C-NCAP 2027 Agricultural Three-Wheeler AEB Test Target
The C-NCAP 2027 Agricultural Three-Wheeler AEB Test Target is developed in accordance with the vulnerable road user active safety requirements of the China New Car Assessment Programme (C-NCAP) 2027 Edition.
The target represents a typical agricultural cargo three-wheeler commonly encountered in Chinese urban-rural and rural traffic environments. It provides a standardized, repeatable and crash-compatible test object for the evaluation and development of Autonomous Emergency Braking (AEB), Forward Collision Warning (FCW), perception systems and related ADAS functions.
The target is designed in accordance with C-NCAP 2027 TR06 — Soft Targets of Active Safety Test Technical Requirements. Its geometry reproduces the representative characteristics of an agricultural cargo three-wheeler, including the front wheel, dual rear wheels, cargo bed, rider compartment and rider posture.
In addition to visual fidelity, the target is designed to provide representative sensing characteristics for camera, automotive millimeter-wave radar and LiDAR, enabling multi-sensor perception and sensor-fusion testing.
A lightweight, soft and modular crash-compatible construction reduces the risk of damage to the vehicle under test during impact. Major components, including the rider, body panels and cargo section, can be rapidly restored or replaced following a collision, allowing efficient repetitive testing.
Technical Specifications
| Item | Specification |
|---|---|
| Product Name | C-NCAP 2027 Agricultural Three-Wheeler AEB Test Target |
| Applicable Protocol | C-NCAP 2027 |
| Technical Reference | TR06 — Soft Targets of Active Safety Test Technical Requirements |
| Test Category | Vulnerable Road User Protection — Active Safety |
| Target Type | Three-Wheeler Target |
| Vehicle Configuration | Single front wheel, dual rear wheels, rider compartment and cargo bed |
| Overall Dimensions | Designed according to the dimensional requirements specified in C-NCAP 2027 TR06 |
| Target Geometry | Representative geometry of a typical Chinese agricultural cargo three-wheeler |
| Rider | Adult-size rider target in a representative driving posture |
| Visual Characteristics | Representative body, cargo bed, wheel and rider features |
| Camera Characteristics | Designed to meet C-NCAP visual detectability requirements |
| Radar Characteristics | Radar response designed according to C-NCAP TR06 requirements |
| LiDAR Characteristics | Representative three-dimensional geometry and laser reflection characteristics |
| Radar Frequency | Suitable for 76–81 GHz automotive radar testing |
| Construction | Lightweight, soft, modular and crash-compatible |
| Impact Performance | Designed to deform or separate during impact to minimize vehicle damage |
| Recoverability | Rapid recovery and reusable construction |
| Motion Interface | Compatible with low-profile robotic target carriers and propulsion platforms |
| Test Functions | AEB, FCW and ADAS perception validation |
| Typical Scenarios | Vehicle-to-three-wheeler conflict, crossing, longitudinal and mixed-traffic scenarios |
| Sensor Compatibility | Camera / Radar / LiDAR |
| Applications | C-NCAP testing, NCAP pre-assessment, AEB development and perception validation |
Key Features
Developed for C-NCAP 2027
Designed specifically for the new three-wheeler AEB assessment introduced in C-NCAP 2027.
China-Specific Traffic Representation
Represents agricultural cargo three-wheelers commonly encountered in Chinese mixed urban-rural traffic.
Multi-Sensor Detectability
Designed for camera, 76–81 GHz millimeter-wave radar and LiDAR perception systems.
Crash-Compatible Soft Construction
Lightweight and flexible components reduce the risk of damage to the vehicle under test in the event of an impact.
Modular and Recoverable Design
Major components can be rapidly restored or replaced for efficient repetitive AEB testing.
Applications
C-NCAP 2027 VRU active safety testing, three-wheeler AEB testing, FCW evaluation, camera object recognition, millimeter-wave radar detection, LiDAR point-cloud validation, sensor-fusion development, ADAS algorithm validation and NCAP pre-assessment.