White paper on Selected Environmental Parameters affecting Autonomous Vehicle (AV) Sensors

TL;DR

This paper examines how environmental parameters affect LiDAR and camera sensors in autonomous vehicles, aiming to inform testing standards and improve sensor robustness for safer AV operation.

Contribution

It provides an analysis of environmental impacts on AV sensors, especially LiDAR, and offers experimental insights to guide regulatory testing standards in Singapore.

Findings

Environmental factors significantly impact LiDAR and camera sensor performance.

Identified specific weaknesses of LiDAR under certain environmental conditions.

Experimental results inform better sensor testing and regulation standards.

Abstract

Autonomous Vehicles (AVs) being developed these days rely on various sensor technologies to sense and perceive the world around them. The sensor outputs are subsequently used by the Automated Driving System (ADS) onboard the vehicle to make decisions that affect its trajectory and how it interacts with the physical world. The main sensor technologies being utilized for sensing and perception (S&P) are LiDAR (Light Detection and Ranging), camera, RADAR (Radio Detection and Ranging), and ultrasound. Different environmental parameters would have different effects on the performance of each sensor, thereby affecting the S&P and decision-making (DM) of an AV. In this publication, we explore the effects of different environmental parameters on LiDARs and cameras, leading us to conduct a study to better understand the impact of several of these parameters on LiDAR performance. From the experiments undertaken, the goal is to identify some of the weaknesses and challenges that a LiDAR may face when an AV is using it. This informs AV regulators in Singapore of the effects of different environmental parameters on AV sensors so that they can determine testing standards and specifications which will assess the adequacy of LiDAR systems installed for local AV operations more robustly. Our approach adopts the LiDAR test methodology first developed in the Urban Mobility Grand Challenge (UMGC-L010) White Paper on LiDAR performance against selected Automotive Paints.