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

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.