Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation

TL;DR

This paper introduces TrAAD, a novel traffic-aware imitation learning method for autonomous driving that integrates traffic simulation to optimize speed control for improved traffic flow and energy efficiency.

Contribution

It unifies traffic simulation with autonomous driving control via a distillation-style imitation learning approach focusing on speed supervision.

Findings

Autonomous vehicles can learn to accelerate for better traffic flow.

The method reduces overall energy consumption.

Improves traffic efficiency through learned speed control.

Abstract

While there have been advancements in autonomous driving control and traffic simulation, there have been little to no works exploring their unification with deep learning. Works in both areas seem to focus on entirely different exclusive problems, yet traffic and driving are inherently related in the real world. In this paper, we present Traffic-Aware Autonomous Driving (TrAAD), a generalizable distillation-style method for traffic-informed imitation learning that directly optimizes for faster traffic flow and lower energy consumption. TrAAD focuses on the supervision of speed control in imitation learning systems, as most driving research focuses on perception and steering. Moreover, our method addresses the lack of co-simulation between traffic and driving simulators and provides a basis for directly involving traffic simulation with autonomous driving in future work. Our results show that, with information from traffic simulation involved in the supervision of imitation learning methods, an autonomous vehicle can learn how to accelerate in a fashion that is beneficial for traffic flow and overall energy consumption for all nearby vehicles.