PCASim uses LLMs to integrate knowledge, data, and adversarial methods for generating promptable safety-critical urban traffic scenarios, with RL training for vehicle behaviors, reporting 12% better DSL accuracy, 8% higher scenario success rate, and 30% improved obstacle avoidance.
Surfelgan: Synthesizing realistic sensor data for autonomous driving
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions.
citing papers explorer
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PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment
PCASim uses LLMs to integrate knowledge, data, and adversarial methods for generating promptable safety-critical urban traffic scenarios, with RL training for vehicle behaviors, reporting 12% better DSL accuracy, 8% higher scenario success rate, and 30% improved obstacle avoidance.
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Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes
Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions.