An investigation of following behavior and associated safety of MTWs in heterogeneous traffic

Damani, Jaikishan; Vedagiri, P.

doi:10.1080/19427867.2023.2203995

Cited by 2 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Easy to measure Time Advance (TAdv) [41] Another version of PET assumes road users do not change path and speed [42][43][44][45][46], as shown in Table 5. Deceleration-based SSM is generally determined by the ultimate braking capability of the vehicle and the required deceleration to avoid collisions [47][48][49][50][51][52].…”

Section: Pet and Its Derived Indicatorsmentioning

confidence: 99%

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

Xiong,

Zhang,

Chen

2023

WEVJ

View full text Add to dashboard Cite

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.

show abstract