Methodology development and evaluation of automotive LiDAR performance in rain

The adoption of autonomous technologies in road vehicles has been accelerating, as evidenced by the advanced driver assistance systems (ADAS) offered in modern vehicles. Features like lane-keeping assistance, adaptive cruise control, and emergency braking rely on sensors to capture environmental data. However, sensor performance degrades in adverse weather conditions. For instance, light detection and ranging (LiDAR) sensors are affected when driving in rain due to soiling, which causes light attenuation by the droplets in the atmosphere and on the sensor lens cover. Therefore, autonomous vehicle developments require extensive field and lab testing on sensor perceptions in different environments and weather conditions to ensure safety through deploying soiling mitigation strategies. Although outdoor testing is realistic, its repeatability is limited by uncontrolled variables. On the other hand, indoor testing is controlled, but existing soiling simulation strategies produce unrealistic precipitation intensity and particle size distribution. Nevertheless, there is currently no standardized methodology for evaluating soiling effects on sensor perceptions, partly due to the lack of understanding of the droplet dynamics and interactions with the sensor optics, which depend highly on three identified fields – kinematics, materials, and aerodynamics. This thesis research investigates sensor performance when driving in rain from a multidisciplinary perspective, which examines the stress factors affecting the vision of time-of-flight (ToF) LiDARs, including vehicle soiling conditions and dynamics on various automotive surfaces. The approach to solving these problems involves three major areas of development and investigations: (1) develop methods for testing soiling in a controlled environment and establish sensor quantification metrics, (2) observe soiling behavior on various surface materials and identify the soiling mitigation criteria based on sensor performance, and (3) conduct fundamental studies on sensor optics-soiling interactions and develop phenomenological and semi-empirical models to explain the sensor soiling behaviors. In this thesis, a novel realistic rain simulation system was developed to conduct parametric studies on driving speeds, rain intensities, and sensor cover material properties using a wind tunnel, and evaluated their effects on LiDAR vision in these adverse conditions. Results suggested that both superhydrophobic and hydrophilic materials have advantages over hydrophobic materials for automotive ToF LiDAR applications when driving in rain.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Pao, Wing Yi

Advisor dc:contributor.advisor

• Agelin-Chaab, Martin

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1963

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1963