2‐dimensional human‐like driver model for autonomous vehicles in mixed traffic. Issue 13 (13th January 2021)

• Record Type: Journal Article
• Authors: Sharath, Mysore N.; Velaga, Nagendra R.; Quddus, Mohammed A.
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 13(2020)
• Page Start: 1913
• Page End: 1922
• Publication Date: 2021-01-13
• Subjects: road traffic -- road safety -- driver information systems -- road vehicles -- path planning -- mobile robots -- traffic engineering computing -- collision avoidance; driver model -- autonomous vehicles -- classical artificial potential approach -- human driving behaviour -- different stimulus parameters including type -- ego‐vehicle -- relative velocity -- relative acceleration -- drivers -- vehicle control -- ego vehicle -- desired state -- behaviour modelling -- developed motion planning algorithm -- attractive potentials -- classical arbitrary formulation -- multivariate cumulative distribution functions -- baseline model -- mixed traffic environment; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2020.0297
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.