Driving emergency response training system and method based on vehicle-mounted cooperation of unmanned aerial vehicle

Abstract

The invention discloses a driving emergency response training system and method based on vehicle-mounted cooperation of an unmanned aerial vehicle, and belongs to the technical field of motor vehicle driving training. The method aims at solving the problems that existing training is insufficient in sense of reality, high in risk and inaccurate in evaluation. The system comprises an unmanned aerial vehicle system, a vehicle-mounted system, a general control system and a wireless communication module, the unmanned aerial vehicle carries a detachable assembly to simulate sudden dangers, the vehicle-mounted system collects operation and vehicle data, and the general control system supports scene configuration, safety monitoring and quantitative evaluation. The method comprises the steps of scene configuration, danger simulation, data acquisition, evaluation feedback and the like. The method constructs a training scene close to a real road, gives consideration to safety and effectiveness, supports personalized training through quantitative evaluation, is suitable for students at different stages, and is high in practicability and popularization value.

Description

Technical Field

[0001] This invention relates to the field of motor vehicle driver training technology, specifically to a driver emergency response training system and method based on unmanned aerial vehicle (UAV) vehicle-mounted collaboration. Background Technology

[0002] In motor vehicle driver training, a student's emergency response capability is one of the core skills to ensure future driving safety. Statistics show that over 40% of major road traffic accidents are related to improper emergency handling or operational errors by drivers. Therefore, targeted training in emergency response capabilities is a crucial aspect of driver training. However, current training methods for emergency response capabilities in driving schools still have many significant shortcomings and fail to meet the training needs of actual driving safety: Virtualized scenarios lack realism: Current training largely relies on driving simulators, which generate sudden dangerous scenarios through virtual images. While this method avoids the risks of actual training, the visual perception and spatial distance perception of virtual scenarios differ significantly from real road environments. The reaction patterns developed by trainees during training are difficult to directly transfer to actual driving scenarios, resulting in limited training effectiveness and failing to effectively improve trainees' ability to cope with real-world emergencies.

[0003] Real-world training is risky and lacks controllability: Some driving schools attempt to simulate dangerous scenarios by setting up fixed obstacles in actual training grounds, but the position of fixed obstacles can be predicted and cannot reproduce the randomness of the sudden appearance of dangerous objects on real roads; if moving obstacles are used for simulation, a dedicated person is required to operate them on-site, which not only poses the risk of colliding with student vehicles and causing safety accidents, but also makes it difficult to flexibly adjust the type, timing, location and movement of dangerous scenarios, resulting in insufficient diversity and flexibility in training scenarios.

[0004] Lack of precise process monitoring and quantitative assessment: Existing training methods cannot capture trainees' operational data in real time and comprehensively when facing sudden dangers, such as reaction time, turning angle, braking force, and operational continuity, resulting in the inability to accurately quantify and assess trainees' emergency response capabilities. Training feedback relies solely on the instructor's subjective judgment, lacking specificity and failing to identify trainees' weaknesses, thus hindering personalized training optimization.

[0005] With the rapid development of drone technology and vehicle-mounted intelligent technology, drones possess core advantages such as flexibility, maneuverability, and precise control, enabling them to simulate the sudden appearance and dynamic trajectory of hazardous materials. Vehicle-mounted systems, on the other hand, can achieve real-time monitoring of vehicle operation status and trainee actions. Both provide reliable technical support for constructing realistic, controllable, and quantifiable emergency response training scenarios. Therefore, there is an urgent need to design a driving emergency response training system and method based on drone-vehicle collaboration to address the core problems of insufficient realism, high training risk, and inaccurate assessment in existing training methods, filling a gap in current driver training technology. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing emergency response training in driver training and to provide a driver emergency response training system and method based on UAV-vehicle collaboration. By simulating sudden hazards using UAVs, combined with real-time monitoring of vehicle status and trainee operation data by the vehicle-mounted system, and coordinated control and data analysis by the central control system, a realistic and risk-controlled emergency response training scenario is constructed. This enables precise quantitative assessment of trainees' emergency response capabilities and personalized training guidance, ultimately improving trainees' actual driving safety ability to cope with sudden hazards on real roads.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: The first part of the present invention is a driving emergency response training system based on UAV-vehicle collaboration, comprising: An unmanned aerial vehicle (UAV) system for simulating sudden hazards includes a flight control module, a positioning module, a communication module, a status monitoring module, and a hazard simulation component. The vehicle-mounted system is used to monitor vehicle status and student operation data. The vehicle-mounted system includes a data acquisition module, a vehicle control linkage module, a communication module, and an alarm module. The overall control system is used to coordinate and control the unmanned aerial vehicle system and the vehicle-mounted system. The overall control system includes an instruction generation module, a data processing module, a scene configuration module, an evaluation module, and a safety monitoring module. The wireless communication module is used to connect the UAV system, vehicle system, and central control system to enable data exchange and coordinated control among the three.

[0008] Furthermore, the flight control module receives instructions from the central control system to control the UAV's takeoff, landing, hovering, movement trajectory, and timing of appearance, simulating the sudden appearance of hazardous materials. The positioning module acquires the UAV's location information in real time and feeds it back to the central control system to ensure the UAV operates along a preset trajectory. The status monitoring module monitors the UAV's battery level, flight attitude, and communication connection status, sending alarm signals to the central control system and executing emergency return or forced landing procedures when abnormalities occur. The hazardous material simulation component is detachable and can be replaced with different types of hazardous material markers according to training scenario requirements. The UAV system also includes an obstacle avoidance module, which uses lidar or visual sensors to detect obstacles in the flight path in real time, enabling autonomous obstacle avoidance.

[0009] Furthermore, the data acquisition module includes a vehicle speed sensor, a steering angle sensor, a brake pedal pressure sensor, and an accelerator pedal sensor, used to collect trainee operation data and vehicle operating status data in real time and transmit them to the central control system; the vehicle control linkage module is used to receive instructions from the central control system in emergency situations and execute emergency braking or deceleration of the vehicle to ensure training safety.

[0010] Furthermore, the assessment module is used to quantify and score the trainee's emergency response capabilities based on the trainee's operational data and preset assessment indicators, and generate a training report; the safety monitoring module is used to monitor the relative position of the drone and the vehicle in real time and determine whether there is a collision risk. When a safety hazard is detected, it immediately sends an emergency return command to the drone system, an emergency braking command to the vehicle system, and triggers the alarm module.

[0011] Furthermore, the overall control system also includes a display terminal for displaying the drone's location, vehicle status, student operation data, and training scene images in real time, facilitating instructors to monitor the training process in real time.

[0012] Furthermore, it also includes a site positioning system, which uses positioning base stations deployed at the training site to assist the UAV positioning module and vehicle-mounted system in achieving high-precision positioning.

[0013] Furthermore, the alarm module is used to issue audible and visual alarms to trainees and instructors when safety hazards occur during training. These safety hazards include the drone being too close to the vehicle and the vehicle speeding.

[0014] The second part of this invention provides a driving emergency response training method based on UAV-vehicle collaboration, characterized in that: applied to the aforementioned driving emergency response training system based on UAV-vehicle collaboration, the method includes the following steps: Scene configuration: Through the scene configuration module of the central control system, preset or custom training scene parameters are set. The training scene parameters include the type of hazard, the direction of appearance of the UAV, distance, movement speed, timing of appearance, and preset evaluation indicators. System startup: The central control system sends a startup command to the UAV system and the vehicle system. After completing the self-test, the UAV system takes off and goes to the preset standby position. The vehicle system starts the data acquisition module and begins to collect vehicle operation status data in real time. Hazard simulation and dynamic adjustment: When the training vehicle travels to the preset area, the UAV system simulates sudden hazards and runs along the preset trajectory according to the instructions of the central control system; the central control system combines the vehicle status data transmitted by the vehicle system to dynamically adjust the flight trajectory of the UAV, the timing and location of the hazards, so as to avoid the trainees' prediction and enhance the realism of the training scenario. Data Acquisition: The onboard system collects real-time operational data from trainees when facing sudden dangers and transmits it to the central control system; Safety monitoring: The safety monitoring module of the central control system monitors the relative position and operating status of the drone and training vehicle in real time. If a safety hazard is detected, emergency safety measures will be implemented immediately. Assessment and Personalized Feedback: The assessment module of the central control system quantifies and scores trainees’ emergency response capabilities based on the collected operational data and preset assessment indicators, generates a training report that includes trainees’ strengths and weaknesses, and can adjust the difficulty of subsequent training scenarios based on the training results, providing personalized training guidance. Training complete: The central control system sends a return-to-home command to the UAV system, and the UAV lands at the designated location; the vehicle-mounted system stops data acquisition, and the training system shuts down.

[0015] Furthermore, the types of hazards include pedestrians, non-motorized vehicles, and obstacles, and the drone system replaces the corresponding hazard markers with detachable hazard simulation components; the dynamically adjusted parameters also include the drone's moving speed and the direction in which the hazard appears.

[0016] Furthermore, the collected student operation data includes reaction time, steering angle, braking force, accelerator pedal pressure change, and vehicle speed change data; the safety hazards include the drone being too close to the vehicle, vehicle speeding, and student operation errors; the emergency safety measures include sending an emergency return command to the drone system, sending an emergency braking or deceleration command to the vehicle system, and triggering the alarm module to issue an audible and visual alarm.

[0017] The present invention, by adopting the above technical solution, has at least the following beneficial effects: This invention utilizes an unmanned aerial vehicle (UAV) system carrying detachable hazard simulation components to dynamically simulate sudden hazards such as pedestrians, non-motorized vehicles, and obstacles in actual training environments. Combined with operational feedback from real training vehicles, it constructs emergency scenarios highly consistent with actual roads. Compared to the "virtualized scenarios" of existing virtual simulators, the trainee's visual perception, spatial distance judgment, and operational experience are indistinguishable from real driving. The emergency response patterns developed during training can be directly transferred to actual driving, completely resolving the core shortcomings of existing training methods that "disconnect between virtual and reality, and difficulty in implementing emergency response capabilities," effectively improving trainees' practical ability to deal with sudden hazards on real roads.

[0018] This invention constructs a safety protection system through multi-module collaboration: the safety monitoring module of the central control system monitors the relative position of the drone and the training vehicle in real time; the obstacle avoidance module of the drone system enables autonomous obstacle avoidance; the vehicle control linkage module of the vehicle system can respond to emergency braking commands; and the alarm module simultaneously triggers audible and visual prompts. When safety hazards such as the drone being too close to the vehicle, vehicle speeding, trainee operational errors, or drone malfunctions (such as low battery or communication interruption) occur, the system can automatically trigger emergency return-to-home for the drone and emergency braking for the vehicle, completely avoiding the drawbacks of existing mobile obstacle training methods that are "highly risky and poorly controllable by manual operation," ensuring safety throughout the entire training process.

[0019] On the one hand, the scenario configuration module of the central control system supports preset common emergency scenarios (such as pedestrians suddenly appearing ahead or non-motorized vehicles suddenly appearing to the side) and custom scenario parameters (hazard type, direction of appearance, distance, speed of movement, timing of appearance, etc.). On the other hand, the method adds "dynamic adjustment" logic, allowing the central control system to adjust the drone's flight trajectory and the timing of hazard appearance in real time based on vehicle status data transmitted from the vehicle system, preventing trainees from making pre-judgments. Meanwhile, the drone's hazard simulation component adopts a detachable design, allowing for quick adaptation to different training needs. Its applicability covers different stages, from basic novice training to advanced complex scenario training, demonstrating exceptional flexibility and adaptability.

[0020] The in-vehicle system uses multiple sensors, including those for vehicle speed, steering angle, and brake pedal pressure, to collect complete operational data from trainees in real time (reaction time, steering angle, braking force, speed changes, etc.). The overall control system's evaluation module performs quantitative analysis based on preset indicators, generating a training report that includes the trainee's strengths and weaknesses. Based on this report, the system can dynamically adjust the difficulty of subsequent training scenarios, providing trainees with personalized training guidance tailored to their individual needs, helping them accurately address their weaknesses. Simultaneously, real-time feedback and targeted training significantly reduce ineffective training time, improving both trainee training outcomes and instructors' teaching efficiency and overall teaching quality. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this embodiment. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is the workflow of the training system of the present invention. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this embodiment. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this embodiment as detailed in the appended claims.

[0024] Example 1: Driving Emergency Response Training System Based on UAV-Vehicle Collaboration This embodiment provides a driving emergency response training system based on UAV-vehicle collaboration, comprising: a UAV system for simulating sudden hazards, the UAV system including a flight control module, a positioning module, a communication module, a status monitoring module, and a hazard simulation component; a vehicle-mounted system for monitoring vehicle status and trainee operation data, the vehicle-mounted system including a data acquisition module, a vehicle control linkage module, a communication module, and an alarm module; a central control system for coordinating and controlling the UAV system and the vehicle-mounted system, the central control system including an instruction generation module, a data processing module, a scene configuration module, an evaluation module, and a safety monitoring module; and a wireless communication module for connecting the UAV system, the vehicle-mounted system, and the central control system to achieve data exchange and collaborative control among the three.

[0025] In one implementation, the flight control module in this embodiment receives instructions from the central control system to control the takeoff, landing, hovering, movement trajectory, and timing of the UAV's appearance, simulating the effect of a sudden appearance of a hazard. The positioning module acquires the UAV's location information in real time and feeds it back to the central control system to ensure the UAV operates along a preset trajectory. The status monitoring module monitors the UAV's battery level, flight attitude, and communication connection status, sending an alarm signal to the central control system and executing an emergency return or forced landing procedure when abnormalities occur. The hazard simulation component is a detachable structure, allowing for the replacement of different types of hazard markers according to training scenario requirements. The UAV system also includes an obstacle avoidance module, which uses lidar or visual sensors to detect obstacles in the flight path in real time, enabling autonomous obstacle avoidance. The detachable hazardous object simulation component can flexibly switch between pedestrian, non-motorized vehicle, and obstacle markers to adapt to different training stages, such as the novice adaptation period and the advanced training period, effectively solving the industry pain point of the single training scenario in traditional training. The obstacle avoidance module and the positioning module work together to avoid collisions between the drone and training vehicles or obstacles on the site, and to ensure the accuracy of the hazardous object simulation position, completely avoiding the safety risks caused by manually moving obstacles. The abnormal handling mechanism of the status monitoring module can automatically trigger the return or emergency landing procedure when abnormal situations such as low power or communication interruption occur, to prevent training interruption and significantly improve the stability of system operation.

[0026] As one implementation method, the data acquisition module in this embodiment includes a vehicle speed sensor, a steering angle sensor, a brake pedal pressure sensor, and an accelerator pedal sensor, used to collect trainee operation data and vehicle operating status data in real time and transmit them to the central control system. The vehicle control linkage module is used to receive instructions from the central control system in emergency situations and execute emergency braking or deceleration of the vehicle to ensure training safety. Multiple types of sensors can comprehensively capture trainee operation data such as steering angle, braking force, and accelerator control amplitude, as well as vehicle status data such as real-time vehicle speed and driving trajectory, providing a complete and reliable data source for subsequent accurate evaluation, effectively compensating for the technical deficiency of existing training methods that cannot collect complete operation data. The vehicle control linkage module is directly linked with the vehicle braking system, forming a rapid response link of "central control command - module response - vehicle braking," intervening promptly when trainee operational errors may cause collisions, comprehensively ensuring the safety of personnel, vehicles, and drones during training.

[0027] As one implementation method, the evaluation module in this embodiment is used to quantify and score the trainee's emergency response capabilities based on the trainee's operational data and preset evaluation indicators, and generate a training report. The safety monitoring module is used to monitor the relative position of the drone and the vehicle in real time and determine whether there is a collision risk. When a safety hazard is detected, it immediately sends an emergency return command to the drone system, an emergency braking command to the vehicle system, and triggers the alarm module. The evaluation module presets quantitative indicators such as reaction time, operational accuracy, and vehicle stability. Through quantitative analysis of the collected data, it generates a training report containing data curves and scoring results, replacing the subjective judgment of the instructor in traditional training, avoiding human error, and making the evaluation results more accurate and objective. The safety monitoring module calculates the relative position of the drone and the vehicle in real time. When it detects potential collision risks, vehicle speeding, or other hazards, it simultaneously triggers the drone's return, vehicle braking, and audible and visual alarms, constructing a closed-loop protection system of "monitoring-judgment-intervention," effectively solving the core problem of poor controllability in traditional real-world training scenarios.

[0028] As one implementation method, the central control system in this embodiment also includes a display terminal for real-time display of the drone's location, vehicle status, student operation data, and training scene visuals, facilitating real-time monitoring of the training process by the instructor. The display terminal provides the instructor with a visual monitoring interface, enabling remote tracking of training dynamics. Instructors can promptly identify abnormal student operations or system vulnerabilities without being physically present with the vehicle, and then quickly adjust training parameters or terminate training through the central control system. This improves teaching efficiency and further enhances the controllability of the training.

[0029] As one implementation method, this embodiment also includes a site positioning system. This system deploys positioning base stations on the training ground to assist the UAV positioning module and vehicle-mounted system in achieving high-precision positioning. The site positioning system can employ UWB positioning base stations to form a collaborative positioning system with the UAV positioning module and vehicle-mounted system, significantly improving positioning accuracy and ensuring the UAV operates along a preset trajectory. Simultaneously, it makes the calculation of the relative position between the vehicle and the UAV more accurate. On the one hand, it makes the location, distance, and timing of hazardous objects more closely resemble real road scenarios; on the other hand, it provides accurate position data support for the safety monitoring module, avoiding collision risks caused by positioning errors, and simultaneously improving training safety and effectiveness.

[0030] As one implementation method, the alarm module described in this embodiment is used to issue audible and visual alarms to trainees and instructors when safety hazards occur during training. These safety hazards include the drone being too close to the vehicle or the vehicle speeding. The alarm module adopts a two-way audible and visual alarm design. Trainees can promptly perceive hazards and adjust their operations through audible and visual prompts in the cockpit, while instructors can quickly intervene through alarm pop-ups and audible prompts on the display terminal, forming a dual early warning mechanism to further reduce training risks and ensure safety and controllability throughout the training process.

[0031] Example 2: Driving Emergency Response Training Method Based on UAV-Vehicle Collaboration This embodiment provides a driving emergency response training method based on UAV-vehicle collaboration, characterized by: being applied to the aforementioned driving emergency response training system based on UAV-vehicle collaboration, the method includes the following steps: Scene configuration: Through the scene configuration module of the central control system, preset or custom training scene parameters are configured, including the type of hazard, the direction of UAV appearance, distance, movement speed, timing of appearance, and preset evaluation indicators; System startup: The central control system sends a startup command to the UAV system and the vehicle system. After completing self-check, the UAV system takes off to a preset standby position, and the vehicle system starts the data acquisition module to begin real-time acquisition of vehicle operation status data; Hazard simulation and dynamic adjustment: When the training vehicle travels to the preset area, the UAV system simulates a sudden hazard and runs along a preset trajectory according to the command of the central control system; The central control system combines the vehicle system's data with the vehicle system's data transmission... The system dynamically adjusts the drone's flight trajectory and the timing and location of hazards based on vehicle status data, avoiding trainees' pre-judgment and enhancing the realism of training scenarios. Data acquisition: The onboard system collects real-time operational data from trainees facing sudden dangers and transmits it to the central control system. Safety monitoring: The central control system's safety monitoring module monitors the relative position and operational status of the drone and training vehicle in real time, immediately implementing emergency safety measures if a safety hazard is detected. Evaluation and personalized feedback: The central control system's evaluation module quantifies and scores the trainee's emergency response capabilities based on the collected operational data and preset evaluation indicators, generating a training report that includes the trainee's strengths and weaknesses. It can also adjust the difficulty of subsequent training scenarios based on the training results, providing personalized training guidance. Training completion: The central control system sends a return-to-home command to the drone system, causing the drone to land at the designated location; the onboard system stops data acquisition, and the training system shuts down.

[0032] As one implementation method, the types of hazards described in this embodiment include pedestrians, non-motorized vehicles, and obstacles. The drone system replaces the corresponding hazard markers with detachable hazard simulation components. The dynamically adjusted parameters also include the drone's movement speed and the direction in which the hazard appears. The preset scenarios cover high-frequency real-world road scenarios such as sudden pedestrians ahead, sudden non-motorized vehicles to the side, and sudden obstacles. Customizable parameters support flexible adjustment of the drone's appearance direction, distance, and movement speed. Combined with the detachable hazard simulation components, a single system can meet the differentiated training needs of different driving schools and students, effectively reducing the investment cost of training equipment. The dynamic adjustment mechanism makes the appearance of hazards more random, preventing students from making predictions, and the training effect is closer to real driving scenarios, effectively improving students' ability to react unprepared to sudden dangers.

[0033] As one implementation method, the student operation data collected in this embodiment includes reaction time, steering angle, braking force, accelerator pedal pressure change, and vehicle speed change data. The safety hazards include the drone being too close to the vehicle, vehicle speeding, and student operational errors. The emergency safety measures include sending an emergency return command to the drone system, sending an emergency braking or deceleration command to the vehicle system, and triggering an alarm module to issue an audible and visual alarm. Complete operation data collection can accurately capture the details of the student's actions from discovering a danger to reacting, providing comprehensive data support for quantitative assessment and helping to accurately identify training weaknesses such as "excessive reaction time" and "insufficient braking force." The combination of multi-dimensional safety hazard coverage and triple emergency measures constructs a comprehensive safety protection system of "prevention, early warning, and intervention," completely solving the drawbacks of uncontrollable risks in traditional real-scene training. Simultaneously, quantitative scoring and detailed training reports allow students to clearly understand their own level, and personalized difficulty adjustments achieve "teaching according to aptitude," avoiding frustration for novice students due to overly difficult scenarios and preventing advanced students from failing to improve their abilities due to overly easy scenarios, significantly improving training efficiency and quality. Example 3

[0034] like Figure 1 As shown, the workflow of the training system of the present invention is as follows: 1. Scene configuration: The instructor can preset training scene parameters through the scene configuration module of the central control system, including the type of hazard, the location of the drone, the timing of its appearance, its movement trajectory and speed, as well as the evaluation index thresholds; 2. System Startup: The central control system sends a start command to the UAV system and the vehicle system. After completing the self-test, the UAV system takes off and goes to the preset standby position. The vehicle system starts the data acquisition module and begins to collect vehicle status data in real time. 3. Hazard Simulation: When the training vehicle travels to the preset area, the central control system sends instructions to the UAV system, controlling the UAV to suddenly appear according to the preset trajectory and timing, simulating a sudden hazard. 4. Data Acquisition and Safety Monitoring: The onboard system collects the trainee's operation data (steering, braking, etc.) and vehicle status data in real time and transmits them to the central control system; at the same time, the safety monitoring module monitors the relative position of the drone and the vehicle in real time to determine whether there is a risk of collision, and if there is a risk, it immediately executes emergency safety measures. 5. Assessment and Feedback: The assessment module of the overall control system quantifies and scores the trainees' emergency response capabilities based on the collected data and preset assessment indicators, generates training reports, and provides feedback to trainees and coaches to identify areas for weakness in training. 6. Training Completion: After training is completed, the central control system sends a return-to-home command to the UAV system, and the UAV lands at the designated location; the vehicle-mounted system stops data acquisition, and the training system is shut down.

[0035] Example A: A driving emergency response training system based on UAV-vehicle collaboration includes a UAV system, a vehicle-mounted system, and a central control system. These three systems interact via a 5G wireless communication module. UWB positioning base stations are deployed within the training ground as a site positioning system to enhance positioning accuracy. The UAV system uses a customized DJI Air3S UAV as the main unit, equipped with a flight control module, a GPS+UWB fusion positioning module, a 5G communication module, a status monitoring module, a LiDAR obstacle avoidance module, and a detachable "simulated pedestrian" hazard simulation component. The flight control module receives commands from the central control system, enabling precise hovering and trajectory control with a response latency of ≤100ms. The status monitoring module monitors the UAV's battery level, flight attitude, and communication status in real time. When the battery level drops below 20%, it automatically sends an alarm signal to the central control system and executes a return-to-home procedure. The vehicle-mounted system is installed on a driving school training vehicle and includes a data acquisition module (containing a vehicle speed sensor, steering angle sensor, and brake pedal pressure sensor), a vehicle control linkage module (linked with the vehicle's braking system), a 5G communication module, and an audible and visual alarm module. The data acquisition module has a sampling frequency of 100Hz, which can accurately capture the trainee's operating actions. The vehicle control linkage module can brake the vehicle within 0.5 seconds upon receiving an emergency braking command from the central control system. The central control system includes an industrial control computer as its core hardware, equipped with a command generation module, data processing module, scenario configuration module, evaluation module, safety monitoring module, and display terminal. The scenario configuration module presets five common emergency scenarios, such as "pedestrians suddenly appearing ahead" and "non-motorized vehicles suddenly appearing to the side," and also allows instructors to customize the drone's appearance location (e.g., 50m or 30m from the vehicle), appearance timing (e.g., when the vehicle speed reaches 30km / h), and movement speed (e.g., 5km / h or 10km / h). The evaluation module presets reaction time thresholds (≤1.5s is considered passing) and braking accuracy (brake distance ≤10m is considered passing), and can generate training reports containing operation data curves and passing scores. The safety monitoring module presets a safe distance threshold of 5m between the drone and the vehicle. When the distance is less than 5m, it immediately sends a return-to-home command to the drone, an emergency braking command to the vehicle system, and triggers the alarm module. The workflow of this embodiment is as follows: 1. Scene configuration: The instructor selects the "sudden pedestrian in front" scene through the scene configuration module of the central control system, sets the drone's appearance position to 30m in front of the vehicle, the timing of appearance to when the vehicle speed reaches 30km / h, the moving speed to 5km / h, and sets the reaction time threshold in the evaluation index to 1.5s. 2. System Startup: The central control system sends a start command to the UAV system and the vehicle-mounted system. After completing the self-check, the UAV takes off and moves to the standby position at the edge of the training ground. The vehicle-mounted system starts the data acquisition module and begins to collect data such as vehicle speed and steering angle in real time. 3. Danger Simulation: Trainees drive training vehicles on the field. When the vehicle speed reaches 30km / h, the central control system sends a command to the drone system to control the drone carrying the "simulated pedestrian" component to fly quickly from the standby position to hover 30m in front of the vehicle to simulate a sudden pedestrian. 4. Data Acquisition and Safety Monitoring: After a trainee detects a sudden hazard and performs braking and steering operations, the onboard system's data acquisition module collects reaction time (the time from the drone's appearance to the trainee pressing the brake pedal), braking force, steering angle, and vehicle speed changes in real time, and transmits the data to the central control system; the safety monitoring module monitors the distance between the drone and the vehicle in real time to ensure that the distance between the two is greater than 5m. 5. Evaluation and Feedback: The evaluation module of the central control system analyzes the collected data. If the trainee's reaction time is 1.2s (≤1.5s) and the braking distance is 8m (≤10m), the evaluation is considered qualified, and a training report containing the operation data curve and the qualification score is generated and fed back to the trainee and the instructor. 6. Training Completion: After training is completed, the central control system sends a return-to-home command to the UAV system, and the UAV lands at the designated location; the vehicle-mounted system stops data acquisition, and the training system is shut down.

[0036] Example B: The difference between this example and Example A is that the hazard simulation component of the UAV system is replaced with a "simulated obstacle" label, and the scenario configuration is a custom scenario: the UAV appears from the side (right side) of the vehicle, at a distance of 20m from the vehicle, during the vehicle's turn, and at a speed of 10km / h; the evaluation module adds a "steering accuracy" evaluation index (steering angle deviation ≤10° is considered acceptable). During training, the trainee's operational data when facing sudden side obstacles while turning is collected and evaluated in real time, enabling targeted emergency response training.

[0037] Compared with existing technologies, this invention has the following beneficial effects: 1. Enhanced training realism and effectiveness: By simulating sudden hazards using drones and combining them with actual training grounds and vehicles, a near-realistic emergency scenario is constructed. Trainees' visual perception and operational experience are highly consistent with actual driving, and the reaction patterns developed during training can be directly transferred to actual driving, significantly improving training effectiveness. 2. Flexible scenario configuration and controllable risks: The central control system allows for flexible settings of the drone's appearance timing, location, movement trajectory, and hazard types, simulating various complex emergency scenarios and preventing trainees from forming pre-judgments. Simultaneously, the coordinated action of the safety monitoring module, obstacle avoidance module, and vehicle control linkage module can avoid collision risks in real time, ensuring training safety. 3. Precise quantitative assessment and personalized training: By collecting complete operational data from trainees in real time through the onboard system and combining it with the assessment module of the central control system, precise quantitative scoring of trainees' emergency response capabilities is achieved, generating targeted training reports and providing data support for personalized training optimization. 4. Strong system scalability: The hazardous material simulation components of the UAV adopt a detachable design, and different hazardous material symbols can be replaced according to training needs; the scene configuration module of the master control system supports custom scene parameters, which can adapt to the training needs of trainees at different stages and has a wide range of applications.

[0038] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A driving emergency response training system based on UAV-vehicle collaboration, characterized in that: include: An unmanned aerial vehicle (UAV) system for simulating sudden hazards includes a flight control module, a positioning module, a communication module, a status monitoring module, and a hazard simulation component. The vehicle-mounted system is used to monitor vehicle status and student operation data. The vehicle-mounted system includes a data acquisition module, a vehicle control linkage module, a communication module, and an alarm module. The overall control system is used to coordinate and control the unmanned aerial vehicle system and the vehicle-mounted system. The overall control system includes an instruction generation module, a data processing module, a scene configuration module, an evaluation module, and a safety monitoring module. The wireless communication module is used to connect the UAV system, vehicle system, and central control system to enable data exchange and coordinated control among the three.

2. The driving emergency response training system according to claim 1, characterized in that: The flight control module receives commands from the central control system to control the UAV's takeoff, landing, hovering, movement trajectory, and timing of appearance, simulating the sudden appearance of hazardous materials. The positioning module acquires the UAV's location information in real time and feeds it back to the central control system to ensure the UAV operates along a preset trajectory. The status monitoring module monitors the UAV's battery level, flight attitude, and communication connection status, sending alarm signals to the central control system and executing emergency return or forced landing procedures when abnormalities occur. The hazardous material simulation component is detachable and can be replaced with different types of hazardous material markers according to training scenario requirements. The UAV system also includes an obstacle avoidance module, which uses lidar or visual sensors to detect obstacles in the flight path in real time, enabling autonomous obstacle avoidance.

3. The driving emergency response training system according to claim 1, characterized in that: The data acquisition module includes a vehicle speed sensor, a steering angle sensor, a brake pedal pressure sensor, and an accelerator pedal sensor, which are used to collect trainee operation data and vehicle operating status data in real time and transmit them to the central control system. The vehicle control linkage module is used to receive instructions from the central control system in emergency situations and execute emergency braking or deceleration of the vehicle to ensure training safety.

4. The driving emergency response training system according to claim 1, characterized in that: The assessment module is used to quantify and score trainees’ emergency response capabilities based on their operational data and preset assessment indicators, and generate training reports. The safety monitoring module is used to monitor the relative position of the drone and the vehicle in real time and determine whether there is a risk of collision. When a safety hazard is detected, it immediately sends an emergency return command to the drone system, an emergency braking command to the vehicle system, and triggers the alarm module.

5. The driving emergency response training system according to claim 1, characterized in that: The overall control system also includes a display terminal, which is used to display the drone's location, vehicle status, student operation data, and training scene images in real time, so that instructors can monitor the training process in real time.

6. The driving emergency response training system according to claim 1, characterized in that: It also includes a site positioning system, which uses positioning base stations deployed at the training site to assist the UAV positioning module and vehicle-mounted system in achieving high-precision positioning.

7. The driving emergency response training system according to any one of claims 1 to 6, characterized in that: The alarm module is used to issue audible and visual alarms to trainees and instructors when safety hazards occur during training. These safety hazards include the drone being too close to the vehicle and the vehicle speeding.

8. A driving emergency response training method based on UAV-vehicle collaboration, characterized in that: The method applied to the UAV-vehicle collaborative driving emergency response training system according to any one of claims 1-7 includes the following steps: Scene configuration: Through the scene configuration module of the central control system, preset or custom training scene parameters are set. The training scene parameters include the type of hazard, the direction of appearance of the UAV, distance, movement speed, timing of appearance, and preset evaluation indicators. System startup: The central control system sends a startup command to the UAV system and the vehicle system. After completing the self-test, the UAV system takes off and goes to the preset standby position. The vehicle system starts the data acquisition module and begins to collect vehicle operation status data in real time. Hazard simulation and dynamic adjustment: When the training vehicle travels to the preset area, the UAV system simulates sudden hazards and runs along the preset trajectory according to the instructions of the central control system; the central control system combines the vehicle status data transmitted by the vehicle system to dynamically adjust the flight trajectory of the UAV, the timing and location of the hazards, so as to avoid the trainees' prediction and enhance the realism of the training scenario. Data Acquisition: The onboard system collects real-time operational data from trainees when facing sudden dangers and transmits it to the central control system; Safety monitoring: The safety monitoring module of the central control system monitors the relative position and operating status of the drone and training vehicle in real time. If a safety hazard is detected, emergency safety measures will be implemented immediately. Assessment and Personalized Feedback: The assessment module of the central control system quantifies and scores trainees’ emergency response capabilities based on the collected operational data and preset assessment indicators, generates a training report that includes trainees’ strengths and weaknesses, and can adjust the difficulty of subsequent training scenarios based on the training results, providing personalized training guidance. Training complete: The central control system sends a return-to-home command to the UAV system, and the UAV lands at the designated location; the vehicle-mounted system stops data acquisition, and the training system shuts down.

9. The driving emergency response training method based on UAV-vehicle collaboration according to claim 8, characterized in that: The types of hazards include pedestrians, non-motorized vehicles, and obstacles. The drone system replaces the corresponding hazard markers with detachable hazard simulation components. The dynamically adjusted parameters also include the drone's movement speed and the direction in which the hazard appears.

10. The driving emergency response training method based on UAV-vehicle collaboration according to claim 8, characterized in that: The collected student operation data includes reaction time, steering angle, braking force, accelerator pedal pressure change, and vehicle speed change data; the safety hazards include the drone being too close to the vehicle, vehicle speeding, and student operation errors; the emergency safety measures include sending an emergency return command to the drone system, sending an emergency braking or deceleration command to the vehicle system, and triggering the alarm module to issue an audible and visual alarm.

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