C-TRAIL combines LLM commonsense with a dual-trust mechanism and Dirichlet-weighted Monte Carlo Tree Search to improve trajectory planning accuracy and safety in autonomous driving.
An environment for autonomous driving decision-making
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The MPC-RL framework cuts collision rates by 21% and raises success rates by 6.5% over pure MPC in intersection scenarios, with superior zero-shot transfer to highway merging.
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C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving
C-TRAIL combines LLM commonsense with a dual-trust mechanism and Dirichlet-weighted Monte Carlo Tree Search to improve trajectory planning accuracy and safety in autonomous driving.
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Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning
The MPC-RL framework cuts collision rates by 21% and raises success rates by 6.5% over pure MPC in intersection scenarios, with superior zero-shot transfer to highway merging.