TY  - JOUR
PY  - 2021//
TI  - A longitudinal car-following risk assessment model based on risk field theory for autonomous vehicles
JO  - International journal of transportation science and technology
A1  - Wu, Bing
A1  - Yan, Yan
A1  - Ni, Daiheng
A1  - Li, Linbo
SP  - 60
EP  - 68
VL  - 10
IS  - 1
N2  - This paper proposes a risk assessment method based on trajectory data which are used to quantify the risk faced by drivers for application in autonomous vehicles. A risk field is derived from the field theory of traffic flow, based on which the risk repulsion indicator of car-following is determined. By describing the repulsion force perceived by drivers in the process of car-following, the risk faced by drivers is assessed. The validity of the indicator is established from crash trajectory data obtained by simulation, and a binary logit model is employed to predict the crash. The result shows that the risk repulsion indicator based on risk field theory can distinguish crash states and non-crash states significantly. The prediction accuracy of binary logit model based on risk repulsion performs better than that of crash prediction model based on loop detector data.<p />  <p>Language: en</p>
LA  - en
SN  - 2046-0430
UR  - http://dx.doi.org/10.1016/j.ijtst.2020.05.005
ID  - ref1
ER  -