An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles

TL;DR

This paper presents an agent-based modeling framework for autonomous vehicle policy learning, emphasizing the benefits of vehicle connectivity and infrastructure-led approaches to improve decision-making in complex driving scenarios.

Contribution

It introduces a dynamic programming framework for in-vehicle policy learning with connectivity and proposes a novel infrastructure-led policy learning method using deep imitation learning.

Findings

V2V communication enhances autonomous decision-making.

Infrastructure-led policy learning improves vehicle behavior.

Simulation demonstrates effectiveness of proposed methods.

Abstract

Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications on policy learning has not been investigated in literature. This paper investigates a data driven driving policy learning framework through an agent-based modelling approaches. The contributions of the paper are two-fold. A dynamic programming framework is proposed for in-vehicle policy learning with and without connectivity to neighboring vehicles. The simulation results indicate that while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V) communication of information improves this capability. Furthermore, to overcome the limitations of sensing in a CAV, the paper proposes a novel concept for infrastructure-led policy learning and communication with autonomous vehicles. In infrastructure-led policy learning, road-side infrastructure senses and captures successful vehicle maneuvers and learns an optimal policy from those temporal sequences, and when a vehicle approaches the road-side unit, the policy is communicated to the CAV. Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework.