Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability

TL;DR

Vista is a novel driving world model that achieves high fidelity, generalizes well to unseen environments, and offers versatile controllability for autonomous driving applications.

Contribution

The paper introduces Vista, a comprehensive driving world model with innovative loss functions, a latent replacement approach, and a versatile control set, enhancing prediction accuracy and action controllability.

Findings

Outperforms state-of-the-art video generators in over 70% of tests.

Surpasses leading driving world models by 55% in FID and 27% in FVD.

Demonstrates effective generalization across multiple datasets.

Abstract

World models can foresee the outcomes of different actions, which is of paramount importance for autonomous driving. Nevertheless, existing driving world models still have limitations in generalization to unseen environments, prediction fidelity of critical details, and action controllability for flexible application. In this paper, we present Vista, a generalizable driving world model with high fidelity and versatile controllability. Based on a systematic diagnosis of existing methods, we introduce several key ingredients to address these limitations. To accurately predict real-world dynamics at high resolution, we propose two novel losses to promote the learning of moving instances and structural information. We also devise an effective latent replacement approach to inject historical frames as priors for coherent long-horizon rollouts. For action controllability, we incorporate a versatile set of controls from high-level intentions (command, goal point) to low-level maneuvers (trajectory, angle, and speed) through an efficient learning strategy. After large-scale training, the capabilities of Vista can seamlessly generalize to different scenarios. Extensive experiments on multiple datasets show that Vista outperforms the most advanced general-purpose video generator in over 70% of comparisons and surpasses the best-performing driving world model by 55% in FID and 27% in FVD. Moreover, for the first time, we utilize the capacity of Vista itself to establish a generalizable reward for real-world action evaluation without accessing the ground truth actions.