World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model

TL;DR

World4Drive introduces a perception annotation-free, end-to-end autonomous driving framework that uses vision foundation models to build latent world models for planning and decision-making, achieving state-of-the-art results.

Contribution

It presents a novel self-supervised approach for end-to-end autonomous driving using latent world models enriched with vision foundation model priors, eliminating the need for perception annotations.

Findings

Achieves 18.1% reduction in L2 error

46.7% lower collision rate

3.75x faster training convergence

Abstract

End-to-end autonomous driving directly generates planning trajectories from raw sensor data, yet it typically relies on costly perception supervision to extract scene information. A critical research challenge arises: constructing an informative driving world model to enable perception annotation-free, end-to-end planning via self-supervised learning. In this paper, we present World4Drive, an end-to-end autonomous driving framework that employs vision foundation models to build latent world models for generating and evaluating multi-modal planning trajectories. Specifically, World4Drive first extracts scene features, including driving intention and world latent representations enriched with spatial-semantic priors provided by vision foundation models. It then generates multi-modal planning trajectories based on current scene features and driving intentions and predicts multiple intention-driven future states within the latent space. Finally, it introduces a world model selector module to evaluate and select the best trajectory. We achieve perception annotation-free, end-to-end planning through self-supervised alignment between actual future observations and predicted observations reconstructed from the latent space. World4Drive achieves state-of-the-art performance without manual perception annotations on both the open-loop nuScenes and closed-loop NavSim benchmarks, demonstrating an 18.1\% relative reduction in L2 error, 46.7% lower collision rate, and 3.75 faster training convergence. Codes will be accessed at https://github.com/ucaszyp/World4Drive.