Probabilistic Anticipation and Control in Autonomous Car Following

Abstract

Human-driven and autonomously driven cars of today act often reactively to the decisions of the cars they follow, which could lead to uncomfortable, inefficient, and sometimes unsafe situations in stop and go traffic. This paper proposes methods for probabilistic anticipation of the motion of the preceding vehicle and for the control of motion of the ego vehicle. We construct: 1) a Markov chain predictor based on the observed behavior of preceding vehicle and 2) a maximum likelihood motion predictor based on historical traffic speed at different locations and times. Heuristics are proposed for combining the two predictions to determine a probability distribution on the position of the preceding vehicle over a future planning horizon. A chance-constrained model predictive control framework is employed to optimize the motion of the ego vehicle, given the probabilistic prediction of motion of preceding vehicle. Effectiveness of the proposed approach is evaluated in multiple simulation scenarios.