Intelligent driving safety test system and method based on adult scooter target object

Abstract

The invention relates to the technical field of intelligent driving evaluation test equipment, and discloses an adult scooter target object which comprises a dummy model, a scooter model and a movable tray. The dummy model is fixed on the scooter model in a standing posture, and the scooter model is fixed on the movable tray; the scooter model comprises a front wheel, a frame and a rear wheel, the front end of the frame is connected with the front wheel, the rear end of the frame is connected with the rear wheel, and the bottom end of the frame is connected with the movable tray; the dummy model comprises limb parts; the limb part comprises upper limbs and lower limbs, the upper limbs are connected with the upper part of the front end of the frame, and the lower limbs are connected with the middle of the frame; the dummy model is sleeved with a skin, and the skin is provided with an infrared reflection coating, a radar reflection adjusting layer and wave-absorbing cotton. The movable tray comprises a dragging type tray and a self-driven type movable flat plate. The problem that at present, an adult riding scooter in a real road scene cannot be replaced can be solved.

Description

[0001] In the field of technology This invention relates to the field of intelligent driving evaluation and testing equipment technology, specifically to an intelligent driving safety testing system and method based on an adult scooter as the target object. Background Technology

[0002] With the continuous development of intelligent connected vehicle technology, the safety and reliability verification of autonomous driving systems has become a core issue of concern in the industry. Among these, the testing and verification of autonomous driving algorithms and hardware such as radar are crucial steps in ensuring the systematic safety and reliability of autonomous driving systems. By utilizing relevant test targets, the performance of autonomous driving systems in complex traffic environments can be effectively verified, providing important evidence for the optimization and improvement of the technology.

[0003] Currently, while various test targets exist, such as bicycles and electric bicycles, these targets, to some extent, meet the simulation needs of the motion characteristics of regular traffic participants. However, with the diversification of short-distance urban travel modes, emerging transportation tools, represented by scooters, are rapidly integrating into mixed transportation systems and becoming high-frequency participants in the road environment. Scooters, however, possess unique motion patterns, such as acceleration by pushing off the ground and shifting the center of gravity, which are significantly different from traditional bicycles and scooters. Traditional bicycles and scooters primarily move in straight lines on real roads, making existing test targets insufficient for simulating the complex trajectories of scooters. Consequently, the validation of autonomous driving algorithms on characteristic roads in China is inadequate.

[0004] Furthermore, the physical characteristics of scooters themselves pose a more stringent challenge to the detection and recognition capabilities of onboard sensing equipment (such as onboard cameras, millimeter-wave radar, and lidar). Their small overall size, low reflectivity, and the mixed target characteristics formed by their metal parts and riders result in irregular variations in radar reflectivity and visual recognition features, making them highly susceptible to missed detections or false alarms by onboard millimeter-wave radar, lidar, and cameras. With the gradual improvement of domestic and international autonomous driving testing standards, testing requirements for vulnerable road users (including scooter riders) have been explicitly incorporated into the regulatory system, and the performance deficiencies of existing testing tools can no longer meet these standardized verification needs. Summary of the Invention

[0005] The present invention aims to provide an intelligent driving safety testing system and method based on adult scooters as the target object, in order to solve the problem that the existing technology lacks a testing scheme that can accurately simulate adult riding scooters in real road scenarios, and thereby build an evaluation capability for intelligent connected vehicles to cover such traffic accident scenarios, and improve the evaluation capability of active safety performance of intelligent connected vehicles.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an adult scooter target object, comprising a detachably connected dummy model, a scooter model, and a movable tray; the dummy model is fixed to the scooter model in an upright riding posture, and the scooter model is fixed to the movable tray; the dummy model, the scooter model, and the movable tray work together to simulate a real-world adult riding a scooter target object; The scooter model includes a front wheel, a frame, and a rear wheel. The lower front end of the frame is fixedly connected to the front wheel, the lower rear end of the frame is fixedly connected to the rear wheel, and the bottom end of the frame is detachably connected to the movable tray. The dummy model includes a head, torso, and limbs; the limbs include upper limbs simulating a gripping posture and lower limbs simulating a pedaling posture, the upper limbs are fixedly connected to the upper part of the front end of the frame; the lower limbs are fixedly connected to the middle part of the frame. The dummy model is covered with a skin to simulate the visual perception of a scooter target object as consistent with that of a real scooter rider. The skin is covered with an infrared reflective coating, a radar reflective adjustment layer, and a wave-absorbing cotton. The infrared reflective coating is used to make the overall infrared reflective characteristics of the adult scooter target object consistent with those of a real rider. The radar reflective adjustment layer and the wave-absorbing cotton work together to make the overall radar cross-section of the adult scooter target object consistent with that of a real rider. The movable tray is used to move the scooter model, simulating the movement mode of an adult riding a scooter in reality; the movable tray includes a drag-type tray and a self-driven movable plate; the drag-type tray is used to simulate the linear movement mode of an adult riding a scooter, such as acceleration, constant speed, and deceleration, while the self-driven movable plate is used to simulate the linear or turning movement mode of an adult riding a scooter.

[0007] The principle and advantages of this scheme are: (1) Through the collaborative design of dummy model, scooter model and mobile tray, a highly realistic adult scooter target object is constructed that can replace the adult riding a scooter in real scene and be used for testing the active safety technology of intelligent vehicles. This design solves the problem that there is currently no way to replace the adult riding a scooter in real road scene in China, and significantly improves the evaluation coverage of intelligent connected vehicles in traffic accident scenarios involving vulnerable road users (VRUs) such as scooters, thereby promoting the optimization and improvement of vehicle active safety performance.

[0008] (2) The adult scooter target in this scheme can simulate the scenario of adults driving scooters on roads with Chinese characteristics. At the same time, it simulates the physical characteristics of scooters, such as small overall size and low reflectivity, which significantly increases the difficulty of target detection for the intelligent driving test vehicle. This allows for a more in-depth performance test of the intelligent driving test vehicle and effectively improves the vehicle's response performance to adult scooter scenarios. This ensures that the vehicle can brake in time when encountering adult scooter drivers, minimizing the occurrence of safety accidents from a technical perspective and ensuring driving safety and adult personal safety.

[0009] (3) The outer layer of the target object in this scheme adopts a skin design, which not only enhances the visual realism in appearance, making it closer to the real human body outline, and improves the recognition accuracy and realism of visual sensors such as vehicle cameras; at the same time, the skin material has good wear resistance and tear resistance, which can effectively protect the internal structure during the test, especially when the target object is hit by a vehicle and rubs against the ground, reducing the impact of external wear on internal components, extending service life, and ensuring test repeatability and safety.

[0010] (4) This solution adopts a modular design concept, dividing the entire target object into three major functional modules: a dummy model, a scooter model, and a mobile tray. The modules are connected in a detachable manner to achieve rapid assembly and disassembly. This design not only facilitates mass production, transportation, and on-site deployment, significantly improving testing efficiency, but also supports flexible configuration and reuse in different testing scenarios. In addition, it can avoid damage to the overall structure in extreme conditions such as collisions, improving the reliability and maintenance convenience of the target object.

[0011] (5) To improve the realism of the target object's perception in a multi-sensor environment, this solution introduces an infrared reflective coating, a radar reflective adjustment layer, and absorbent cotton. The infrared reflective coating controls the reflection data of the adult scooter target object under the detection of the vehicle's infrared sensing sensor in intelligent vehicle testing, making it closer to the reflection data of a real adult and a real scooter, thereby improving the accuracy of the target object in intelligent connected vehicle testing and evaluation by replacing a real adult riding a scooter. The radar reflective adjustment layer and absorbent cotton adjust the radar cross-section of the target object in the radar monitoring instrument, making it the same as the radar cross-section value of a real adult riding a scooter, to ensure that the adult scooter target object can replace a real adult scooter in scenario testing.

[0012] Preferably, as an improvement, the skin includes a person skin and a clothing skin; the person skin includes a hair area covered on the head, a face area covered on the head, and a hand area covered on the limbs; the clothing skin includes a clothing skin covered on the upper limbs and a trouser skin covered on the lower limbs.

[0013] Beneficial effects: The skin-covered design accurately replicates the biometric details and movement postures of an adult riding a scooter, such as weight shifts. These details can be captured as richer feature points by the visual sensors (such as cameras and LiDAR) of the intelligent driving system, resulting in visual perception consistency with a scooter in a real riding state. This avoids misjudgments caused by blurred target features (such as misidentifying a dummy as a static object), thereby improving the system's accuracy in recognizing "living adults." The clothing-covered design further enhances the realism of the target's appearance, allowing the intelligent driving system to encounter target forms that are closer to reality during testing.

[0014] Preferably, as an improvement, the infrared reflective coating includes a near-infrared reflective coating; the infrared reflective coating controls the infrared reflectivity of the adult scooter target to be 40-60% at a wavelength of 850-950nm.

[0015] Beneficial effects: The infrared reflective coating design simulates the natural thermal radiation and reflection behavior of the human body, preventing the dummy from deviating from the signal characteristics of a real cyclist due to excessively high infrared reflectivity (such as metallic reflection) or excessively low infrared reflectivity (such as light-absorbing materials). This allows the intelligent driving system to identify it as a "real living target," rather than an abnormal or atypical object. 850–950nm is a commonly used operating wavelength range for vehicle-mounted cameras, infrared imagers, and some LiDAR systems. By controlling the infrared reflectivity within a specific range (40%–60%) of this band, it is ensured that the target object exhibits reflective characteristics highly consistent with those of a real human body / scooter rider in the infrared sensing system, improving the sensor realism of the test scenario.

[0016] Preferably, as an improvement, the radar reflection adjustment layer controls the target to meet the reflection characteristic fluctuation range of the radar cross-section of a real adult and a scooter for 77GHz millimeter-wave radar at angles of 0°, 30°, 60°, 90°, 120°, 150°, and 180°.

[0017] Beneficial effects: The radar reflection adjustment layer highly replicates the radar perception characteristics of real targets, making the targets appear in radar images with a "radar fingerprint" that is almost identical to that of real cyclists.

[0018] Preferably, as an improvement, the frame includes a handlebar, a steering rod, and a pedal; one end of the steering rod is fixedly connected to the pedal, and the other end of the steering rod is fixedly connected to the handlebar.

[0019] Beneficial effects: The design of the handlebars, steering levers, and pedals replicates the control logic of a real scooter. In the test scenario, even if the dummy model does not actively steer, the rigid connection can ensure that the relative position and movement relationship between the handlebars and pedals are consistent with the real riding state, making the target shape and behavior characteristics perceived by the intelligent driving system more credible.

[0020] Preferably, as an improvement, the overall dimensions of the adult scooter target are: height 1800mm, width 415mm, and length 1050mm; In the height parameter, the head area accounts for 12%, the torso area accounts for 45%, and the lower limbs area accounts for 43%. In the width parameter, it is divided into the shoulder width adaptation area, the torso width area, and the hand control area, with the shoulder width adaptation area accounting for 60%, the torso width area accounting for 25%, and the hand control area accounting for 15%. In the length parameter, it is divided into the foot support area, the hip sitting area, and the upper body forward leaning area, with the foot support area accounting for 30%, the hip sitting area accounting for 25%, and the upper body forward leaning area accounting for 45%.

[0021] Beneficial effects: The size design of the adult scooter target object is based on statistical analysis, taking into account key human body dimensions such as height, shoulder width, and leg length of adult riders. At the same time, it also fully considers the structural characteristics of the scooter itself and reasonably matches the human-machine ratio, so that the appearance and overall proportion of the target object are highly close to those of real riders. It can cover the body characteristics of more than 90% of adult riders in China and can effectively represent all adults of different heights, weights and builds to present test results, thereby improving the accuracy, universality and reliability of the test results.

[0022] Preferably, as an improvement, it further includes a fixing component, which includes a horseshoe, an upright support rod, and an auxiliary support rod; the horseshoe and the upright support rod are located in the middle of the movable tray and are used to connect the scooter model and the movable tray; one end of the upright support rod is fixedly connected to the bottom end of the pedal, and the other end of the upright support rod is magnetically connected to the horseshoe and connected to the movable tray through the horseshoe; the auxiliary support rod is located at the rear end of the movable tray and is used to connect the dummy model and the movable tray; one end of the auxiliary support rod is connected to the pedal, and the other end of the auxiliary support rod is connected to the waist of the upper limb.

[0023] Beneficial effects: The fixed components ensure that the relative position between the dummy model and the scooter model always maintains a realistic riding posture, avoiding misjudgment by the perception system due to posture distortion.

[0024] Preferably, as an improvement, the dummy model and the skateboard model are connected by Velcro; both the dummy model and the skateboard model are made of polyethylene foam.

[0025] Beneficial effects: (1) The Velcro connection method is easy to operate and does not require tools or complex fixing devices. It can realize the rapid assembly and separation between the dummy model and the scooter model, thereby greatly improving the efficiency of test preparation and facilitating the frequent replacement or adjustment of the target object in different test scenarios, thus improving the flexibility and response speed of the test process. At the same time, the Velcro can provide sufficient connection strength to ensure that the dummy is stably fixed on the scooter, avoiding accidental detachment due to driving vibration or turning action, and realistically simulating the riding state. (2) Polyethylene foam is a new type of environmentally friendly packaging material with good plasticity and strong toughness. Using polyethylene foam to make dummy models and scooter models can provide human body shape simulation capabilities in actual production and manufacturing, and ensure that the adult scooter target object will not cause harm to the test vehicle and surrounding personnel during the test, thereby ensuring the safety of the test.

[0026] Preferably, as an improvement, it includes a control module, and an input module, a data acquisition module, an evaluation module, and a star rating module, which are respectively connected to the control module; Input module: Used to set the test parameters for the test vehicle and mobile pallet, and plan their preset movement trajectory; Data acquisition module: used to collect real-time motion status data and video data of the test vehicle and mobile pallet during their movement on the test track; The control module includes a processing module and an analysis and storage module; the processing module is used to receive motion state data and send it to the analysis and storage module; Analysis and storage module: used to store the collected data and analyze the vehicle actions generated by the test vehicle based on motion state data and video data; Evaluation module: This module calculates the primary indicator score based on vehicle actions and preset scoring criteria, and then calculates the final overall autonomous driving assistance score based on the primary indicator scores and their weights. The calculation method for the overall autonomous driving assistance score is as follows:

[0027] In the formula, The total score for autonomous driving assistance. This refers to the primary indicator number; and They are the serial numbers. The primary indicator scores and weights are given below. The primary indicator scores are calculated based on the secondary indicator scores, and the calculation method is shown in the following formula:

[0028] In the formula, The score for the primary indicator. This is the sequence number of the primary indicator. The number of valid test cases; For serial number For the primary indicator score, For the first The effective secondary score is calculated based on the test result score. Star rating module: used to generate corresponding star ratings based on the total score of autonomous driving assistance; the star rating scores are defined as follows: score < 1 is 1 star, 1 ≤ score ≤ 1.5 is 2 stars, 1.5 < score ≤ 2 is 3 stars, 2 < score ≤ 2.5 is 4 stars, and 2.5 < score ≤ 3 is 5 stars.

[0029] Beneficial Effects: This solution, through the setup of control, data acquisition, evaluation, and star-rating modules, achieves accurate reproduction of urban road scenarios and the characteristics of adult scooters, forming a systematic testing and evaluation mechanism for the safety performance of autonomous vehicles. This ensures the scientific rigor and objectivity of the safety performance testing of autonomous vehicles against adult scooters, from scenario reproduction to result output, providing quantifiable and traceable testing evidence for the safe implementation of autonomous driving technology.

[0030] Preferably, as an improvement, step S100: determine the test site and perform initialization processing on the test vehicle before the test vehicle enters the test site; Step S200: Control the test vehicle to enter the test field, drive along the preset route and keep it in the center of the lane; Step S300: Activate the autonomous driving or city navigation function of the test vehicle, perform a preset number of tests on the preset scenarios in the test field, and calculate the corresponding scenario index score based on the test results of the test vehicle in the corresponding scenario. Step S400: Calculate the total score for autonomous driving or city navigation assistance based on the indicator scores and corresponding weights, and obtain the vehicle's star rating based on the total score for autonomous driving or city navigation assistance.

[0031] Beneficial Effects: The intelligent driving safety testing method for adult scooters in this scheme comprehensively evaluates the test vehicle's performance in scenario testing by acquiring test data from settings mounted on the test vehicle. Specifically, it assigns corresponding weights based on the importance of different test scenarios and quantitatively scores the test vehicle's performance by combining the vehicle's specific response behaviors in each scenario (such as braking, avoidance, and lane changing). This method fully considers the differences in risk under different traffic situations, achieving differentiation and refinement in the evaluation process, and effectively improving the scientific rigor, objectivity, and reliability of the test results.

[0032] In summary, the beneficial effects of this solution are: (1) This solution has designed a soft target object for adult scooters that conforms to the characteristics of Chinese roads by cleverly designing the structure and size of the dummy model, scooter model, mobile tray and fixed components, and ensuring that the target object can replace the group of adult scooters in the real scene, so as to improve the recognition ability of the autonomous driving algorithm and sensors in complex scenes and solve the shortcomings of the target object equipment in terms of authenticity, coverage and safety.

[0033] (2) This scheme can comprehensively simulate the main dangerous conditions of vehicle interaction with adult scooters in urban road environment. Through comprehensive testing of different scenarios, this scheme can highly restore the complex dynamic process involving adult scooters in real urban traffic, meet the authenticity of autonomous driving test of various scenarios of adult scooters on urban roads, and effectively verify the perception, decision-making and execution capabilities of intelligent driving system in various typical scenarios, so as to realize the safety and reliability assessment of autonomous driving function in practical application. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the structure of the adult scooter target object provided in an embodiment of the present invention. Figure 1 .

[0035] Figure 2 This is a schematic diagram of the structure of the adult scooter target object provided in an embodiment of the present invention. Figure 2 .

[0036] Figure 3 This is a left view of the adult scooter target object provided in an embodiment of the present invention.

[0037] Figure 4 This is a schematic diagram of the structure of the target object (covering) of the adult scooter provided in an embodiment of the present invention.

[0038] Figure 5 This is a left view of the adult scooter target object (covering) provided in an embodiment of the present invention.

[0039] Figure 6 This is a comparison chart of the RCS curves obtained by testing the adult scooter target at 0° relative to the radar in an embodiment of the present invention.

[0040] Figure 7 This is a comparison chart of the RCS curves obtained when the target object, an adult scooter, is at 30° relative to the radar in an embodiment of the present invention.

[0041] Figure 8 This is a comparison chart of the RCS curves obtained when the target object, an adult scooter, is at 60° relative to the radar in an embodiment of the present invention.

[0042] Figure 9This is a comparison chart of the RCS curves obtained when the adult scooter target is at 90° relative to the radar in an embodiment of the present invention.

[0043] Figure 10 This is a comparison chart of the RCS curves obtained when the target object, an adult scooter, is 120° relative to the radar in an embodiment of the present invention.

[0044] Figure 11 This is a comparison chart of the RCS curves obtained when the target object, an adult scooter, is 150° relative to the radar in an embodiment of the present invention.

[0045] Figure 12 This is a comparison chart of the RCS curves obtained when the target object, an adult scooter, is 180° relative to the radar in an embodiment of the present invention.

[0046] Figure 13 This is a schematic diagram of the intelligent driving safety testing system based on an adult scooter target provided in an embodiment of the present invention.

[0047] Figure 14 This is a test scenario diagram of an adult scooter target and a test vehicle in an intelligent driving safety test system based on an adult scooter target provided in an embodiment of the present invention.

[0048] The reference numerals in the accompanying drawings include: dummy model 1, head 110, torso 120, limbs 130, upper limbs 131, lower limbs 132, scooter model 2, front wheel 210, frame 220, handlebars 221, steering rod 222, pedals 223, rear wheel 230, moving tray 3, fixing component 4, horseshoe 410, upright support rod 420, auxiliary support rod 430, skin 5, figure skin 510, clothing skin 520. Detailed Implementation

[0049] The following detailed description illustrates the specific implementation method: In the current era of rapid development in the intelligent connected vehicle industry, the improvement of testing equipment and scenario libraries is a key link in promoting the implementation of technologies. China's "Pilot Program for Access and Road Access of Intelligent Connected Vehicles" clearly requires the improvement of testing equipment and scenario libraries. The production of adult scooters is a positive response to this policy, promoting the improvement of autonomous driving test scenario libraries, meeting the mandatory testing requirements for VRU (vulnerable road user) protection under regulations, and accelerating the product access process.

[0050] This means that testing for intelligent connected vehicles needs to be closer to real-world road environments to ensure the safety and reliability of vehicles in complex traffic scenarios. The mandatory testing requirements for VRU (Vulnerable Road User) protection further emphasize the importance of testing pedestrians, cyclists, and other vulnerable road users. Adult scooters, as a rapidly growing mode of transportation in China in recent years, have seen a dramatic increase in users and have become an indispensable part of road traffic. However, there is currently a lack of software targets for adult scooters that are suitable for China's road conditions. This makes it impossible to simulate the real-world scenarios of adults riding scooters, a vulnerable group, in intelligent connected vehicle testing. This clearly contradicts policy requirements and hinders product market access. Therefore, manufacturing such targets is an inevitable choice to align with policy guidance and promote the standardization of the industry.

[0051] Meanwhile, China's unique road environment, characterized by high pedestrian traffic, a complex composition of traffic participants, and diverse road infrastructure, makes the testing scenarios for intelligent connected vehicles more complex. The riding speed, agility, and trajectory of adult scooters differ from those of other traffic participants. Without a dedicated software target to simulate this group, the testing of intelligent connected vehicles suffers from serious flaws. Autonomous driving algorithms and sensors cannot be optimized and verified for the characteristics of adult scooters in testing, which significantly increases safety hazards in real-world driving and hinders the commercialization of intelligent connected vehicles. Therefore, it is crucial to manufacture software target models of adult scooters that conform to the characteristics of Chinese roads and ensure that these target models can represent real-world adult scooter users. This would improve the recognition capabilities of autonomous driving algorithms and sensors in complex scenarios, address the shortcomings of target-based equipment in terms of realism, coverage, and safety, and provide standardized and efficient testing equipment for the development and commercialization of intelligent connected vehicles.

[0052] Example 1: like Figure 1 As shown, an adult scooter target object includes a detachably connected dummy model 1, a scooter model 2, and a movable tray 3; the dummy model 1 is fixed to the scooter model 2 in a standing position, and the scooter model 2 is fixed to the movable tray 3; the dummy model 1, the scooter model 2, and the movable tray 3 work together to simulate a real-world adult riding a scooter target object.

[0053] The device employs a modular assembly design, allowing for rapid assembly and disassembly of the dummy model 1, scooter model 2, and mobile tray 3 to form a complete adult scooter target. This design not only significantly improves the assembly efficiency of the adult scooter target, facilitating rapid assembly and mass production, but also supports repeated use in different testing scenarios, enhancing the device's flexibility, practicality, and economy.

[0054] In this embodiment, both the dummy model 1 and the scooter model 2 are made of polyethylene foam, a new type of environmentally friendly packaging material with excellent plasticity and high toughness. Using polyethylene foam to make the dummy model 1 and scooter model 2 provides human form simulation capabilities during actual production and manufacturing, and ensures that the adult scooter target will not cause harm to the test vehicle or surrounding personnel during testing, thus guaranteeing test safety.

[0055] Specifically, like Figure 2 , Figure 3 As shown, the scooter model 2 includes a front wheel 210, a frame 220, and a rear wheel 230, used to realistically simulate the movement and appearance characteristics of an adult riding a scooter. The frame 220 serves as the core support structure, with its lower front end fixedly connected to the front wheel 210 to ensure stability during steering and riding. The lower rear end of the frame 220 is fixedly connected to the rear wheel 230, forming a front and rear wheel axle layout that conforms to the mechanical structure of an actual scooter. The bottom of the frame 220 is detachably connected to the moving tray 3, allowing the entire scooter model 2 to move stably on the test track or road surface with the moving tray 3, facilitating the simulation of dynamic traffic participant behavior in intelligent driving safety testing.

[0056] The frame 220 includes a handlebar 221, a steering rod 222, and a footrest 223. Specifically, one end of the steering rod 222 is fixed vertically or obliquely to the front end of the footrest 223, and the other end of the steering rod 222 is fixedly connected to the handlebar 221. The handlebar 221 has a T-shaped structure with symmetrical crossbars at both ends, simulating the operating handlebars of a real scooter. This not only enhances the realism of the adult scooter's shape but also provides a more representative feature profile for the visual recognition of intelligent driving systems (such as target detection and posture estimation). The footrest 223 is a flat plate structure used to support the feet of the dummy model 1. Its surface can be provided with anti-slip textures or markings to accurately match the dummy's foot posture and improve the overall simulation accuracy.

[0057] The movable tray 3 is the core hub that drives the scooter to simulate various motion models. Specifically, the movable tray 3 includes different types such as a drag-type tray and a self-propelled moving plate. The drag-type tray is used to simulate the linear motion patterns of an adult riding a scooter, such as acceleration, constant speed, and deceleration; the self-propelled moving plate is used to simulate the linear or turning motion patterns of an adult riding a scooter. Among them: Drag-and-drop tray: This tray has magnetic clips on both sides for securing the drag strap. In use, the scooter is moved by pulling the strap with external force (such as human or mechanical force). This method is primarily used to simulate the displacement changes caused by external forces acting on the scooter (such as the push-off motion when starting or a slight collision with another object). The drag-and-drop tray is simple to use and suitable for basic dynamic response testing.

[0058] Self-propelled mobile flatbed: This tray incorporates a built-in GNSS positioning system and intelligent control components, enabling autonomous movement. It can precisely control speed and acceleration to simulate the process of a cyclist accelerating by pushing off the ground. Different acceleration curves can be preset along the trajectory to mimic the starting, constant-speed driving, and deceleration processes at different speeds.

[0059] The dummy model 1 includes a head 110, a torso 120, and limbs 130. The limbs 130 include an upper limb 131 and a lower limb 132. The upper limb 131 is fixedly connected to the upper part of the front end of the frame 220; the lower limb 132 is fixedly connected to the middle of the frame 220. Specifically, the upper limb 131 is fixedly connected to the handlebars 221, and the lower limb 132 is fixedly connected to the pedals 223. In this embodiment, the dummy model 1 has simulated human facial features, closely resembling the facial features of an adult scooter target object collected by an intelligent driving system in a real-world scenario.

[0060] like Figure 4 , Figure 5 As shown, the dummy model 1 is covered with a skin 5 to simulate the visual similarity of the scooter target object to a scooter in a real riding state. The skin 5 includes a human skin 510 and a clothing skin 520. The human skin 510 includes a hair area covered on the head 110, a face area covered on the head 110, and a hand area covered on the limbs 130; the clothing skin 520 includes a clothing skin 5 covered on the upper limbs 131 of the limbs 130 and a pants skin 5 covered on the lower limbs 132. In this embodiment, drawing corresponding patterns on the skin 5 and changing the skin 5 to different colors can further improve the simulation capability of the adult scooter target object, thereby increasing the visual similarity between the adult scooter target object and a scooter in a real riding state, and improving the accuracy of the test results.

[0061] Meanwhile, the skin 5 is equipped with an infrared reflective coating, which makes the infrared reflectivity of the adult scooter target object 40-60% in the 850-950nm wavelength range. Specifically, the infrared reflective coating includes a near-infrared reflective coating. By adjusting the thickness and distribution of the infrared reflective coating, and based on the fact that the wavelengths of infrared characteristics perceived by existing intelligent connected vehicle sensors are mostly in the 850-950nm range, the infrared reflectivity of the target object in the 850-950nm wavelength range is adjusted so that the reflection data of the target object detected by the vehicle's infrared sensing sensor in intelligent vehicle testing is closer to the actual reflection data. This achieves the goal of improving the accuracy of using the target object to replace a real adult scooter in intelligent connected vehicle testing and evaluation. In this embodiment, the thickness and distribution of the infrared reflective coating can be dynamically adjusted according to actual conditions and testing requirements.

[0062] Meanwhile, different parts of the dummy model 1 are equipped with radar reflection adjustment layers and wave-absorbing cotton. The radar reflection adjustment layers and wave-absorbing cotton are used to adjust the radar cross section (hereinafter referred to as RCS) of the adult scooter target object in the radar monitoring instrument to be the same as the RCS value of an adult in a real riding state. Since polyethylene foam is used as the raw material of the target object, in the actual radar detection and debugging, the RCS value of the adult scooter target object in the radar is greater than the RCS value fed back by the real adult scooter. Therefore, in this embodiment, the RCS value of the adult scooter target object is mainly reduced by wave-absorbing cotton. Specifically, wave-absorbing cotton is glued and fixed under the skin 5 of different parts of the dummy model 1, and the RCS value at that point is reduced by the wave-absorbing cotton.

[0063] like Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 , Figure 12 As shown, in order to improve the radar perception realism of adult scooter targets in intelligent driving tests, this embodiment adds radar-absorbing cotton inside the skin 5 of each part of the target to reduce the abnormal echoes caused by polyethylene foam material, so that the radar reflection cross section of the target can be closer to the radar reflection cross section of an adult riding a scooter in real life.

[0064] Specifically, in this embodiment, the wave-absorbing cotton is located on both sides of the waist of the limb 130. Through the dielectric loss and magnetic loss mechanism of the material, it absorbs or attenuates the incident radar waves, suppresses strong reflection points, and optimizes the overall scattering distribution.

[0065] To verify the effectiveness of RCS adjustment, the target object with adjusted RCS was subjected to 77GHz millimeter-wave radar cross-section data tests at seven angles relative to the radar detector: 0°, 30°, 60°, 90°, 120°, 150°, and 180°. During the tests, the radar detector scanned the target object from different azimuth angles, acquiring its RCS values ​​at each angle, and comparing these values ​​with actual radar measurements from a real adult riding a scooter. in, Figure 6 - Figure 12 In this context, the Upper Boundary curve represents the upper boundary data, and the Lower Boundary curve represents the lower boundary data; both the upper and lower boundary data are obtained by detecting adults in real cycling conditions.

[0066] Test results show that the RCS values ​​obtained by this target object in radar detection tests at the above seven angles are all between the upper and lower boundary data of a real adult riding a scooter, and the overall trend is highly consistent with the distribution of real data. This indicates that the target object can fully meet the RCS reflection characteristic fluctuation range of a real adult riding a scooter, thus ensuring the accuracy of test evaluation when used as a substitute target for a real adult riding a scooter.

[0067] This design aims to create a target scooter that represents the posture and behavioral characteristics of most adults riding in China. Through meticulous ergonomic analysis and research into actual riding postures, the specific dimensions of the adult scooter target are: height 1800mm, width 415mm, and length 1050mm. These dimensions not only consider the average body size of adult riders but also fully reference the structural characteristics of the scooter itself, ensuring that the overall appearance and proportions closely approximate reality.

[0068] Specifically, the target object has a total height of 1800mm, designed based on the average height of Chinese adults (169.7cm for men and 158.0cm for women, data source: "Report on Nutrition and Chronic Diseases of Chinese Residents"). The height of the head area accounts for 12%, the height of the torso area accounts for 45%, corresponding to the torso length of an adult standing posture, and the height of the lower limb area accounts for 43%, matching the differences in adult leg length (the average leg length of Chinese adults is 77cm for men and 70cm for women, with an adjustment range covering 65-85cm). Through this design, the target object can be adapted to adult cyclists in the height range of 150cm-190cm in China, covering more than 95% of the adult population in China.

[0069] The 415mm width design is based on the body width of an adult rider, divided into a shoulder width adaptation zone, a torso width zone, and a hand control zone. The shoulder width adaptation zone accounts for 60% of the width, referencing the average shoulder width of adults in China (38.6cm for men and 35.4cm for women, data source: National Anthropometric Standards), and can accommodate people with a shoulder width of 32cm-45cm; the torso width zone accounts for 25%, corresponding to the lateral dimensions of an adult torso; and the hand control zone accounts for 15%, matching the width of the scooter handlebars and the grip spacing of adult hands. Simultaneously, considering the weight distribution characteristics of adults, the target object's load-bearing structure design can accommodate a weight range of 40kg-120kg. Through the distribution of internal filling materials with different densities (e.g., higher density in the core area of ​​the torso and lower density in the peripheral areas), it simulates the body force and posture deformation of people of different weights while riding, ensuring coverage for people of different body types, including thin, normal, slightly overweight, and obese, covering more than 92% of the weight range of adults in China.

[0070] The 1050mm length design focuses on the body's forward and backward extension range during cycling, divided into a foot support zone, a hip seating zone, and an upper body forward lean zone. The foot support zone accounts for 30% of the length, referencing adult foot length (the average adult foot length in China is 25.5cm for men and 23.5cm for women), accommodating people with foot lengths of 22cm-28cm, ensuring stable foot placement during cycling; the hip seating zone accounts for 25%, matching adult hip width (average 32cm for men and 30cm for women) and the hip support needs when seated; the upper body forward lean zone accounts for 45%, corresponding to the length change of the upper body from upright to a 30° forward lean during adult cycling (covering 420mm-530mm), simulating the forward lean posture at different cycling speeds (low-speed cruising, medium-speed riding, and high-speed sprinting).

[0071] The size design of the target object in this solution is not subjectively set, but is based on a large amount of adult anthropometric data in China. Through statistical analysis, the range of key dimensions such as height, weight, shoulder width, and leg length is determined, which can cover the body characteristics of more than 90% of adult cyclists in China. Therefore, it can represent all adults of different heights, weights and builds to present test results, thus improving the accuracy of the test results.

[0072] From an appearance standpoint, the target object uses highly realistic materials to create its skin 5, with a delicate and lifelike surface texture and natural color matching, further enhancing the realism of visual recognition. Meanwhile, detailed processing has been added to key areas (such as the head 110 and limbs) to make it closer to the actual morphological characteristics of a rider, which helps improve the recognition accuracy of intelligent connected vehicle sensors.

[0073] Furthermore, this solution further enhances the reflectivity of the target object to be similar to that of an adult riding a scooter in the monitoring of infrared sensors and radar detectors by adding an infrared reflective coating, a radar reflective adjustment layer, and absorbent cotton to the skin 5. This ensures that the adult scooter target object can replace an adult riding a scooter in scenario testing, thereby building the ability to evaluate intelligent connected vehicles covering such traffic accident scenarios.

[0074] In real-world testing, by placing a target object in a specific test scenario, replacing an adult in a real riding situation, and then conducting experiments, the performance of the vehicle's Automatic Emergency Braking (AEB) system and intelligent driving safety functions can be effectively evaluated. During the test, the ability of the intelligent connected vehicle to react promptly and correctly to the target object is observed and recorded, thereby refining the AEB test scenario and improving the active safety of intelligent connected vehicles.

[0075] In summary, the adult scooter target object designed in this solution can replace adults in actual scooter riding for scenario testing, thus providing strong support for building intelligent connected vehicles' evaluation capabilities covering such traffic accident scenarios.

[0076] like Figure 13 As shown, the intelligent driving safety test system based on an adult scooter target includes a control module, as well as an input module, a data acquisition module, an evaluation module, and a star rating module, which are respectively connected to the control module. Input module: Used to set the test parameters of the test vehicle and the mobile tray 3, and plan its preset movement trajectory; the test parameters include the starting position, movement trajectory, velocity curve and acceleration; Data acquisition module: used to collect real-time motion status data and video data of the test vehicle and mobile pallet 3 during their movement on the test track; the motion status data includes speed, acceleration, lateral position and longitudinal position; to facilitate subsequent behavior analysis and scene reconstruction.

[0077] The control module includes a processing module and an analysis and storage module. The processing module receives motion state data and sends it to the analysis and storage module. The analysis and storage module stores the collected data and analyzes the vehicle actions generated by the test vehicle based on the motion state data and video data. It can ensure the integrity and traceability of the test process data. All data is aligned by timestamp and sent to the analysis and processing module in a unified manner, providing a reliable data foundation for subsequent analysis.

[0078] Evaluation module: It is used to calculate the score of the first-level indicator based on the vehicle's actions and the preset scoring criteria, and to calculate the final total score of autonomous driving assistance based on the score of the first-level indicator and its weight. Star Rating Module: This module generates a star rating based on the total score of the autonomous driving assistance system. It provides an intuitive and standardized assessment of the intelligent driving safety performance of test vehicles, facilitating R&D evaluation and result presentation.

[0079] In this embodiment, the test vehicle is equipped with a complete intelligent driving system and features a high-precision positioning inertial navigation system, a video recording device, millimeter-wave radar, lidar, and an audio-visual alarm collector to comprehensively collect the relative motion state and interaction behavior between the test vehicle and the adult scooter target.

[0080] To ensure the accuracy and reliability of the test data, the system must meet the following technical requirements: (1) The video acquisition equipment installed inside and outside the test vehicle has a resolution of not less than 720P to ensure that the visual information of key scenes is clearly distinguishable; (2) The sampling and storage frequency of motion state data shall not be less than 100Hz to ensure high time resolution of the dynamic process; (3) The speed acquisition accuracy is no greater than 0.1 km / h; (4) The accuracy of horizontal and vertical position acquisition shall not exceed 0.02m; (5) Acceleration acquisition accuracy is not greater than .

[0081] After setting up the real-vehicle testing environment, real-vehicle tests were conducted on actual roads or closed tracks. During the tests, the following driving requirements were observed for the test vehicles: the test vehicles were to activate autonomous driving or City Navigation (NOA) functions; the vehicle warning threshold was set to a medium level to match typical user settings; the test vehicles maintained a constant speed, within ±2 km / h of the set speed; and the lateral distance deviation of the test vehicles did not exceed ±0.5m of the prescribed driving path, ensuring stable operation along the predetermined route. These testing conditions ensured the consistency and repeatability of the experimental process, providing high-precision and highly reliable data support for evaluating the safety performance of intelligent driving systems when facing vulnerable road users such as adult scooters.

[0082] The test results are evaluated based on the test data collected from actual vehicle testing. The specific evaluation method is as follows: The indicators for intelligent driving vehicles to activate autonomous driving or city navigation (NOA) assistance in the scenario of adult scooters at intersections are shown in Tables 1 and 3: Table 1. Target Indicator System for Adult Scooters Assisted by Autonomous Driving or City Navigation (NOA)

[0083] In a scenario involving adult scooters at intersections, intelligent driving vehicles with autonomous driving or City Navigation (NOA) assistance engaged are configured with three primary indicators: longitudinal collision avoidance capability, lateral collision avoidance capability, and cornering collision avoidance capability. The weights of each primary indicator are shown in Table 2. Table 2. Weights of Primary Indicators Included in Automated Driving or City Navigation (NOA) Assistance

[0084] Table 3. Scores of Secondary Indicators Included in Automated Driving or City Navigation (NOA) Assist.

[0085] The total score for autonomous driving or city navigation (NOA) assistance of the test vehicle is calculated based on the scores and weights of the primary indicators, rounded to two decimal places. The calculation method is shown in the following formula:

[0086] In the formula, The total score for assisting city navigation vehicles. This refers to the primary indicator number; and They are the serial numbers. The primary indicator scores and weights are given below. The primary indicator scores are calculated based on the secondary indicator scores, and the calculation method is shown in the following formula:

[0087] In the formula, The score for the primary indicator of urban navigation assistance. This is the sequence number of the primary indicator. The number of valid test cases; For serial number For the primary indicator score, For the first The effective secondary score is calculated based on the test result score.

[0088] To comprehensively evaluate the safety performance of the intelligent driving system when facing adult scooters, the test was divided into three typical scenarios: longitudinal collision avoidance, lateral collision avoidance, and cornering collision avoidance. A unified scoring mechanism and pass / fail rules were used for each scenario, as detailed below: Each test condition can be performed a maximum of 3 times. If the first 2 tests are passed, there is no need to perform the 3rd test, and the condition is considered to have passed. If at least 2 out of 3 tests are passed, the condition is considered to have passed the test. If 2 or more out of 3 tests are failed, the condition is considered to have failed. In addition, if a collision occurs or the autonomous driving assistance function is disengaged and manual intervention is required, the test will be terminated immediately and the test score will be 0.

[0089] like Figure 14 As shown, longitudinal collision avoidance capability rating: Test scenario: An adult scooter is blocking the road in the longitudinal direction. The test vehicle travels along the preset route afe, and the adult scooter is located in the center of the af segment, forming a longitudinal chase situation. Scoring Criteria: The test vehicle identifies a slow-moving adult scooter ahead. If it automatically changes lanes to avoid the obstacle or slows down and follows, and safely completes the avoidance maneuver, the test is passed and 3 points are awarded. If it automatically brakes and remains stationary without a collision, the test is passed and 1.5 points are awarded. If a collision occurs or the autonomous driving assistance function disengages and manual intervention is required, the test ends and 0 points are awarded. Each test condition can be performed a maximum of 3 times. If the requirements are met 2 out of 3 times, the test condition is considered passed. If the first 2 tests are both passed, the 3rd test is not performed. If the vehicle fails the test 2 or more out of 3 times, the test condition is failed.

[0090] Lateral collision avoidance capability rating Test scenario: When the test vehicle encounters an adult scooter at the far end of an intersection, it travels along the preset route afe. ​​The target of the adult scooter is located at the beginning of the bc section, forming a far-end crossover situation. Scoring Criteria: The test vehicle identifies a slow-moving adult scooter ahead. If the scooter enters the test vehicle's path without obstruction, and the vehicle can stop or slow down to avoid it before continuing, the test is passed and 3 points are awarded. If the scooter completely blocks the test vehicle's path, and the vehicle can stop to avoid a collision, the test is passed and 3 points are awarded. If the test vehicle makes an illegal lane change (such as crossing solid lines or double yellow lines) to avoid the scooter, the test is failed and 0 points are awarded. If a collision occurs or the autonomous driving assistance function is disengaged and manual intervention is initiated, the test ends and 0 points are awarded. Each test condition can be performed a maximum of 3 times. If the requirements are met 2 out of 3 times, the test is considered passed. If the first 2 tests are both passed, the 3rd test is not performed. If the vehicle fails 2 or more out of 3 tests, the test for that condition is failed.

[0091] Cornering collision avoidance rating: Test scenario: When turning right at an intersection, an adult scooter crosses the road and encroaches on the road. The test vehicle is traveling along the preset route afe, and the adult scooter is located at the beginning of the dge section, resulting in a left-turn collision. Scoring Criteria: The test vehicle must identify a slow-moving adult scooter ahead while turning. If the vehicle can stop or decelerate and then change lanes in an obscured path, the test is passed and 3 points are awarded. If the vehicle can stop or decelerate and then follow the scooter in an unobscured path, the test is passed and 3 points are awarded. If the vehicle can accelerate through the obscured path, the test is passed and 3 points are awarded. If the vehicle only stops in an obscured path without further response, the test is passed and 1.5 points are awarded. If an illegal lane change is used to avoid the scooter, the test is failed and 0 points are awarded. If a collision occurs or the driver manually takes over after disengaging the automated driving assistance function, the test ends and 0 points are awarded. Each test condition can be performed a maximum of 3 times. If the requirements are met 2 out of 3 times, the test is considered passed. If the first 2 tests are both passed, the 3rd test is not performed. If the vehicle fails the test 2 or more times out of 3 times, the test for that condition is failed.

[0092] In this embodiment, the test cases for the test vehicle are scored out of 3 points, the secondary indicators are scored out of 3 points, the primary indicators are scored out of 3 points, and the autonomous driving or city navigation (NOA) assisted test of the test vehicle is scored out of 3 points, which is used to evaluate the overall performance of the autonomous driving or city navigation (NOA) function of the test vehicle.

[0093] Based on the test scores of the test vehicles' autonomous driving or city navigation (NOA) assistance tests, a star rating will be given to achieve an intuitive classification of intelligent driving safety performance. The higher the score, the better the vehicle's performance in the relevant scenarios, and the higher its corresponding star rating.

[0094] The star rating system is as follows: 1 star for a score < 1, 2 stars for a score 1 ≤ score ≤ 1.5, 3 stars for a score 1.5 < score ≤ 2, 4 stars for a score 2 < score ≤ 2.5, and 5 stars for a score 2.5 < score ≤ 3.

[0095] In this embodiment, the intelligent driving safety testing system for adult scooter targets is based on the traffic flow characteristics of urban intersections containing adult scooter targets, particularly the travel behavior patterns of adult scooters as vulnerable road users. The test field includes three different scenarios: longitudinal, lateral, and turning, covering longitudinal collision avoidance capability and response tests, lateral collision avoidance capability and response tests, and turning collision avoidance capability and response tests. This comprehensively simulates the main dangerous situations involving vehicles interacting with adult scooters in urban road environments. Through comprehensive testing of different scenarios in the aforementioned areas, this solution can highly reproduce the complex dynamic processes involving adult scooters in real urban traffic, satisfying the authenticity of autonomous driving tests for various scenarios involving adult scooter targets on urban roads. This effectively verifies the perception, decision-making, and execution capabilities of the intelligent driving system in various typical scenarios, achieving a safety and reliability assessment of autonomous driving functions in practical applications. Furthermore, other traffic scenarios involving adult scooter targets include encountering a parking vehicle crossing an adult scooter target, and lane changing into an adult scooter target in urban areas.

[0096] The intelligent driving safety testing method based on adult scooter targets includes the following steps: Step S100: Determine the test site and initialize the test vehicle before it enters the test site; Specifically, the test site is a two-lane intersection, and the test surface is a level concrete or asphalt surface. During the test, no vehicles, obstacles, or other objects that interfere with the test appear. Traffic signs and markings are clearly visible and meet the requirements of GB 5768. The initialization process includes calibrating the functions of the radar, video recording instruments, and vehicle system to ensure that the vehicle under test can work normally before the interference source in the test site is activated. Step S200: Control the test vehicle to enter the test field, drive along the preset route and keep it in the center of the lane; Specifically, the default route is the AFE route.

[0097] Step S300: Activate the autonomous driving or city navigation function of the test vehicle, perform a preset number of tests on the preset scenarios in the test field, and calculate the corresponding scenario index score based on the test results of the test vehicle in the corresponding scenario. Specifically, The predicted scenarios include: longitudinal pursuit test, lateral collision test, and intersection turning collision test.

[0098] (1) Longitudinal pursuit test (i.e., longitudinal condition where an adult scooter occupies the lane): The test vehicle travels along the preset route afe, and the adult scooter target is located in the center of the af section, forming a longitudinal pursuit condition. After the test vehicle travels smoothly on the af straight section, the automatic driving mode is activated. The adult scooter target triggers a signal at least 30m before the automatic driving mode is activated and moves at a constant speed of 15km / h along the af direction. The test ends after the test vehicle generates an action and response to the adult scooter target. The actions generated by the vehicle include lane changing, deceleration, braking, collision, or manual intervention. The actions and responses are analyzed to determine whether they are qualified, and the corresponding scores and weight data are recorded. (2) Lateral Collision Test (i.e., encountering an adult scooter at a crossroads): The test vehicle travels along the preset route afe, with the adult scooter target located in section bc, forming a lateral crossing scenario. After the test vehicle travels smoothly on the straight section, the automatic driving mode is activated. The adult scooter target is automatically calculated based on the test vehicle's speed to ensure that it enters the intersection area within the TTC time of a head-on collision, thus recreating a real intersection conflict scenario. The test ends when the test vehicle takes action and responds to the adult scooter target. The actions taken by the vehicle include changing lanes, slowing down, braking, and exiting the function. The system analyzes whether the actions and responses are qualified and records the corresponding scores and weight data. (3) Intersection Turning Collision Test (i.e., when turning right at an intersection and encountering an adult scooter crossing the road): The test vehicle travels along the preset route afe, and the adult scooter target is located in segment dg, forming a left-turn collision scenario. After the vehicle travels smoothly on the straight section, the automatic driving mode is activated. The collision TTC time of the adult scooter target is automatically calculated based on the speed of the test vehicle to ensure that it enters the test vehicle's trajectory within the typical collision time window. The test ends after the test vehicle generates an action and response to the adult scooter target. The effective collision range is the range within which the front of the adult scooter target intrudes into the test vehicle's driving path and the test vehicle actively collides with the target. The actions generated by the test vehicle include lane changing, deceleration, braking, and function exit. The system analyzes whether the actions and responses are qualified and records the corresponding scores and weight data. Step S400: Calculate the total score for autonomous driving or city navigation assistance based on the indicator scores and corresponding weights, and obtain the vehicle's star rating based on the total score for autonomous driving or city navigation assistance. Specifically, the rules for classifying the safety rating of intelligent driving are as follows: An autonomous driving assistance rating score greater than 2.5 and less than or equal to 3 points is 5 stars; An autonomous driving assistance rating score greater than 2 and less than or equal to 2.5 is 4 stars; An autonomous driving assistance rating score greater than 1.5 and less than or equal to 2 points is 3 stars; An autonomous driving assistance rating score greater than 1 and less than or equal to 1.5 is 2 stars; An autonomous driving assistance rating score of 1 or less is 1 star.

[0099] With the rapid increase in car ownership, the number of traffic accidents is also on the rise year by year. Among them, "ghost pedestrian" accidents caused by adults riding scooters are becoming a new type of traffic safety hazard. This group often has a relatively weak awareness of traffic rules and frequently engages in behaviors such as crossing roads at will, going against traffic, or suddenly changing direction on urban roads. Because scooters are small, agile, and have low reflectivity, car drivers are very likely to cause collisions in emergency situations due to delayed judgment or operational errors (such as mistaking the accelerator for the brake).

[0100] In response to this situation, research on autonomous driving assistance technologies in the automotive field is receiving increasing attention, especially regarding the protection mechanisms for vulnerable road users (VRUs). However, current intelligent driving safety tests for autonomous vehicles typically focus on bicycles and electric bicycles as test subjects, severely neglecting the unique scenario of adults riding scooters. In fact, in real traffic environments, the speed (typically 5-15 km / h), height (approximately 0.7-1 meter), and riding posture (high center of gravity, agile steering) of adult scooters differ significantly from traditional non-motorized vehicles: Firstly, the standing riding posture results in a higher center of gravity and a larger exposed upper body area, increasing the risk of injury in collisions with vehicles; secondly, scooters lack physical protective structures and often appear at the intersection of pedestrian walkways and motor vehicle lanes, easily creating "blind spots" due to obstructed vision (such as roadside parking or guardrails); thirdly, some adult riders are distracted while riding, further increasing the probability of suddenly crossing the road.

[0101] This solution is specifically designed for the unique needs of adult scooter riders. It innovatively employs a combination of an adult dummy, a scooter model 2, and a moving tray 3 as the test subjects, realistically simulating scenarios such as standing while riding, crossing diagonally, and suddenly darting out of obstacles. By setting the relative speed, position, and path parameters between the test vehicle and the moving target, the solution focuses on evaluating the performance of intelligent driving in scenarios such as longitudinal pursuit, lateral collision, and intersection turning collision: whether it can quickly identify the scooter and rider's trajectory when the path is obscured or unobscured; and whether it can effectively brake within a safe distance when the rider suddenly changes direction or accelerates across. Through repeated testing, the algorithm parameters and hardware such as radar for intelligent driving can be optimized in a targeted manner. For example, the recognition threshold of millimeter-wave radar for low-altitude moving objects can be adjusted, and the deep learning capability of the camera for the combined features of "standing posture + scooter" can be improved to ensure that the system completes the entire process from recognition to braking within the golden reaction time of 0.5-2 seconds. This not only fills the gap in the existing testing system, but also forces the intelligent driving system to adapt to the adult scooter scenario—because this scenario has higher requirements for recognition speed and braking accuracy, the optimized system will perform more reliably when dealing with scenarios such as adults riding scooters. In testing the intelligent driving performance of vehicles, existing technologies use bicycles and electric bicycles as target objects to simulate real-life situations, resulting in limited reference value of test data for actual adult scooter riding scenarios. This solution, however, focuses on the adult scooter rider group, which can not only specifically reduce the accident rate in this scenario but also provide technical support for urban traffic management (such as optimizing scooter lane design and improving riding regulations). In the long run, this testing solution will help build a more refined road safety protection system, providing comprehensive safety protection for diverse traffic participants.

[0102] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A target object for an adult scooter, characterized in that: The adult scooter target includes a detachably connected dummy model, a scooter model, and a movable tray; the dummy model is fixed to the scooter model in a standing riding posture, and the scooter model is fixed to the movable tray; the dummy model, the scooter model, and the movable tray work together to simulate the target of an adult riding a scooter in reality. The scooter model includes a front wheel, a frame, and a rear wheel. The lower front end of the frame is fixedly connected to the front wheel, the lower rear end of the frame is fixedly connected to the rear wheel, and the bottom end of the frame is detachably connected to the movable tray. The dummy model includes a head, torso, and limbs; the limbs include upper limbs simulating a gripping posture and lower limbs simulating a pedaling posture, the upper limbs are fixedly connected to the upper part of the front end of the frame; the lower limbs are fixedly connected to the middle part of the frame. The dummy model is covered with a skin to simulate the visual perception of a scooter target object as consistent with that of a real scooter rider. The skin is covered with an infrared reflective coating, a radar reflective adjustment layer, and a wave-absorbing cotton. The infrared reflective coating is used to make the overall infrared reflective characteristics of the adult scooter target object consistent with those of a real rider. The radar reflective adjustment layer and the wave-absorbing cotton work together to make the overall radar cross-section of the adult scooter target object consistent with that of a real rider. The movable tray is used to move the scooter model, simulating the movement mode of an adult riding a scooter in reality; the movable tray includes a drag-type tray and a self-driven movable plate; the drag-type tray is used to simulate the linear movement mode of an adult riding a scooter, such as acceleration, constant speed, and deceleration, while the self-driven movable plate is used to simulate the linear or turning movement mode of an adult riding a scooter.

2. The target object for an adult scooter according to claim 1, characterized in that: The skin includes a person skin and a clothing skin; the person skin includes a hair area covered on the head, a face area covered on the head, and a hand area covered on the limbs; the clothing skin includes clothing skin covered on the upper limbs and trouser skin covered on the lower limbs.

3. The target object for an adult scooter according to claim 1, characterized in that: The infrared reflective coating includes a near-infrared reflective coating; the infrared reflective coating controls the infrared reflectivity of the adult scooter target to be 40-60% at a wavelength of 850-950nm.

4. The target object for an adult scooter according to claim 1, characterized in that: The radar reflection adjustment layer controls the target to meet the reflection characteristic fluctuation range of the radar cross-section of a real adult and a scooter at angles of 0°, 30°, 60°, 90°, 120°, 150°, and 180°.

5. The target object for an adult scooter according to claim 1, characterized in that: The frame includes a handlebar, a steering rod, and a pedal; one end of the steering rod is fixedly connected to the pedal, and the other end of the steering rod is fixedly connected to the handlebar.

6. The target object for an adult scooter according to claim 1, characterized in that: The overall dimensions of the adult scooter target are: height 1800mm, width 415mm, and length 1050mm. In the height parameter, the head area accounts for 12%, the torso area accounts for 45%, and the lower limbs area accounts for 43%. In the width parameter, it is divided into the shoulder width adaptation area, the torso width area, and the hand control area, with the shoulder width adaptation area accounting for 60%, the torso width area accounting for 25%, and the hand control area accounting for 15%. In the length parameter, it is divided into the foot support area, the hip sitting area, and the upper body forward leaning area, with the foot support area accounting for 30%, the hip sitting area accounting for 25%, and the upper body forward leaning area accounting for 45%.

7. The target object for an adult scooter according to claim 5, characterized in that: It also includes a fixing component, which includes a horseshoe, an upright support rod, and an auxiliary support rod. The horseshoe and the upright support rod are located in the middle of the movable tray and are used to connect the scooter model and the movable tray. One end of the upright support rod is fixedly connected to the bottom end of the pedal, and the other end of the upright support rod is magnetically connected to the horseshoe and connected to the movable tray through the horseshoe. The auxiliary support rod is located at the rear end of the movable tray and is used to connect the dummy model and the movable tray. One end of the auxiliary support rod is connected to the pedal, and the other end of the auxiliary support rod is connected to the waist of the upper limb.

8. The target object for an adult scooter according to claim 1, characterized in that: The dummy model and the skateboard model are connected by Velcro; both the dummy model and the skateboard model are made of polyethylene foam.

9. The intelligent driving safety testing system based on an adult scooter target object according to any one of claims 1-8, characterized in that: It includes a control module, as well as an input module, a data acquisition module, an evaluation module, and a star rating module, which are respectively connected to the control module; Input module: Used to set the test parameters for the test vehicle and mobile pallet, and plan their preset movement trajectory; Data acquisition module: used to collect real-time motion status data and video data of the test vehicle and mobile pallet during their movement on the test track; The control module includes a processing module and an analysis and storage module; the processing module is used to receive motion state data and send it to the analysis and storage module; Analysis and storage module: used to store the collected data and analyze the vehicle actions generated by the test vehicle based on motion state data and video data; Evaluation module: This module calculates the primary indicator score based on vehicle actions and preset scoring criteria, and then calculates the final overall autonomous driving assistance score based on the primary indicator scores and their weights. The calculation method for the overall autonomous driving assistance score is as follows: In the formula, The total score for autonomous driving assistance. This refers to the primary indicator number; and They are respectively serial numbers The primary indicator scores and weights are given below. The primary indicator scores are calculated based on the secondary indicator scores, and the calculation method is shown in the following formula: In the formula, The score for the primary indicator. This is the sequence number of the primary indicator. The number of valid test cases; For serial number For the primary indicator score, For the first The effective secondary score is calculated based on the test result score. Star rating module: used to generate corresponding star ratings based on the total score of autonomous driving assistance; the star rating scores are defined as follows: score < 1 is 1 star, 1 ≤ score ≤ 1.5 is 2 stars, 1.5 < score ≤ 2 is 3 stars, 2 < score ≤ 2.5 is 4 stars, and 2.5 < score ≤ 3 is 5 stars.

10. The intelligent driving safety testing method based on an adult scooter target object according to any one of claims 1-9, characterized in that: Step S100: Determine the test site and initialize the test vehicle before it enters the test site; Step S200: Control the test vehicle to enter the test field, drive along the preset route and keep it in the center of the lane; Step S300: Activate the autonomous driving or city navigation function of the test vehicle, perform a preset number of tests on the preset scenarios in the test field, and calculate the corresponding scenario index score based on the test results of the test vehicle in the corresponding scenario. Step S400: Calculate the total score for autonomous driving or city navigation assistance based on the indicator scores and corresponding weights, and obtain the vehicle's star rating based on the total score for autonomous driving or city navigation assistance.

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