Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation

TL;DR

Symphony enhances autonomous vehicle simulation realism by combining learned policies with beam search and hierarchical goal conditioning, resulting in more realistic and diverse agent behaviors.

Contribution

The paper introduces Symphony, a novel method that improves the realism and diversity of simulated agents through a combination of beam search and hierarchical goal modeling.

Findings

Symphony agents outperform baselines in realism and diversity.

The hierarchical goal approach prevents mode collapse during training.

Experiments on Waymo datasets validate the effectiveness of Symphony.

Abstract

Simulation is a crucial tool for accelerating the development of autonomous vehicles. Making simulation realistic requires models of the human road users who interact with such cars. Such models can be obtained by applying learning from demonstration (LfD) to trajectories observed by cars already on the road. However, existing LfD methods are typically insufficient, yielding policies that frequently collide or drive off the road. To address this problem, we propose Symphony, which greatly improves realism by combining conventional policies with a parallel beam search. The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator. However, it can also harm diversity, i.e., how well the agents cover the entire distribution of realistic behaviour, as pruning can encourage mode collapse. Symphony addresses this issue with a hierarchical approach, factoring agent behaviour into goal generation and goal conditioning. The use of such goals ensures that agent diversity neither disappears during adversarial training nor is pruned away by the beam search. Experiments on both proprietary and open Waymo datasets confirm that Symphony agents learn more realistic and diverse behaviour than several baselines.