The<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e783" altimg="si231.gif"><mml:mi>P</mml:mi><mml:mi>c</mml:mi></mml:math>metric: A performance measure for collision avoidance algorithms

Pradeep, Yazhini C.; Amezquita-Semprun, Kendrick; Rosario, Manuel Del; Chen, Peter C. Y.

doi:10.1016/j.robot.2018.08.005

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…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]. Meanwhile, researchers have developed a number of collision avoidance methods such as dynamic window approach [13] performance metrics [14].…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Zhang

Gao

et al. 2018

Sensors

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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.

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