驾驶人驾驶决策机制遵循最小作用量原理

A probabilistic risk assessment framework considering lane-changing behavior interaction

Driving risk assessment based on naturalistic driving study and driver attitude questionnaire analysis

Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

An integrated architecture for intelligence evaluation of automated vehicles

Behavioral decision‐making model of the intelligent vehicle based on driving risk assessment

Objective and Subjective Analysis to Quantify Influence Factors of Driving Risk*

A Novel Framework for Road Traffic Risk Assessment with HMM-Based Prediction Model

Over the past decades, there has been significant research effort dedicated to the development of intelligent vehicles and V2X systems. This paper proposes a road traffic risk assessment method for road traffic accident prevention of intelligent vehicles. This method is based on HMM (Hidden Markov Model) and is applied to the prediction of steering angle status to (1) evaluate the probabilities of the steering angle in each independent interval and (2) calculate the road traffic risk in different analysis regions. According to the model, the road traffic risk is quantified and presented directly in a visual form by the time-varying risk map, to ensure the accuracy of assessment and prediction. Experiment results are presented, and the results show the effectiveness of the assessment strategies.