Weather Effects on Obstacle Detection for Autonomous Car



Song, Rui ORCID: 0000-0002-8695-1522, Wetherall, Jon, Maskell, Simon ORCID: 0000-0003-1917-2913 and Ralph, Jason F ORCID: 0000-0002-4946-9948
(2020) Weather Effects on Obstacle Detection for Autonomous Car. In: 6th International Conference on Vehicle Technology and Intelligent Transport Systems, 2020-5-2 - 2020-5-4, Prague, Czeh Republic.

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Abstract

Adverse weather conditions have become a critical issue when developing autonomous vehicles and driver assistance systems. Training and testing autonomous vehicles in a simulation environment before deploying them into the market have many benefits due to lower costs and fewer risks. However, there are only a few works about weather influences on sensors in the simulated environment. A more systematic study of weather effects on the sensors used on autonomous cars is required. This paper presents a multi-sensor simulation environment under different weather conditions and examines the influence on environmental perception and obstacle detection for autonomous cars. The simulation system is being developed as part of a collaborative project entitled: Artificial Learning Environment for Autonomous Driving (ALEAD). The system incorporates a suite of sensors typically used for autonomous cars. Each sensor model has been developed to be as realistic as possible - incorporating physical defects and other artefacts found in real sensors. The influence of weather on these sensors has been simulated based on experimental data. The multi-sensor system has been tested under different simulated weather conditions and analysed to determine the effect on detection of a dynamic obstacle and a road lane in a 3D environment.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Autonomous Vehicle, Multiple Sensors, Weather Simulation, Virtual Environment, Object Detection
Depositing User: Symplectic Admin
Date Deposited: 17 Mar 2020 11:14
Last Modified: 04 Jun 2024 07:33
DOI: 10.5220/0009354503310341
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3079344