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Driver Behavior Estimation at Signalized Intersections Using a Physics-Constrained Decision-Conditioned Autoregressive Transformer
Not provided in the abstract
traffic predictiondriver behaviormachine learningautonomous systems
2609.16058
Builder Relevance
1h ago80%
Abstract
This paper presents a framework for predicting driver behavior at signalized intersections to reduce traffic accidents.
Reality Card
Core Claim
The proposed two-stage modeling framework accurately predicts driver stop-go decisions and longitudinal acceleration trajectories, outperforming baseline methods.
Method / Result
Achieved 0.49m/s^2 acceleration MAE and 0.62m distance MAE.
Limitations
The dataset and source code are publicly available, but real-world applicability may vary due to environmental factors not accounted for.
Paper to code
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