CARLA Simulator-Based Evaluation Framework Development of Lane Detection Accuracy Performance Under Sensor Blockage Caused by Heavy Rain for Autonomous Vehicle

Abstract

As self-driving cars have been developed targeting level 4 and 5 autonomous driving, the capability of the vehicle to handle environmental effects has been considered importantly. The sensors installed on autonomous vehicles can be easily affected by blockages (e.g., rain, snow, dust, fog, and others) covering the surface of them. In a virtual environment, we can safely observe the behavior of the vehicle and the degradation of the sensors by blockages. In this paper, the CARLA simulator-based evaluation framework has been developed and the assessment of lane detection performance under sensor blockage by heavy rain, which was analyzed by using the experimental data. Thus, we thoroughly note that the accuracy of lane detection for the autonomous vehicle has been decreased as the rainfall rate increases, and the impact of the blockage is more critical to curved lanes than straight lanes. Finally, we have suggested a critical rainfall rate causing safety failures of the autonomous vehicles, based on reasonably established rainfall equation based on experimental rain datasets. IEEE