NeuroNCAP: Photorealistic Closed-loop Simulation for Evaluating the Safety of Autonomous Driving Models
The core message of this work is that current state-of-the-art end-to-end autonomous driving models exhibit critical flaws when navigating safety-critical scenarios in a closed-loop setting, despite performing well in nominal open-loop driving. This highlights the need for advancements in the safety and real-world usability of these models.
Controllable and Reactive Driving Agents with Offline Reinforcement Learning
CtRL-Sim leverages return-conditioned offline reinforcement learning to enable the generation of reactive, closed-loop, and controllable driving agent behaviors within a physics-enhanced simulation environment.
Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents: Enhancing Realism and Flexibility
The author introduces ChatSim, a system enabling editable 3D driving scene simulations through natural language commands with realistic rendering. The approach leverages collaborative LLM agents to enhance realism and flexibility in autonomous driving simulations.
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