Closed park docking vehicle scheduling system and method based on dynamic demand response

Abstract

The invention relates to a closed park connection vehicle scheduling system and method based on dynamic demand response, and the system comprises a user interaction end which is used for providing an interface of a park electronic map and obtaining a vehicle use request of a user, and the vehicle use request comprises a starting point coordinate, an end point coordinate, a time requirement and the number of people; the cloud scheduling platform is used for receiving the vehicle using request of the user interaction end, monitoring vehicle states and road condition information of all connection vehicles in the park, performing global optimization calculation based on the user request, the vehicle states, the park electronic map and the road condition information, determining an assigned connection vehicle, planning a real-time optimal driving path of the connection vehicle, and sending the real-time optimal driving path to the user interaction end. Forming a vehicle instruction, and issuing the vehicle instruction to a corresponding connection vehicle; and the connection vehicle is used for receiving and executing the vehicle instruction issued by the cloud scheduling platform and reporting self-positioning coordinates to the cloud scheduling platform in real time. Compared with the prior art, the method has the advantages that the passenger experience is greatly improved, and the operation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of park vehicle dispatching technology, and in particular to a closed park shuttle vehicle dispatching system and method based on dynamic demand response. Background Technology

[0002] Park shuttle buses are used in closed or semi-closed parks (such as science parks, university campuses, large factory areas, theme parks, etc.) and are a short-distance shuttle transportation tool connecting various parts of an industrial park.

[0003] The existing shuttle buses in the park have the following technical problems: 1. Fixed Route / Station Restrictions: Traditional campus shuttle buses (whether autonomous or not) typically use fixed routes and stations, resulting in poor flexibility. Passengers need to walk to the station to wait, failing to provide convenient "door-to-door" or "point-to-point" services, especially when the distance within the campus is far or the weather is bad, leading to a poor experience.

[0004] 2. Delayed response: Reservation-based shuttle buses usually require booking a long time in advance, and the departure time is fixed, making it difficult to meet users' immediate or temporary travel needs.

[0005] 3. Low resource utilization efficiency: Fixed routes may lead to high vehicle empty-running rates (especially during off-peak hours), or insufficient capacity and long passenger waiting times during peak hours. The lack of dynamic optimization results in low vehicle and energy utilization.

[0006] 4. Poor user experience: Passengers cannot flexibly hail a ride based on their real-time location and destination, and waiting time and trip time are uncontrollable.

[0007] 5. Traditional ride-hailing models are not applicable: Using private ride-hailing vehicles within closed parks presents issues related to management, safety, and cost. Existing dispatch systems for autonomous driving primarily focus on RoboTaxi on open roads or fixed-route shuttles within parks, lacking optimized solutions for dynamic connections between "any origin and destination" within closed parks.

[0008] Some parks have tried using apps to call for rides, but the dispatch logic is simple (such as first-come, first-served, assigning the nearest vehicle) and lacks comprehensive dynamic optimal route planning for multiple vehicles, multiple orders, and real-time traffic conditions. It cannot effectively handle the global optimization of the complete journey chain of "pick up people at the starting point -> drop people off at the destination". Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a closed-loop shuttle vehicle dispatching system and method based on dynamic demand response, which combines user on-demand reservation, dynamic route planning and real-time scheduling optimization.

[0010] The objective of this invention can be achieved through the following technical solutions: A closed-loop campus shuttle vehicle dispatching system based on dynamic demand response includes: The user interface provides an electronic map of the park and obtains users' car-hailing requests, which include the origin coordinates, destination coordinates, time requirements, and number of passengers. The cloud-based dispatch platform is used to receive vehicle requests from user terminals, monitor the vehicle status and road conditions of all shuttle vehicles in the park, perform global optimization calculations based on user requests, vehicle status, park electronic maps and road conditions, determine the assigned shuttle vehicle, and plan the real-time optimal driving path of the shuttle vehicle from its current location to the starting and ending coordinates of the vehicle request, forming a vehicle instruction, which is then sent to the corresponding shuttle vehicle. The shuttle vehicle is used to receive and execute vehicle instructions issued by the cloud dispatch platform, and to report its own location coordinates to the cloud dispatch platform in real time.

[0011] Furthermore, the cloud-based scheduling platform includes: The request receiving and processing module is used to receive vehicle use requests from the user's interactive terminal; The dynamic scheduling optimization engine monitors the vehicle status and road conditions of all shuttle vehicles in the park; it performs global optimization calculations based on user requests, vehicle status, park electronic map and road conditions to determine the assigned shuttle vehicle and plan the optimal path for the shuttle vehicle from its current location to the starting and ending coordinates of the user request. The route planning module is used to recalculate the real-time optimal driving route from the current location to the starting coordinates and the destination coordinates of the user request based on the dynamic changes in user requests and road conditions. The vehicle instruction issuance module is used to generate vehicle instructions in real time and issue them to the assigned shuttle vehicles based on the output of the dynamic scheduling optimization engine and the route planning module.

[0012] Furthermore, the optimization objectives of the global optimization calculation include: minimizing the total passenger waiting time, minimizing the total passenger travel time, minimizing the total vehicle mileage or energy consumption, maximizing vehicle utilization or carpooling rate, and balancing vehicle load.

[0013] Furthermore, the global optimization calculation process also performs similarity judgment on multiple ride requests: if the starting coordinates or ending coordinates of multiple ride requests are close in time and space and meet the optimization target of the global optimization calculation, then multiple ride requests are merged to optimize the carpooling route.

[0014] Furthermore, the vehicle status of the shuttle vehicle includes location, battery level / range, passenger status, current task list, and health status.

[0015] Furthermore, the shuttle vehicle is an autonomous vehicle.

[0016] Furthermore, the shuttle vehicle, based on onboard sensors and the park's electronic map, travels to the designated location to pick up passengers according to the received real-time optimal driving route and then delivers them to the destination.

[0017] Furthermore, the vehicle-mounted sensors include lidar, cameras, millimeter-wave radar, GNSS / RTK modules, and IMU modules.

[0018] Furthermore, the system also includes a management backend that connects to a cloud-based scheduling platform to monitor the overall operating status and configuration parameters of the system.

[0019] The present invention also provides a closed-loop shuttle vehicle dispatching method for a closed-loop shuttle vehicle dispatching system based on dynamic demand response, as described above, comprising the following steps: Users can select the starting and ending coordinates on the electronic map interface of the park through the user interface, and enter the time requirements and number of people to generate a user's car rental request. The user interface sends the vehicle request to the cloud dispatch platform; Based on the user request, vehicle status, park electronic map and road condition information, the cloud dispatch platform performs global optimization calculations in real time or before the time requirement to determine the assigned shuttle vehicle and plan the real-time optimal driving route of the shuttle vehicle from the current location to the starting coordinates and the ending coordinates of the user request, thus forming a vehicle instruction. The cloud-based dispatch platform issues vehicle instructions to the assigned shuttle vehicles; After receiving vehicle instructions, the vehicle-side control system of the shuttle vehicle will automatically drive to the corresponding starting point coordinates to pick up passengers based on the park's electronic map and real-time perception. After the shuttle vehicle reaches the starting point coordinates, a notification to board the vehicle is sent to the user's interactive terminal; Once passengers are confirmed to have been picked up, the shuttle vehicle will automatically proceed to the corresponding destination coordinates. After reaching the destination coordinates, passengers disembark.

[0020] Compared with the prior art, the present invention has the following advantages: (1) Significantly improve passenger experience: This invention provides a user interaction terminal to receive user car requests containing the starting point coordinates, the destination coordinates, the time requirement and the number of people. The cloud dispatch platform plans the real-time optimal driving route of the shuttle vehicle from the current location to the starting point coordinates and the destination coordinates of the car request, realizing "anytime, anywhere" door-to-door on-demand shuttle, significantly reducing the walking distance to the station and the waiting time, making the trip time more controllable and convenient.

[0021] (2) Significantly improves operational efficiency: The path planning module of the cloud-based dispatching platform of this invention can recalculate the real-time optimal driving route for the assigned shuttle vehicle based on the dynamic changes in user requests and road conditions; and in the global optimization process of the dynamic dispatching optimization engine, it merges multiple ride-sharing requests after similarity judgment to optimize the ride-sharing route. Through dynamic path planning and ride-sharing optimization, it reduces the empty driving mileage of vehicles and improves the service capacity of a single vehicle. It reduces the overall vehicle demand and saves on purchase and maintenance costs. It improves the efficiency of vehicle and energy utilization.

[0022] (3) Improve the level of intelligent management of the park: realize the digital and intelligent management and scheduling of traffic resources in the park.

[0023] (4) Enhance the park’s attractiveness and image: provide convenient, efficient and high-tech shuttle services to improve the quality of the park.

[0024] (5) Promote the application of autonomous driving: Verify and promote the practical value of autonomous driving technology in a relatively controllable closed park environment. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of a closed-loop shuttle vehicle dispatching system based on dynamic demand response provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating a closed-loop shuttle vehicle scheduling method based on dynamic demand response provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0028] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0029] Example 1 like Figure 1 As shown, this embodiment provides a closed-campus shuttle vehicle dispatching system based on dynamic demand response, applicable to closed or semi-closed campuses (such as science parks, university campuses, large factory areas, theme parks, etc.), including: The user interface provides an electronic map of the park and obtains users' car-hailing requests, which include the origin coordinates, destination coordinates, time requirements, and number of passengers. The cloud-based dispatch platform is used to receive vehicle requests from user terminals, monitor the vehicle status and road conditions of all shuttle vehicles in the park, perform global optimization calculations based on user requests, vehicle status, park electronic maps and road conditions, determine the assigned shuttle vehicle, and plan the real-time optimal driving path of the shuttle vehicle from its current location to the starting and ending coordinates of the vehicle request, forming a vehicle instruction, which is then sent to the corresponding shuttle vehicle. The shuttle vehicle is used to receive and execute vehicle instructions issued by the cloud dispatch platform, and to report its own location coordinates to the cloud dispatch platform in real time.

[0030] The following is a detailed description of each part.

[0031] 1. User interaction interface: The provided electronic map interface of the park allows users to select or enter their real-time / reservation start point and destination (end point) on the map to submit a car rental request.

[0032] Preferably, users can view information such as the vehicle's real-time location, estimated time of arrival (ETA), and trip status.

[0033] The user interaction terminal can be a standalone user terminal device or a user interaction mini-program integrated into existing electronic devices.

[0034] 2. Cloud-based scheduling platform, including: The request receiving and processing module is used to receive car rental requests from user interaction terminals or mini programs, including origin and destination coordinates, time requirements - immediate / reserved, number of people, etc. The dynamic scheduling optimization engine monitors the vehicle status and road conditions of all shuttle vehicles in the park; it performs global optimization calculations based on user requests, vehicle status, park electronic map and road conditions to determine the assigned shuttle vehicle and plan the optimal path for the shuttle vehicle from its current location to the starting and ending coordinates of the user request. Optionally, vehicle status includes location, battery level / range, passenger status, current task list, and health status; The park's electronic map is a high-precision map, including road networks, speed limits, restricted areas, and charging station locations; Traffic information is real-time; for example, when there is congestion or temporary obstacles, it is reported through vehicle-mounted sensors or roadside units.

[0035] The core of the dynamic scheduling optimization engine lies in the fact that the scheduling process is not just about assigning the nearest vehicle, but about dynamically planning an optimal path that must complete the following: go to the starting point A to pick up passenger P -> transport passenger P to the destination B.

[0036] Optimization objectives include, but are not limited to: minimizing total passenger waiting time, minimizing total passenger travel time, minimizing total vehicle mileage / energy consumption, maximizing vehicle utilization / carpooling rate, and balancing vehicle load.

[0037] Preferably, during the global optimization calculation process, a similarity judgment is also performed on multiple ride requests: if the starting coordinates or ending coordinates of multiple ride requests are close in time and space and meet the optimization objective of the global optimization calculation, then multiple ride requests are merged to optimize the carpooling route.

[0038] That is, the system will intelligently merge ride-sharing orders (when the origins or destinations of multiple passengers are close in time and space and meet the optimization objectives) to achieve ride-sharing optimization and improve bicycle efficiency.

[0039] The route planning module is used to recalculate the real-time optimal driving route from the current location to the starting coordinates and the destination coordinates of the user request based on the dynamic changes in user requests and road conditions. In other words, route planning needs to consider dynamic factors (such as newly inserted orders and changes in road conditions) and be able to dynamically replan.

[0040] The vehicle instruction issuance module is used to generate vehicle instructions in real time and issue them to the assigned shuttle vehicles based on the output of the dynamic scheduling optimization engine and the route planning module.

[0041] That is, the dispatching decisions (which order to accept, where to pick up, and where to deliver) and the planned route instructions are sent to the target vehicle in real time.

[0042] 3. Shuttle vehicles, preferably autonomous vehicles, such as autonomous minibuses.

[0043] It is used to receive and execute scheduling and path instructions issued by the cloud scheduling platform.

[0044] Relying on onboard sensors (LiDAR, cameras, millimeter-wave radar, GNSS / RTK, IMU) and high-precision maps of the park, the system achieves autonomous driving (perception, localization, planning, and control), safely and accurately driving along the planned route to the designated starting point to pick up passengers and deliver them to the destination.

[0045] It maintains real-time communication with the cloud-based dispatch platform and reports its own location.

[0046] Preferably, the system also includes a management backend connected to a cloud-based dispatch platform for monitoring the overall operating status and configuration parameters of the system; the overall operating status includes vehicle location / status, order information, and system performance indicators; the configuration parameters include the number of vehicles and service time. Optionally, the management backend can also be used to handle exceptions and view reports.

[0047] Example 2 like Figure 2 As shown in the figure, this embodiment provides a closed-loop shuttle vehicle dispatching method for a closed-loop shuttle vehicle dispatching system based on dynamic demand response, as described in Embodiment 1, including the following steps: S1: The user selects the starting point and ending point coordinates on the electronic map interface of the park through the user interface, and enters the time requirement and number of people to generate the user's car rental request. S2: The user interface sends the vehicle request to the cloud dispatch platform; S3: Based on the user request, vehicle status, park electronic map and road condition information, the cloud dispatch platform performs global optimization calculations in real time or before the time requirement to determine the assigned shuttle vehicle and plan the real-time optimal driving route of the shuttle vehicle from the current location to the starting coordinates and the ending coordinates of the user request, thus forming a vehicle instruction. S4: The cloud-based dispatch platform sends vehicle instructions to the assigned shuttle vehicle; S5: After receiving vehicle instructions, the vehicle-side control system of the shuttle vehicle will automatically drive to the corresponding starting point coordinates to pick up passengers based on the park's electronic map and real-time perception. S6: After the shuttle vehicle reaches the starting point coordinates, send a boarding notification to the user interface. S7: After confirming that the passenger has been picked up, the shuttle vehicle will automatically drive to the corresponding destination coordinates; S8: After reaching the destination coordinates, passengers disembark.

[0048] Below is an example of a workflow for the above method: 1. Users open the mini-program, select or enter the current / reserved starting point and destination on the park map, and submit the request.

[0049] 2. The mini-program sends the request (including origin and destination coordinates, timestamp, user ID, etc.) to the cloud scheduling platform.

[0050] 3. The cloud-based scheduling engine performs scheduling calculations in real time or before the scheduled time (for reservation orders): Based on the optimization objective (such as minimizing global waiting time), the optimal vehicle assignment scheme and pick-up / drop-off route (including possible carpooling matching) are dynamically calculated.

[0051] Determine the final dispatch plan (which vehicle to assign, when and where to pick up the passenger, and the passenger's position in the vehicle's current task sequence).

[0052] 4. The cloud sends dispatch instructions and the planned detailed routes to the assigned autonomous minibus.

[0053] 5. The vehicle-mounted control system receives instructions and, based on high-precision maps and real-time perception, autonomously drives to the starting point to pick up passengers.

[0054] 6. When the vehicle arrives near the starting point, passengers will be notified via a mini-program (or prompted on the vehicle's interactive screen), and passengers will board the vehicle (this can be combined with identity verification).

[0055] 7. The vehicle drives itself to transport passengers to their designated destination.

[0056] 8. Passengers disembark at the final destination.

[0057] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A closed-loop park shuttle vehicle dispatching system based on dynamic demand response, characterized in that, include: The user interface provides an electronic map of the park and obtains users' car-hailing requests, which include the origin coordinates, destination coordinates, time requirements, and number of passengers. The cloud-based dispatch platform is used to receive vehicle requests from user terminals, monitor the vehicle status and road conditions of all shuttle vehicles in the park, perform global optimization calculations based on user requests, vehicle status, park electronic maps and road conditions, determine the assigned shuttle vehicle, and plan the real-time optimal driving path of the shuttle vehicle from its current location to the starting and ending coordinates of the vehicle request, forming a vehicle instruction, which is then sent to the corresponding shuttle vehicle. The shuttle vehicle is used to receive and execute vehicle instructions issued by the cloud dispatch platform, and to report its own location coordinates to the cloud dispatch platform in real time.

2. The closed-loop shuttle vehicle dispatching system based on dynamic demand response according to claim 1, characterized in that, The cloud-based scheduling platform includes: The request receiving and processing module is used to receive vehicle use requests from the user's interactive terminal; The dynamic scheduling optimization engine monitors the vehicle status and road conditions of all shuttle vehicles in the park; it performs global optimization calculations based on user requests, vehicle status, park electronic map and road conditions to determine the assigned shuttle vehicle and plan the optimal path for the shuttle vehicle from its current location to the starting and ending coordinates of the user request. The route planning module is used to recalculate the real-time optimal driving route from the current location to the starting coordinates and the destination coordinates of the user request based on the dynamic changes in user requests and road conditions. The vehicle instruction issuance module is used to generate vehicle instructions in real time and issue them to the assigned shuttle vehicles based on the output of the dynamic scheduling optimization engine and the route planning module.

3. A closed-loop shuttle vehicle dispatching system based on dynamic demand response as described in claim 2, characterized in that, The optimization objectives of the global optimization calculation include: minimizing total passenger waiting time, minimizing total passenger travel time, minimizing total vehicle mileage or energy consumption, maximizing vehicle utilization or carpooling rate, and balancing vehicle load.

4. A closed-loop shuttle vehicle dispatching system based on dynamic demand response as described in claim 2, characterized in that, The global optimization calculation process also involves similarity judgment for multiple ride requests: if the starting coordinates or ending coordinates of multiple ride requests are close in time and space and meet the optimization objective of the global optimization calculation, then multiple ride requests are merged to optimize the carpooling route.

5. A closed-loop shuttle vehicle dispatching system based on dynamic demand response according to claim 2, characterized in that, The vehicle status of the shuttle vehicle includes location, battery level / range, passenger status, current task list, and health status.

6. A closed-loop shuttle vehicle dispatching system based on dynamic demand response as described in claim 1, characterized in that, The shuttle vehicles are autonomous vehicles.

7. A closed-loop shuttle vehicle dispatching system based on dynamic demand response according to claim 6, characterized in that, The shuttle vehicles, based on onboard sensors and the park's electronic map, travel to designated locations to pick up passengers according to the received real-time optimal driving route and then deliver them to their destination.

8. A closed-loop shuttle vehicle dispatching system based on dynamic demand response according to claim 7, characterized in that, The vehicle-mounted sensors include lidar, cameras, millimeter-wave radar, GNSS / RTK modules, and IMU modules.

9. A closed-loop shuttle vehicle dispatching system based on dynamic demand response according to claim 1, characterized in that, The system also includes a management backend that connects to a cloud-based scheduling platform to monitor the overall operating status and configuration parameters of the system.

10. A method for scheduling shuttle vehicles in a closed-loop park based on a dynamic demand response system, as described in any one of claims 1-9, characterized in that, Includes the following steps: Users can select the starting and ending coordinates on the electronic map interface of the park through the user interface, and enter the time requirements and number of people to generate a user's car rental request. The user interface sends the vehicle request to the cloud dispatch platform; Based on the user request, vehicle status, park electronic map and road condition information, the cloud dispatch platform performs global optimization calculations in real time or before the time requirement to determine the assigned shuttle vehicle and plan the real-time optimal driving route of the shuttle vehicle from the current location to the starting coordinates and the ending coordinates of the user request, thus forming a vehicle instruction. The cloud-based dispatch platform issues vehicle instructions to the assigned shuttle vehicles; After receiving vehicle instructions, the vehicle-side control system of the shuttle vehicle will automatically drive to the corresponding starting point coordinates to pick up passengers based on the park's electronic map and real-time perception. After the shuttle vehicle reaches the starting point coordinates, a notification to board the vehicle is sent to the user's interactive terminal; Once passengers are confirmed to have been picked up, the shuttle vehicle will automatically proceed to the corresponding destination coordinates. After reaching the destination coordinates, passengers disembark.

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