  In the realm of automated driving simulation and sensor modeling, the need
for highly accurate sensor models is paramount for ensuring the reliability and
safety of advanced driving assistance systems (ADAS). Hence, numerous works
focus on the development of high-fidelity models of ADAS sensors, such as
camera, Radar as well as modern LiDAR systems to simulate the sensor behavior
in different driving scenarios, even under varying environmental conditions,
considering for example adverse 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 automated, 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
deterioration, output power reduction and intrinsic parameter drift, which are
mostly neglected 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 suggest quantitative model validation metrics.
