Product Description
The Fallen Tire Target is designed for automotive intelligent safety, ADAS and autonomous driving function testing in controlled proving-ground environments. It provides a realistic and repeatable road debris surrogate for evaluating vehicle perception, obstacle recognition, path planning, emergency avoidance and lane-keeping related functions.
The product supports the requirements of the AI-SAP Automotive Intelligent Safety Assessment Protocoland is suitable for test scenarios involving scattered road debris, fallen tires, emergency obstacle avoidance, lane-change decision-making and road hazard recognition.
The target is developed with reference to GB/T 2977 tire size designation requirements and represents a typical radial passenger-car tire with a nominal width of 215 mm and an aspect ratio of 60.
Configuration
- Fallen tire target
- Flexible tire body structure
- Road-contact support interface
- Optional fixing or positioning base for repeated tests
Key Features
The target adopts a realistic radial tire appearance and geometric profile, providing representative visual characteristics for camera-based perception systems.
It supports multiple sensing solutions, including vision, millimeter-wave radar and LiDAR, enabling comprehensive evaluation of road debris recognition and obstacle avoidance performance.
The flexible and vehicle-friendly structure contains no sharp or rigid damaging components, reducing the risk of test vehicle damage during impact. The target is suitable for repeated closed-course testing and can be used independently or combined with other road debris and roadside facility targets to build complex traffic hazard scenarios.
Technical Specifications
Target type:Fallen tire / road debris surrogate
Tire type:Radial tire
Nominal tire width: 215 mm
Aspect ratio:60
Reference standard:GB/T 2977
Applicable protocol:AI-SAP Automotive Intelligent Safety Assessment Protocol
Sensor compatibility:Camera, millimeter-wave radar and LiDAR
Application scenarios:Road debris recognition, emergency avoidance, lane-keeping, path planning and autonomous driving safety validation