Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego‐vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human‐like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data‐driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two‐dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment.
2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic
IET Intelligent Transport Systems ; 14 , 13 ; 1913-1922
2020-12-01
10 pages
Article (Journal)
Electronic Resource
English
ego vehicle , driver information systems , road safety , different stimulus parameters including type , drivers , road traffic , collision avoidance , traffic engineering computing , vehicle control , relative velocity , autonomous vehicles , desired state , classical artificial potential approach , behaviour modelling , road vehicles , driver model , classical arbitrary formulation , developed motion planning algorithm , multivariate cumulative distribution functions , mobile robots , mixed traffic environment , path planning , relative acceleration , baseline model , human driving behaviour , attractive potentials , ego‐vehicle
2-dimensional human-like driver model for autonomous vehicles in mixed traffic
IET | 2021
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