Scenic area unmanned sightseeing vehicle capable of automatically planning curve path

Abstract

The invention provides a scenic spot unmanned sightseeing vehicle capable of automatically planning a curve path, which comprises a vehicle body, a data acquisition module, a data analysis module and an execution module, and is characterized in that the data acquisition module is used for acquiring vehicle state parameters, obstacle state parameters and map data; the data analysis module generates result data according to the vehicle state parameters and the obstacle state parameters; the execution module controls the vehicle to act according to the result data; a virtual boundary line is delimited based on a satellite map, a strip-shaped initial path is generated in combination with the vehicle body width and the safety distance, obstacle states are distinguished, time axis prediction collision points are added for moving obstacles, tire pressure data and battery remaining factor correction paths are introduced, the turning efficiency is improved, the tire burst risk is reduced, and the endurance is prolonged.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology for unmanned sightseeing vehicles, and in particular to an unmanned sightseeing vehicle for scenic areas that can automatically plan curved paths. Background Technology

[0002] With the booming development of the tourism industry, driverless sightseeing vehicles in scenic areas are gradually entering people's field of vision as an emerging mode of transportation. Driverless sightseeing vehicles refer to sightseeing vehicles that do not require on-site driver control, relying on pre-set programs, sensors, and advanced navigation systems to achieve autonomous driving. They integrate multiple cutting-edge technologies such as autonomous driving, environmental perception, and communication, and can automatically complete a series of tasks such as passenger transport, driving, and obstacle avoidance according to preset routes or real-time environmental information, providing tourists with a convenient and efficient sightseeing experience.

[0003] The experience of riding the driverless sightseeing vehicles in the scenic area differs from that of traditional sightseeing vehicles. Tourists don't need to wait at fixed stops; they can simply make a reservation via a mobile app or designated equipment within the scenic area. The vehicle will then automatically plan its route based on the tourist's location and destination and come to pick them up. Once on board, tourists can comfortably sit and enjoy the scenery along the way, without worrying about driving, allowing them to fully immerse themselves in the sightseeing experience.

[0004] However, the following problems still exist in the path planning of unmanned sightseeing vehicles in scenic areas when driving on curves: In scenic environments, the shape, curvature and surrounding environment of curves are complex and diverse. When faced with these situations, the existing unmanned sightseeing vehicles generally prefer to decelerate the vehicle and then detour around obstacles, without considering the vehicle and curve conditions, resulting in low efficiency in passing curves. Summary of the Invention

[0005] In view of this, the present invention proposes an unmanned sightseeing vehicle for scenic areas that can automatically plan curved paths. Based on satellite maps, virtual boundary lines are delineated, and a strip-shaped initial path is generated by combining the vehicle width and safety distance. The obstacle status is distinguished, and a time axis is added to predict the collision point for moving obstacles. Tire pressure data and battery remaining factors are introduced to correct the path, thereby improving cornering efficiency, reducing the risk of tire blowout, and extending the range.

[0006] The technical solution of this invention is implemented as follows:

[0007] An unmanned sightseeing vehicle for scenic areas that can automatically plan curved paths includes a vehicle body, a data acquisition module, a data analysis module, a data storage module, and an execution module;

[0008] The data acquisition module is used to acquire curve map data, obstacle data, and vehicle data;

[0009] The data analysis module is used to process the curve map data, generate an initial path, and re-plan the target path based on the obstacle data and the initial path.

[0010] The data storage module is used to store the target path, process the historical data of the target path, correct the target path, and generate an optimized path.

[0011] The execution module controls vehicle movements based on the optimized path.

[0012] Preferably, the data acquisition module acquires curve map data through the following specific steps:

[0013] Step S11: Obtain satellite map data, generate curve data, and draw virtual boundary lines on both sides of the curve data;

[0014] Step S12: Generate an initial path based on the virtual boundary lines on both sides, the vehicle width, the safety distance, and the vehicle's minimum turning radius. The initial path is a strip of preset width, and the width of the strip is equal to the sum of the vehicle width and the safety distance.

[0015] Preferably, the data acquisition module acquires obstacle data through the following specific steps:

[0016] Step S21: The obstacle data includes position coordinates, geometric dimensions, velocity magnitude, and direction;

[0017] Step S22: Calculate the distance between the obstacle and the virtual boundary lines on both sides.

[0018] Preferably, the specific steps of the data analysis module in analyzing obstacle data and replanning the path to generate a new path are as follows:

[0019] Step S31: Determine whether the obstacle is stationary or moving based on the obstacle data. If the obstacle is stationary, replan the avoidance paths on both sides of the obstacle.

[0020] Step S32: If the obstacle is in motion, generate the obstacle's trajectory based on the obstacle's position and velocity parameters;

[0021] Step S33: Determine whether the obstacle's trajectory intersects with the initial path. If they intersect, add a time parameter for comprehensive analysis. When the obstacle is at the intersection point, if the distance between the vehicle body and the intersection point is greater than a threshold, maintain the initial path; if the distance between the vehicle body and the intersection point is less than the threshold, replan a new path.

[0022] Preferably, the specific steps for the data analysis module to optimize and correct the new path are as follows:

[0023] Step S41: Obtain tire pressure monitoring data for each tire of the vehicle in real time;

[0024] Step S42: Determine if there are any tires with tire pressure values ​​lower than a preset threshold;

[0025] Step S43: If there is a tire with abnormal tire pressure, the obstacle avoidance path on the same side as the abnormal tire is determined as the priority passage path.

[0026] Preferably, in step S43, after selecting the priority passage path, the vehicle speed is adjusted according to the tire pressure.

[0027] Preferably, in step S42, when all tire pressure values ​​are greater than a preset threshold, the following steps are performed:

[0028] Step S42a: Obtain the remaining battery capacity data of the vehicle and assess the driving range of the vehicle.

[0029] Step S42b: Evaluate the vehicle's remaining driving range based on the battery's remaining data, and select the shortest path inside the curve as the preferred path.

[0030] Preferably, the data storage module is configured to store the optimized path process data, analyze the location and probability of obstacles based on historical data, generate an obstacle probability heatmap, and select the path that avoids the location of obstacles with a high probability of occurrence as the preferred path.

[0031] Preferably, the data storage module obtains the probability of obstacle occurrence based on historical data. When the probability is greater than 60%, the vehicle speed is reduced to 30% of the original cornering speed to compensate for the speed on a flat road, ensuring timely arrival at the destination.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] Virtual boundary lines are drawn based on satellite maps to prevent path planning from deviating from the drivable area. A strip-shaped initial path is generated by combining vehicle width and safety distance. The status of obstacles is distinguished, and a time axis is added to predict the collision point for moving obstacles. Tire pressure data and battery balance factors are introduced to correct the path. Historical obstacle data is stored, and the location and probability of obstacle appearance are analyzed based on historical data to generate an obstacle location probability heat map. Avoiding the location of obstacles with a high probability of appearance is the preferred path, reducing the number of emergency brakings, improving traffic flow, improving cornering efficiency, reducing the risk of tire blowouts, and extending the driving range. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the structure of an unmanned sightseeing vehicle for scenic areas that can automatically plan curved paths according to the present invention.

[0036] Figure 2 This is a flowchart illustrating the steps of the data acquisition module of the present invention in acquiring curve map data.

[0037] Figure 3 This is a flowchart of the steps for the data acquisition module of the present invention to acquire obstacle data;

[0038] Figure 4 A new path step flowchart is generated for the data analysis module of this invention;

[0039] Figure 5 The data analysis module of this invention has been optimized and the new path step flowchart has been revised.

[0040] Figure 6 This is a flowchart of step S42 of the present invention; Detailed Implementation

[0041] To better understand the technical content of this invention, a specific embodiment is provided below, and the invention will be further described in conjunction with the accompanying drawings.

[0042] See Figures 1 to 6 The present invention provides an unmanned sightseeing vehicle for scenic areas that can automatically plan curved paths, including a vehicle body, a data acquisition module, a data analysis module, a data storage module, and an execution module;

[0043] The data acquisition module is used to acquire curve map data, obstacle data, and vehicle data;

[0044] The data analysis module is used to process the curve map data, generate an initial path, and re-plan the target path based on the obstacle data and the initial path.

[0045] The data storage module is used to store the target path, process the historical data of the target path, correct the target path, and generate an optimized path.

[0046] The execution module controls vehicle movements based on the optimized path.

[0047] The data acquisition module includes: a GPS / BeiDou module to acquire map data and vehicle position; cameras and radar to acquire obstacle status parameters: position coordinates, geometric dimensions, speed, and direction; tire pressure sensors to acquire tire pressure monitoring data for each tire; a battery management system to acquire battery level data; and wheel speed sensors to detect the vehicle's speed.

[0048] The data analysis module processes vehicle status parameters, obstacle status parameters, and map data to generate path data;

[0049] The data storage module is used for real-time path data archiving, storing the path point sequence output for each planning, associating timestamps, vehicle IDs, and planning algorithm versions to process historical data, and correcting path result data;

[0050] The execution module controls the vehicle's actions based on the result data. The unmanned vehicle uses an electronic steering system. The execution module generates control commands based on the path result data. The control commands include a coordinate point sequence, commands to control the electronic steering system based on the coordinate sequence, commands to control power output, and commands to brake.

[0051] Preferably, the data acquisition module acquires curve map data through the following specific steps:

[0052] Step S11: Obtain satellite map data, generate curve data, and draw virtual boundary lines on both sides of the curve data;

[0053] Step S12: Generate an initial path based on the virtual boundary lines on both sides, the vehicle width, the safety distance, and the vehicle's minimum turning radius. The initial path is a strip of preset width, and the width of the strip is equal to the sum of the vehicle width and the safety distance.

[0054] Map data of the curve area is retrieved via a satellite map interface, and the curve's curvature radius, length, and slope parameters are analyzed. Virtual boundary lines are generated on both sides of the curve, extending outwards from the lane edge with a safety buffer distance (preset 0.5 meters) to form a closed drivable area. Based on the virtual boundary lines, the actual width of the vehicle body, and the safety distance, a strip-shaped initial path is calculated: path width = vehicle width + 2 × safety distance. Combined with the vehicle's minimum turning radius, a circular arc fitting algorithm is used to generate a smooth and continuous path, ensuring the vehicle can safely pass through the curve along the centerline. The obstacle's 3D point cloud is scanned by LiDAR, and image data is captured by a camera, with the output fused as follows: position coordinates, geometric dimensions, and velocity vector. The Euclidean distance between the obstacle and the virtual boundary lines on both sides is calculated: if the distance is ≤ the safety distance, it is marked as an obstacle within the path, triggering the obstacle avoidance process. When the obstacle's speed is < 0.1 m / s, it is considered stationary, and avoidance paths are generated on its left and right sides. When the obstacle's speed is ≥0.1m / s, it is determined to be in motion, and the trajectory is predicted based on Newton's laws of motion: S(t)=S0 + V·t + 0.5a·t². When the predicted trajectory intersects with the initial path, if the vehicle's distance from the collision point is >10 meters: maintain the original path, only reducing the speed to 15km / h; if the vehicle's distance from the collision point is ≤10 meters: generate an arc-shaped obstacle avoidance path in real time, ensuring a minimum lateral distance of 1 meter.

[0055] Preferably, the data acquisition module acquires obstacle data through the following specific steps:

[0056] Step S21: The obstacle data includes position coordinates, geometric dimensions, velocity magnitude, and direction;

[0057] Step S22: Calculate the distance between the obstacle and the virtual boundary lines on both sides.

[0058] Map data of the curve area is retrieved via a satellite map interface, and the curve's curvature radius, length, and slope parameters are analyzed. Virtual boundary lines are generated on both sides of the curve, extending outwards by a safety buffer distance (preset 0.5–1 meter) based on the lane edge, forming a closed drivable area. Based on the virtual boundary lines, the actual width of the vehicle body, and the safety distance, a strip-shaped initial path is calculated: path width = vehicle width + 2 × safety distance. Combining this with the vehicle's minimum turning radius, a circular arc fitting algorithm is used to generate a smooth, continuous path, ensuring the vehicle can safely pass through the curve along the centerline.

[0059] Preferably, the specific steps of the data analysis module in analyzing obstacle data and replanning the path to generate a new path are as follows:

[0060] Step S31: Determine whether the obstacle is stationary or moving based on the obstacle data. If the obstacle is stationary, replan the avoidance paths on both sides of the obstacle.

[0061] Step S32: If the obstacle is in motion, generate the obstacle's trajectory based on the obstacle's position and velocity parameters;

[0062] Step S33: Determine whether the obstacle's trajectory intersects with the initial path. If they intersect, add a time parameter for comprehensive analysis. When the obstacle is at the intersection point, if the distance between the vehicle body and the intersection point is greater than a threshold, maintain the initial path; if the distance between the vehicle body and the intersection point is less than the threshold, replan a new path.

[0063] The vehicle-mounted LiDAR scans the environment to generate 3D point cloud data. A camera identifies the geometric dimensions of obstacles and converts them from pixel coordinates to a position coordinate system. Millimeter-wave radar detects the radial velocity of obstacles in real time, calculates the velocity vector, and determines the distance between the obstacle and the virtual boundary lines on both sides.

[0064] Obstacle state determination and static obstacle avoidance: Millimeter-wave radar detects the radial velocity of obstacles in real time, calculates the velocity vector, and determines the stationary state if the absolute value of the velocity is < 0.1 m / s (system preset threshold) and lasts for more than 3 seconds. For static obstacles (such as rocks or roadblocks), a two-way avoidance path is generated. When the obstacle velocity is ≥ 0.1 m / s, it is determined to be in motion, and the obstacle's trajectory is predicted based on Newton's law of motion:

[0065] S(t) = S0 + V·t + 0.5a·t²

[0066] If the obstacle's trajectory and path intersect, when the obstacle is at the intersection point, a circle with the obstacle's geometric center as the center and a radius of a threshold is drawn. If the vehicle is inside the circle, it is determined that there is a potential collision risk, and the path is replanned.

[0067] Preferably, the specific steps for the data analysis module to optimize and correct the new path are as follows:

[0068] Step S41: Obtain tire pressure monitoring data for each tire of the vehicle in real time;

[0069] Step S42: Determine if there are any tires with tire pressure values ​​lower than a preset threshold;

[0070] Step S43: If there is a tire with abnormal tire pressure, the obstacle avoidance path on the same side as the abnormal tire is determined as the priority passage path.

[0071] The system uses embedded tire pressure sensors to monitor the pressure of all four tires in real time. It determines if any tire has a pressure below a preset threshold of 200 kPa. If an abnormal tire with a pressure below 200 kPa is found, the obstacle avoidance path on the same side as the abnormal tire is prioritized. When turning left, if the abnormal tire is on the right, the obstacle avoidance path on the same side as the abnormal tire is selected (i.e., the path has a larger turning radius, reducing tire pressure). If the abnormal tire is on the left, the obstacle avoidance path on the same side as the abnormal tire is selected (i.e., the path has a smaller turning radius, avoiding the risk of tire blowout due to centrifugal force during cornering). When turning right, if the abnormal tire is on the right, the obstacle avoidance path on the same side is selected (i.e., the path has a smaller turning radius, reducing tire pressure).

[0072] Preferably, in step S43, after selecting the priority passage path, the vehicle speed is adjusted according to the tire pressure.

[0073] Adjust vehicle speed according to tire pressure. When tire pressure is less than a preset threshold of 200 kPa, the target speed is adjusted according to the following formula: Where P is the tire pressure. For the target speed, This is the originally planned cornering speed. When P = 150 kPa, =40km / h Lower tire pressure leads to a reduction in vehicle speed, thereby reducing the lateral force generated when the vehicle is cornering and reducing the risk of tire blowout by actively slowing down.

[0074] Preferably, in step S42, when all tire pressure values ​​are greater than a preset threshold, the following steps are performed:

[0075] Step S42a: Obtain the remaining battery capacity data of the vehicle and assess the driving range of the vehicle.

[0076] Step S42b: Evaluate the vehicle's remaining driving range based on the battery's remaining data, and select the shortest path inside the curve as the preferred path.

[0077] When all tire pressures are normal (tire pressure ≥ preset threshold 200KPa), the system switches to range optimization mode, obtains battery remaining data in real time through the battery management system, assesses the vehicle's remaining driving range, selects the shortest path inside the curve as the preferred path, and extends the operating time of a single charge.

[0078] Preferably, the data storage module is configured to store the optimized path process data, analyze the location and probability of obstacles based on historical data, generate an obstacle probability heatmap, and select the path that avoids the location of obstacles with a high probability of occurrence as the preferred path.

[0079] The data storage module is configured to store the optimized path process data. Based on historical data analysis, it identifies the location and probability of obstacles. As the unmanned sightseeing vehicle repeatedly passes through curves, covering different time periods, it generates an obstacle probability heatmap. The more repetitions, the more accurately it represents the recurrence of curves. For example, if there is a temporary construction site on a curve, the probability of obstacles appearing on the heatmap is higher. When the probability of obstacles appears is greater than 80%, it is marked as high risk. The heatmap is automatically updated every 24 hours to adapt to changes in passenger flow during holidays and seasons, providing predictive optimization for path planning. Based on heatmap-based pre-avoidance, sharp turns are reduced, and path planning calculation time is shortened.

[0080] Preferably, the data storage module obtains the probability of obstacle occurrence based on historical data. When the probability is greater than 60%, the vehicle speed is reduced to 30% of the original cornering speed to compensate for the speed on a flat road, ensuring timely arrival at the destination.

[0081] When the driverless sightseeing vehicle moves tourists from point A to point B in the scenic area, there are multiple curves between points A and B. As the driverless sightseeing vehicle passes through the path multiple times, it will obtain different historical data, count the number of times obstacles appear on the curves, and determine the probability of obstacles appearing. In this way, the vehicle will reduce its speed in advance on curves with a high probability of obstacles, and increase its speed on the other flat paths. This improves the safety of turning curves and prevents the driverless sightseeing vehicle from failing to reach its destination on time.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A driverless sightseeing vehicle for scenic areas capable of automatically planning curved paths, characterized in that: It includes the vehicle body, data acquisition module, data analysis module, data storage module, and execution module; The data acquisition module is used to acquire curve map data, obstacle data, and vehicle data; The data analysis module is used to process the curve map data, generate an initial path, and re-plan the target path based on the obstacle data and the initial path. The data storage module is used to store the target path, process the historical data of the target path, correct the target path, and generate an optimized path. The execution module controls vehicle movements based on the optimized path.

2. The unmanned sightseeing vehicle for scenic areas capable of automatically planning curved paths according to claim 1, characterized in that, The data acquisition module acquires curve map data through the following specific steps: Step S11: Obtain satellite map data, generate curve data, and draw virtual boundary lines on both sides of the curve data; Step S12: Generate an initial path based on the virtual boundary lines on both sides, the vehicle width, the safety distance, and the vehicle's minimum turning radius. The initial path is a strip of preset width, and the width of the strip is equal to the sum of the vehicle width and the safety distance.

3. The unmanned sightseeing vehicle for scenic areas capable of automatically planning curved paths according to claim 1, characterized in that, The data acquisition module acquires obstacle data through the following specific steps: Step S21: The obstacle data includes position coordinates, geometric dimensions, velocity magnitude, and direction; Step S22: Calculate the distance between the obstacle and the virtual boundary lines on both sides.

4. The unmanned sightseeing vehicle for scenic areas capable of automatically planning curved paths according to claim 1, characterized in that, The specific steps of the data analysis module in analyzing obstacle data and replanning the path to generate a new path are as follows: Step S31: Determine whether the obstacle is stationary or moving based on the obstacle data. If the obstacle is stationary, replan the avoidance paths on both sides of the obstacle. Step S32: If the obstacle is in motion, generate the obstacle's trajectory based on the obstacle's position and velocity parameters; Step S33: Determine whether the obstacle's trajectory intersects with the initial path. If they intersect, add a time parameter for comprehensive analysis. When the obstacle is at the intersection point, if the distance between the vehicle body and the intersection point is greater than a threshold, maintain the initial path. If the distance between the vehicle body and the intersection point is less than the threshold, a new path is replanned.

5. The unmanned sightseeing vehicle for scenic areas capable of automatically planning curved paths according to claim 1, characterized in that, The specific steps for the data analysis module to optimize and correct the new path are as follows: Step S41: Obtain tire pressure monitoring data for each tire of the vehicle in real time; Step S42: Determine if there are any tires with tire pressure values ​​lower than a preset threshold; Step S43: If there is a tire with abnormal tire pressure, the obstacle avoidance path on the same side as the abnormal tire is determined as the priority passage path.

6. The unmanned sightseeing vehicle for scenic areas capable of automatically planning curved paths according to claim 5, characterized in that, In step S43, after selecting the priority passage path, the vehicle speed is adjusted according to the tire pressure.

7. A scenic area unmanned sightseeing vehicle capable of automatically planning curved paths according to claim 5, characterized in that, In step S42, if all tire pressure values ​​are greater than a preset threshold, the following steps are performed: Step S42a: Obtain the remaining battery capacity data of the vehicle and assess the driving range of the vehicle. Step S42b: Evaluate the vehicle's remaining driving range based on the battery's remaining data, and select the shortest path inside the curve as the preferred path.

8. The unmanned sightseeing vehicle for scenic areas capable of automatically planning curved paths according to claim 6, characterized in that, The data storage module is configured to store the optimized path process data, analyze the location and probability of obstacles based on historical data, generate an obstacle probability heatmap, and select the path that avoids the location of obstacles with a high probability of occurrence as the preferred path.

9. A driverless sightseeing vehicle for scenic areas capable of automatically planning curved paths according to claim 6, characterized in that, The data storage module obtains the probability of obstacles appearing based on historical data. When the probability is greater than 60%, the vehicle speed is reduced to 30% of the original cornering speed to compensate for the speed on a flat road, ensuring that the vehicle arrives at the destination on time.

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