1 research outputs found
Weather Influence and Classification with Automotive Lidar Sensors
Lidar sensors are often used in mobile robots and autonomous vehicles to
complement camera, radar and ultrasonic sensors for environment perception.
Typically, perception algorithms are trained to only detect moving and static
objects as well as ground estimation, but intentionally ignore weather effects
to reduce false detections. In this work, we present an in-depth analysis of
automotive lidar performance under harsh weather conditions, i.e. heavy rain
and dense fog. An extensive data set has been recorded for various fog and rain
conditions, which is the basis for the conducted in-depth analysis of the point
cloud under changing environmental conditions. In addition, we introduce a
novel approach to detect and classify rain or fog with lidar sensors only and
achieve an mean union over intersection of 97.14 % for a data set in controlled
environments. The analysis of weather influences on the performance of lidar
sensors and the weather detection are important steps towards improving safety
levels for autonomous driving in adverse weather conditions by providing
reliable information to adapt vehicle behavior.Comment: 8 pages, will be published in the IEEE IV 2019 Proceeding