Robustness Enhancement of Object Detection in Advanced Driver Assistance Systems (ADAS)
This addresses robustness issues in ADAS for autonomous driving safety, but it appears incremental as it combines existing components without major breakthroughs.
The paper tackles the instability of object detectors in ADAS due to high latency and environmental variations by proposing a unified system with a compact object detector and an environmental condition classifier, showing reliability and scalability in empirical results.
A unified system integrating a compact object detector and a surrounding environmental condition classifier for enhancing the robustness of object detection scheme in advanced driver assistance systems (ADAS) is proposed in this paper. ADAS are invented to improve traffic safety and effectiveness in autonomous driving systems where object detection plays an extremely important role. However, modern object detectors integrated in ADAS are still unstable due to high latency and the variation of the environmental contexts in the deployment phase. Our system is proposed to address the aforementioned problems. The proposed system includes two main components: (1) a compact one-stage object detector which is expected to be able to perform at a comparable accuracy compared to state-of-the-art object detectors, and (2) an environmental condition detector that helps to send a warning signal to the cloud in case the self-driving car needs human actions due to the significance of the situation. The empirical results prove the reliability and the scalability of the proposed system to realistic scenarios.