Review of Intelligent Vehicle Driving Risk Assessment in Multi-Vehicle Interaction Scenarios

Abstract

With the rapid breakthroughs in artificial intelligence technology and intelligent manufacturing technology, automotive intelligence has become a research hotspot, and much progress has been made. However, a skeptical attitude is still held towards intelligent vehicles, especially when driving in a complex multi-vehicle interaction environment. The interaction among multi-vehicles generally involves more uncertainties in vehicle motion and entails higher driving risk, and thus deserves more research concerns and efforts. Targeting the safety assessment issue of complex multi-vehicle interaction scenarios, this article summarizes the existing literature on the relevant data collection methodologies, vehicle interaction mechanisms, and driving risk evaluation methods for intelligent vehicles. The limitations of the existing assessment methods and the prospects for their future development are analyzed. The results of this article can provide a reference for intelligent vehicles in terms of timely and accurate driving risk assessment in real-world multi-vehicle scenarios and help improve the safe driving technologies of intelligent vehicles.

Driving Style Recognition Method Based on Risk Field and Masked Learning Techniques

“…By modeling a driving risk field, one can assess environmental risks and gather relevant variable information about the current environmental conditions. Through real-time monitoring and analysis of driving risks, this model allows for the assessment of real-time risks [17][18][19][20][21][22][23][24][25] in driving environments and the planning of feasible paths [21]. It also facilitates the prediction of potential risks under different road and traffic conditions.…”