RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion

• URL: http://arxiv.org/abs/2505.03178v1
• Date: Tue, 06 May 2025 04:41:20 GMT
• Title: RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion
• Authors: Jiawei Wang, Xintao Yan, Yao Mu, Haowei Sun, Zhong Cao, Henry X. Liu,
• Abstract summary: Risk- Driving Environment (RADE) is a simulation framework that generates statistically realistic and risk-adjustable traffic scenes.<n>RADE learns risk-conditioned behaviors directly from data, preserving naturalistic multi-agent interactions with controllable risk levels.<n>We validate RADE on the real-world rounD dataset, demonstrating that it preserves statistical realism across varying risk levels.
• Score: 17.46462636610847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating safety-critical scenarios in high-fidelity simulations offers a promising and cost-effective approach for efficient testing of autonomous vehicles. Existing methods typically rely on manipulating a single vehicle's trajectory through sophisticated designed objectives to induce adversarial interactions, often at the cost of realism and scalability. In this work, we propose the Risk-Adjustable Driving Environment (RADE), a simulation framework that generates statistically realistic and risk-adjustable traffic scenes. Built upon a multi-agent diffusion architecture, RADE jointly models the behavior of all agents in the environment and conditions their trajectories on a surrogate risk measure. Unlike traditional adversarial methods, RADE learns risk-conditioned behaviors directly from data, preserving naturalistic multi-agent interactions with controllable risk levels. To ensure physical plausibility, we incorporate a tokenized dynamics check module that efficiently filters generated trajectories using a motion vocabulary. We validate RADE on the real-world rounD dataset, demonstrating that it preserves statistical realism across varying risk levels and naturally increases the likelihood of safety-critical events as the desired risk level grows up. Our results highlight RADE's potential as a scalable and realistic tool for AV safety evaluation.

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