A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment. Issue 2 (1st October 2018)

• Record Type: Journal Article
• Authors: Zheng, Xunjia; Huang, Bin; Ni, Daiheng; Xu, Qing
• Is Part Of: Journal of intelligent and connected vehicles. Volume 1:Issue 2(2018)
• Page Start: 41
• Page End: 54
• Publication Date: 2018-10-01
• Subjects: Automated vehicles -- Advanced vehicle safety systems -- Autonomous driving -- Connected vehicles -- Environment perception -- Sensor information fusion; Intelligent transportation systems -- Periodicals; Automobiles -- Safety measures -- Periodicals; Autonomous vehicles -- Periodicals; Motor vehicles -- Technological innovations -- Periodicals; 629.2042
• DOI: 10.1108/JICV-02-2018-0004
• Languages: English
• ISSNs: 2399-9802

Abstract

Abstract : Purpose: The purpose of this paper is to accurately capture the risks which are caused by each road user in time. Design/methodology/approach: The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area. Findings: The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance. Originality/value: This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.