A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads [version 2; peer review: 2 approved]

Wong, MK, Connie, T, Goh, MKO, Wong, LP, Teh, PS and Choo, AL (2022) A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads [version 2; peer review: 2 approved]. F1000Research, 10. p. 928. ISSN 2046-1402

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Abstract

Background: Autonomous vehicles are important in smart transportation. Although exciting progress has been made, it remains challenging to design a safety mechanism for autonomous vehicles despite uncertainties and obstacles that occur dynamically on the road. Collision detection and avoidance are indispensable for a reliable decision-making module in autonomous driving. Methods: This study presents a robust approach for forward collision warning using vision data for autonomous vehicles on Malaysian public roads. The proposed architecture combines environment perception and lane localization to define a safe driving region for the ego vehicle. If potential risks are detected in the safe driving region, a warning will be triggered. The early warning is important to help avoid rear-end collision. Besides, an adaptive lane localization method that considers geometrical structure of the road is presented to deal with different road types. Results: Precision scores of mean average precision (mAP) 0.5, mAP 0.95 and recall of 0.14, 0.06979 and 0.6356 were found in this study. Conclusions: Experimental results have validated the effectiveness of the proposed approach under different lighting and environmental conditions.