation information is output on the vehicle-mounted human-machine interaction device, the target traffic participants are marked on the display screen of the vehicle-mounted human-machine interaction device. The marking method includes highlighting borders, different color markings, dynamic arrow pointing, or text label markings. The target traffic participants include at least one of the following: vehicles, non-motorized vehicles, pedestrians, animals, obstacles, and traffic facilities that affect the driving safety or continuity of the vehicle.

4. The vehicle navigation method according to claim 3, characterized in that, The multiple driving routes leading to the vehicle navigation destination are updated based on changes in the status of the target traffic participants.

5. The vehicle navigation method according to any one of claims 1-4, characterized in that, The method further includes: Collision risk assessment and driver operation complexity assessment are performed on the multiple driving routes leading to the vehicle navigation destination; Based on the evaluation results, the target driving route is determined from the multiple driving routes leading to the vehicle navigation destination; The navigation information is determined based on the target driving route.

6. The vehicle navigation method according to any one of claims 1-5, characterized in that, If the environmental perception information includes the detection of other vehicles within the turning path, the navigation information includes turning prompt information, which is used to provide the driver with suggested turning lanes and safe turning times.

7. The vehicle navigation method according to any one of claims 1-6, characterized in that, When the environmental perception information includes the detection of a vehicle ahead slowing down or braking, the navigation information includes a deceleration prompt, which is used to provide the driver with a suggested deceleration range and timing.

8. The vehicle navigation method according to any one of claims 1-7, characterized in that, When the environmental perception information includes detecting that a vehicle ahead is accelerating and increasing its distance, the navigation information includes an acceleration prompt, which is used to provide the driver with a suggested acceleration range and timing.

9. The vehicle navigation method according to any one of claims 1-8, characterized in that, When the environmental perception information includes the detection of congestion on the road ahead, the navigation information includes lane change guidance prompts, which are used to suggest to the driver the timing of a lane change and the target lane.

10. The vehicle navigation method according to any one of claims 1-9, characterized in that, When the environmental perception information includes the presence of a vehicle with a collision risk in the vehicle's lane or an adjacent lane, the navigation information includes driving prompts, which are used to suggest driving routes and avoidance routes to the driver.

11. The vehicle navigation method according to any one of claims 1-10, characterized in that, The navigation information is output through the in-vehicle human-machine interaction device, specifically including: The in-vehicle human-machine interaction device is configured to display driving suggestions graphically in front of the driver's field of vision or on the vehicle's central control display device. The graphical method includes displaying lane boundaries, traffic participant identification, predicted trajectories of traffic participants, and driving suggestion identification indicating lane changing, turning, or deceleration.

12. The vehicle navigation method according to any one of claims 1-11, characterized in that, The environmental perception information is used to determine based on data collected from one or more of the following: cameras, lidar, millimeter-wave radar, or infrared sensors.

13. A vehicle navigation device, characterized in that, The vehicle navigation device includes: The route determination module is used to determine multiple routes to the vehicle's navigation destination based on map information, vehicle positioning information, and environmental perception information. The navigation information determination module is used to determine navigation information based on the multiple driving routes leading to the vehicle's navigation destination, and the navigation information is used to provide driving suggestions to the driver. The navigation display module is used to output the navigation information through the in-vehicle human-machine interaction device.

14. A control device, characterized in that, The control device includes: Memory is used to store programs, instructions, or code; A processor for executing programs, instructions, or code in the memory to perform the vehicle navigation method as described in any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed, implement the vehicle navigation method as described in any one of claims 1 to 12.

16. A computer program product, characterized in that, It includes computer program code that, when run on a computer, causes the computer to perform the vehicle navigation method as described in any one of claims 1 to 12.

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