A Framework to Analyze Noise Factors of Automotive Perception Sensors
Pak Hung Chan, Gunwant Dhadyalla, Valentina Donzella
Abstract
Automated vehicles (AVs) are one of the breakthroughs of this century. The main argument to support their development is increased safety and reduction of human and economic losses; however, to demonstrate that AVs are safer than human drivers billions of miles of testing are required. Thus, realistic simulation and virtual testing of AV systems and sensors are crucial to accelerate the technological readiness. In particular, perception sensor measurements are affected by uncertainties due to noise factors; these uncertainties need to be included in simulations. This letter presents a framework to exhaustively analyze and simulate the effect of the combination of noise factors on sensor data. We applied the framework to analyze one sensor, the light detection and ranging (LiDAR), but it can be easily adapted to study other sensors. Results demonstrate that single noise factor analysis gives an incomplete knowledge of measurement degradation and perception is dramatically hindered when more noises are combined. The proposed framework is a powerful tool to predict the degradation of AV sensor performance.