Solving Motion Planning Tasks with a Scalable Generative Model

TL;DR

This paper introduces a scalable generative model for simulating diverse driving scenarios, improving efficiency and realism, and enhancing motion planning tasks for autonomous vehicles.

Contribution

The paper presents a novel generative model capable of fast, high-fidelity simulation of driving scenes, operable in multiple modes, and validated on real-world datasets.

Findings

Achieves state-of-the-art performance in simulation realism and scene generation.

Outperforms prior methods in planning benchmarks.

Operates efficiently in both full- and partial-Autoregressive modes.

Abstract

As autonomous driving systems being deployed to millions of vehicles, there is a pressing need of improving the system's scalability, safety and reducing the engineering cost. A realistic, scalable, and practical simulator of the driving world is highly desired. In this paper, we present an efficient solution based on generative models which learns the dynamics of the driving scenes. With this model, we can not only simulate the diverse futures of a given driving scenario but also generate a variety of driving scenarios conditioned on various prompts. Our innovative design allows the model to operate in both full-Autoregressive and partial-Autoregressive modes, significantly improving inference and training speed without sacrificing generative capability. This efficiency makes it ideal for being used as an online reactive environment for reinforcement learning, an evaluator for planning policies, and a high-fidelity simulator for testing. We evaluated our model against two real-world datasets: the Waymo motion dataset and the nuPlan dataset. On the simulation realism and scene generation benchmark, our model achieves the state-of-the-art performance. And in the planning benchmarks, our planner outperforms the prior arts. We conclude that the proposed generative model may serve as a foundation for a variety of motion planning tasks, including data generation, simulation, planning, and online training. Source code is public at https://github.com/HorizonRobotics/GUMP/