lane change prediction

Causal Explanation Framework Enhances Lane Change Prediction Accuracy for Automated Driving

Researchers introduce a causal explanation framework for lane change prediction in automated driving. The approach combines causal discovery and deep structural causal modeling to achieve F1-scores above 95% and generate interpretable causal chains. This moves beyond correlation-based classification to more robust maneuver anticipation.