Accelerated Real-Life Testing of Automotive LiDAR Sensors as Enabler for In-Field Condition Monitoring

In the realm of automated driving, the need for highly reliable environmental perception sensors is paramount for ensuring the safe operation of advanced driving assistance systems (ADAS). Moreover, these systems represent a sharply increasing cost factor in modern vehicles. Hence, numerous works focus on investigating and modeling sen-sor performance under varying environmental conditions, considering for example ad-verse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the au-tomated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile de-terioration, output power reduction and intrinsic parameter drift, which are mostly ne-glected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to propose in-field condition monitoring approaches for early fail detection and predictive maintenance.