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
2021-01-13
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
behaviour modelling , ego-vehicle , baseline model , multivariate cumulative distribution functions , developed motion planning algorithm , autonomous vehicles , mobile robots , mixed traffic environment , road traffic , ego vehicle , collision avoidance , relative acceleration , human driving behaviour , driver information systems , classical artificial potential approach , attractive potentials , road safety , road vehicles , classical arbitrary formulation , vehicle control , desired state , different stimulus parameters including type , driver model , path planning , drivers , traffic engineering computing , relative velocity
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