C-ICAP Goat Target

C-ICAP Goat TargetDeveloped for animal-type unconventional target scenarios in the driving assistance tests of the 'China Intelligent Connected Vehicle Technology Regulations (C-ICAP) 2026 Edition', used to simulate goats that suddenly appear or are located on the vehicle's driving path in road environments, providing standardized and repeatable test objects for intelligent assisted driving systems' recognition, risk assessment, path planning, and braking response to animal targets.

As intelligent assisted driving systems gradually expand from standard vehicle, pedestrian, and two-wheeler target recognition to complex open-road scenarios, the perception capability of non-standard traffic participants such as animals has become an important evaluation criterion for system safety performance. The C-ICAP 2026 edition adds unconventional targets such as animals, tires, and construction obstacles in driving assistance tests to assess vehicle target recognition and safety response capabilities in real complex road environments.

The goat target is designed as a 1:1 biomimetic model based on the typical appearance and body proportions of real adult goats, simulating key visual and geometric features such as the head, torso, limbs, ears, horns, and tail. The target also takes into account the detection requirements of vehicle-mounted environmental perception sensors such as cameras, millimeter-wave radar, and LiDAR, achieving consistent design of visual, radar, and LiDAR multimodal perception features.

The product adopts a lightweight, flexible, and modular structure, which can deform, detach, or fall over in the event of a vehicle collision, thereby reducing the risk of damage to the test vehicle, sensors, and test equipment. Main components can be quickly disassembled, replaced, and restored, making it suitable for a large number of repeated tests during the development phase of intelligent driving systems.

Used with a target motion platform or towing system, it can achieve stationary, lateral crossing, and other animal intrusion road scenarios for assisted driving system perception, AEB, risk recognition, path planning, and multi-sensor fusion performance verification.

Technical Parameters

Note: Specific external dimensions should be determined based on the final physical product and C-ICAP test configuration. The above dimensions are the product design range for a typical adult goat target.

Product Features

1. Designed for C-ICAP animal-shaped unconventional target testing

Developed for the newly added animal-shaped unconventional target testing requirements in C-ICAP 2026, used to evaluate vehicle perception, recognition, and safety response capabilities when facing non-standard road targets.

2. 1:1 high-fidelity animal appearance

Designed according to the typical body proportions of adult goats, realistically simulating important external features such as the head, torso, legs, ears, and horns that affect vehicle perception and recognition.

3. Multi-sensor feature design

Unlike animal models used only for visual display, this target is designed for Camera, 77 GHz millimeter-wave radar, and LiDAR perception systems simultaneously, and can be used for single-sensor and multi-sensor fusion testing.

4. Flexible Collision-Enabled Structure

The target adopts a low-mass, flexible structure, which can quickly collapse or partially detach upon vehicle collision, effectively reducing the risk of damage to the vehicle's bumper, grille, sensors, and body panels.

5. Modularity and rapid recovery

Leg, head, horn, and body modules can be connected using detachable connections based on the actual design, allowing quick reset or replacement after a collision to improve continuous testing efficiency.

6. Supports static and dynamic animal scenarios

It can be used both as a stationary road animal target and combined with a motion platform to construct complex test scenarios such as a goat crossing the road, suddenly entering, entering from the roadside, and appearing after being occluded.

Typical Applications

• C-ICAP animal-shaped unconventional target testing

• Intelligent assisted driving animal recognition testing

• AEB animal target test

• Camera Animal Classification and Recognition

• 76–81 GHz millimeter-wave radar target detection

• LiDAR point cloud recognition

• Multi-Sensor Fusion Algorithm Validation

• Animal Crossing Road Scenario Testing

• Intelligent driving risk prediction and path planning verification

• Vehicle Assisted Driving System Development and Pre-Evaluation