Enhancing Perception in Robot-Guided Vehicle Tests: A Rapid Long-Range Obstacle Detector

Abstract

The importance of accurate and efficient vehicle testing on proving grounds continues to grow, which makes automation a key solution to meet this growing demand. Robot-guided vehicles enable precise and reproducible execution of complex tests without a driver. In dynamic driving test scenarios, the ability to perceive obstacles at long distances plays a crucial role in ensuring the safety of both the test vehicle and individ-uals on the proving ground. To enable automated testing with robot-guided vehicles, Rapid and Long-Range Detection (RLD) algorithms need to be developed for on-board implementation. This paper presents a novel point cloud-based detector for LiDAR (Light Detection And Ranging) sensors that utilizes trajectory data and robot-guided vehicle information to achieve long-range obstacle detection with minimal latency. Our detector excels in computational efficiency, making it suitable for low-performance hardware. In various experiments, we demonstrate the performance of our detector in recognizing obstacles during highly dynamic driving maneuvers. Our method contributes significantly to the further development of automated vehicle testing and paves the way for safer and more efficient driving systems on proving grounds.