A Multimodal ADAS System for Unmarked Urban Scenarios Based on Road Context Understanding

Abstract

Comprehensive situational awareness is paramount to the effectiveness of advanced driver assistance systems (ADASs) used in daily urban traffic, particularly for the unmarked roads, which cannot fulfill the requirements of conventional ADAS systems. This paper proposed a stereovision-based multimodal ADAS system designed for expanding the usability of ADAS functions, including lane-keeping assist, adaptive cruise control, and precrash system, to normal urban scenarios with unmarked roads. At first, the physical road boundary and vehicle candidates are detected. Subsequently, the contextual information between the host vehicle, the road, and the other vehicles are correlated for both low-level object detection improvement and high-level road structure estimation. Finally, the required ADAS elements are generated based on the correlation results with respect to the system functionalities. Experimental results in various typical but challenging scenarios have substantiated the effectiveness of the proposed system, which could help increase the value of the existing ADAS system without major modifications or expense.