SafeMVDrive: Multi-view Safety-Critical Driving Video Synthesis in the Real World Domain

TL;DR

SafeMVDrive is a novel framework that generates realistic, safety-critical multi-view driving videos from real-world data, enhancing the testing and robustness evaluation of autonomous driving systems.

Contribution

It introduces a multi-view video synthesis framework that integrates safety-critical trajectory generation with realistic video rendering, leveraging scene understanding and diffusion models.

Findings

Increases collision rate in testing autonomous systems, indicating improved stress-testing.

Produces high-quality, realistic safety-critical driving videos.

Demonstrates effectiveness in evaluating autonomous driving planning modules.

Abstract

Safety-critical scenarios are rare yet pivotal for evaluating and enhancing the robustness of autonomous driving systems. While existing methods generate safety-critical driving trajectories, simulations, or single-view videos, they fall short of meeting the demands of advanced end-to-end autonomous systems (E2E AD), which require real-world, multi-view video data. To bridge this gap, we introduce SafeMVDrive, the first framework designed to generate high-quality, safety-critical, multi-view driving videos grounded in real-world domains. SafeMVDrive strategically integrates a safety-critical trajectory generator with an advanced multi-view video generator. To tackle the challenges inherent in this integration, we first enhance scene understanding ability of the trajectory generator by incorporating visual context -- which is previously unavailable to such generator -- and leveraging a GRPO-finetuned vision-language model to achieve more realistic and context-aware trajectory generation. Second, recognizing that existing multi-view video generators struggle to render realistic collision events, we introduce a two-stage, controllable trajectory generation mechanism that produces collision-evasion trajectories, ensuring both video quality and safety-critical fidelity. Finally, we employ a diffusion-based multi-view video generator to synthesize high-quality safety-critical driving videos from the generated trajectories. Experiments conducted on an E2E AD planner demonstrate a significant increase in collision rate when tested with our generated data, validating the effectiveness of SafeMVDrive in stress-testing planning modules. Our code, examples, and datasets are publicly available at: https://zhoujiawei3.github.io/SafeMVDrive/.