Citation

Wong MK, Connie T, Goh MKO, Wong LP, Teh PS, Choo AL. F1000Res. 2021; 10: e928.

Copyright

(Copyright © 2021, F1000 Research)

DOI

10.12688/f1000research.72897.2

PMID

35350706

PMCID

PMC8924939

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.

Language: en

Keywords

Protective Devices; Autonomous vehicles; *Automobile Driving; *Accidents, Traffic/prevention & control; Autonomous Vehicles; Computer Vision; Data Collection; Forward Collision Warning; Lane detection; Object recognition