Product Description:
Designed for automotive active safety and ADAS testing. The product complies with IVISTA test protocol requirements.
Configuration:
Upper-body module and lower-body module.
Key Features:
Realistic visual, millimeter-wave radar and LiDAR characteristics equivalent to a real squatting child. Designed as a static target for RAEB, automatic parking and other special scenarios. Maximum impact speed up to 60 km/h.
Technical Specifications:
Height: 525 ± 20 mm.
Depth: 391 ± 20 mm.
Width: 350 ± 20 mm.
The Crouching Child Target is designed for C-NCAP 2027 vulnerable road user protection active safety testing and complex child traffic scenario development. It is used to simulate the typical human characteristics of children in a crouching, squatting, or low-posture state on roads, around vehicles, and in occluded environments, providing a standardized, repeatable, and collidable test object for vehicle automatic emergency braking systems (AEB), forward collision warning systems (FCW), and intelligent driving perception systems.
Compared to traditional standing or crossing child targets, the squatting child hasLow target height, small visible area, significant changes in body contour, and susceptibility to occlusion by vehicles, green belts, and roadside facilitiesfeatures, placing higher demands on the ability of cameras, millimeter-wave radar, and LiDAR to recognize low-profile targets.
The target is designed based on typical child anthropometric dimensions and squatting posture, simulating the geometric contours of the head, torso, arms, legs, and the human body in a squatting posture. It also accounts for the detection characteristics of mainstream automotive environment perception sensors such as cameras, millimeter-wave radar, and LiDAR, and can be used for validation of single-sensor and multi-sensor fusion systems.
The product adoptsLightweight, Flexible, Modular, and Collision-Tolerant StructureIn the event of a vehicle collision during AEB testing, the target can reduce damage to the tested vehicle through local deformation, module detachment, or overall tilting, and can quickly recover after the collision to meet the requirements of high-frequency repeated testing.
When combined with a target motion platform or a low-profile carrier platform, it can construct test scenarios such as children crouching, appearing after occlusion, crossing at a low posture, children around vehicles, and complex road conflicts, for the development, verification, and evaluation of vehicle active safety and intelligent driving systems.
Technical Parameters
| Item | Technical Description |
|---|---|
| Product Name | Squatting Child Target |
| English Name | Crouching Child Target |
| Target category | Child vulnerable road user target |
| Applicable regulations | Designed for C-NCAP 2027 and Related Active Safety Testing |
| Reference Technical Documents | C-NCAP 2027 TR06 'Technical Requirements for Soft Targets in Active Safety Testing' |
| Target human body | Child |
| Target posture | Crouching / Squatting posture |
| Body Proportions | Simulated according to specified child anthropometric characteristics |
| Overall Height | Determined by child body dimensions and specified crouching posture |
| Body structure | Head, torso, arms, and legs |
| Posture characteristics | Legs bent with lowered center of gravity, forming a typical child crouching profile |
| Visual characteristics | Simulates real child body contours, clothing, and crouching posture characteristics |
| Camera Compatibility | Supports testing of automotive visual perception system recognition |
| Radar Compatibility | Supports 76–81 GHz automotive millimeter-wave radar detection |
| LiDAR compatibility | Supports vehicle-mounted LiDAR point cloud detection and target classification |
| Target structure | Flexible, Lightweight, Modular |
| Collision Characteristics | Deformable, Tiltable, or Partially Detachable |
| Recoverability | Can be quickly reset and reused after collision |
| Mounting method | Can be mounted on a fixed base or a target motion platform |
| Motion Capability | Supports Static and Dynamic Test Scenarios |
| Typical Applications | AEB, FCW, low-profile target recognition, occluded target recognition, and ADAS algorithm validation |
| Applicable sensors | Camera / Radar / LiDAR |
Product features
1. Low-profile child target characteristics
The crouching posture significantly reduces the overall height and sensor-visible area of the child target, effectively evaluating the vehicle's ability to detect low-profile vulnerable road users.
2. Adaptability to Complex Occlusion Scenarios
Applicable to test scenarios where children are occluded by vehicles, green belts, guardrails, and other roadside facilities, simulating high-risk traffic situations such as a child suddenly appearing from an occluded area.
3. Multi-Sensor Perception Characteristics
The target accommodates the detection requirements of Camera, 76–81 GHz millimeter-wave radar, and LiDAR, and can be used for visual perception, radar perception, point cloud recognition, and multi-sensor fusion algorithm development.
4. Flexible Collision-Tolerant Structure
Designed with lightweight flexible materials and detachable structures to reduce the risk of damage to bumpers, vehicle bodies, and sensors in the event of a collision between the tested vehicle and the target.
5. Modular design and quick recovery
The main components of the human body adopt a modular design, allowing quick reset or replacement after collision, improving continuous AEB testing efficiency and reducing testing costs.
6. Supports static and dynamic scenarios
Can be used as a static low-profile target, or combined with a motion platform to construct scenarios such as lateral movement, appearance after occlusion, and complex trajectory conflicts.
Typical Applications
C-NCAP 2027 active safety testing for child vulnerable road users
Child AEB Testing
Squatting and Low-Profile Child Recognition
Occluded child "sudden appearance" scenario
Camera low-profile target recognition
76–81 GHz Millimeter-Wave Radar Detection
LiDAR point cloud recognition
Multi-sensor fusion development
AEB/FCW Function Validation
ADAS and Intelligent Driving Perception Algorithm Development
C-ICAP Parking Lot Squatting Child Scenario Test