Abstract
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.
Highlights
Road traffic accidents are one of the largest and most serious public health problems in the world.A report published by the World Health Organization in 2015 stated that around 1.2 million people lost their lives in crashes on the roads around the world each year and was the leading cause of death among the young crowd 15 to 29 years of age
This paper presents a model for quantitative analysis of the road traffic risk, based on the slope resistance and acceleration resistance are worth considering under some specific conditions
This paper presents a model for quantitative analysis of the road traffic risk, based on the and steadily in front of her/him, but she/he does when the vehicle drives fast and abnormally
Summary
Road traffic accidents are one of the largest and most serious public health problems in the world.A report published by the World Health Organization in 2015 stated that around 1.2 million people lost their lives in crashes on the roads around the world each year and was the leading cause of death among the young crowd 15 to 29 years of age. To 90% rear-end and intersection accidents can be prevented if the vehicle can recognize the risk one second in advance. In this regard, the risk assessment for road traffic is of great importance for accident avoidance. Researchers proposed microscopic road risk assessment approaches by distinguishing the longitudinal and lateral of vehicle motion directions. Microscopic road traffic risk indicators in the longitudinal [7,8,9] include time to collision (TTC), inverse time to collision (TTCi), time headway (THW) threshold and the deceleration rate to avoid crash (DRAC), and those in the lateral include car’s current position (CCP), time to lane cross (TLC), and variable rumble strip (VRBS) [10,11,12]. Researchers have developed a number of collision avoidance methods such as dynamic window approach [13] performance metrics [14]
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