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Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving
arXiv – CS AI|Zilin Huang, Zhengyang Wan, Zihao Sheng, Boyue Wang, Junwei You, Yue Leng, Sikai Chen|
🤖AI Summary
Researchers developed Sim2Real-AD, a framework that successfully transfers VLM-guided reinforcement learning policies trained in CARLA simulation to real autonomous vehicles without requiring real-world training data. The system achieved 75-90% success rates in real-world driving scenarios when deployed on a full-scale Ford E-Transit.
Key Takeaways
- →Sim2Real-AD enables zero-shot transfer of simulation-trained RL policies to real autonomous vehicles without real-world training data.
- →The framework uses four key components: Geometric Observation Bridge, Physics-Aware Action Mapping, Two-Phase Progressive Training, and Real-time Deployment Pipeline.
- →Real-world testing on a Ford E-Transit achieved 90% success in car-following, 80% in obstacle avoidance, and 75% in stop-sign interactions.
- →This represents one of the first demonstrations of closed-loop deployment of CARLA-trained VLM-guided RL policies on full-scale vehicles.
- →The modular approach successfully bridges the gap between simulator observations and real-world vehicle control systems.
#autonomous-driving#reinforcement-learning#sim-to-real#vlm#carla#zero-shot-transfer#real-world-deployment
Read Original →via arXiv – CS AI
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