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.
The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems
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Multi-layered LSTM models achieve 76% accuracy for lane change prediction on highway ramps and 94% on straight highway sections for horizons up to 4 seconds using the ExiD drone dataset.
citing papers explorer
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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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From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps
Multi-layered LSTM models achieve 76% accuracy for lane change prediction on highway ramps and 94% on straight highway sections for horizons up to 4 seconds using the ExiD drone dataset.