Visual-based on-road vehicle detection: A transnational experiment and comparison

Abstract

As a key technique in ADAS (Advanced Driving Assistant System) or autonomous driving systems, visual-based on-road vehicle detection has been studied widely, while it faces still great challenges, among which are the complexity, diversity and unpredictable changes of the real-world environments. In the authors' previous work, an algorithm was developed in a probabilistic inference framework with its focus on solving the multi-view and occlusion problems at multi-lane motor way scenes. In this research, we seek to answer the questions: how efficient is the system during a long-term operation across a large area of changed conditions? To this end, a large scale experiment is conducted, where three testing data sets are developed containing the samples of more than 30,000 on Beijing's ring roads, 800 on Nagoya's fast road, and 3,000 on Nagoya's downtown streets, and the performance of visual-based vehicle detection concerning the multi-view and occlusion problems across extensive regions and at transnational environments are studied. We present our preliminary findings in this paper, which leads to a more extensive study in future work.