VILTA: A VLM-in-the-Loop Adversary for Enhancing Driving Policy Robustness

TL;DR

VILTA introduces a novel framework integrating a Vision Language Model directly into the training loop of autonomous driving systems, enabling the generation of diverse, challenging scenarios that improve safety and robustness in rare, critical situations.

Contribution

This work presents VILTA, a new approach that leverages VLMs for direct, fine-grained scenario editing within closed-loop training, surpassing traditional methods in diversity and challenge generation.

Findings

Enhanced safety and robustness of driving policies.

Improved handling of critical long-tail events.

Generation of diverse, plausible challenging scenarios.

Abstract

The safe deployment of autonomous driving (AD) systems is fundamentally hindered by the long-tail problem, where rare yet critical driving scenarios are severely underrepresented in real-world data. Existing solutions including safety-critical scenario generation and closed-loop learning often rely on rule-based heuristics, resampling methods and generative models learned from offline datasets, limiting their ability to produce diverse and novel challenges. While recent works leverage Vision Language Models (VLMs) to produce scene descriptions that guide a separate, downstream model in generating hazardous trajectories for agents, such two-stage framework constrains the generative potential of VLMs, as the diversity of the final trajectories is ultimately limited by the generalization ceiling of the downstream algorithm. To overcome these limitations, we introduce VILTA (VLM-In-the-Loop Trajectory Adversary), a novel framework that integrates a VLM into the closed-loop training of AD agents. Unlike prior works, VILTA actively participates in the training loop by comprehending the dynamic driving environment and strategically generating challenging scenarios through direct, fine-grained editing of surrounding agents' future trajectories. This direct-editing approach fully leverages the VLM's powerful generalization capabilities to create a diverse curriculum of plausible yet challenging scenarios that extend beyond the scope of traditional methods. We demonstrate that our approach substantially enhances the safety and robustness of the resulting AD policy, particularly in its ability to navigate critical long-tail events.