NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity
Researchers have developed NOVA, a symbolic regression framework designed to uncover interpretable models of driver behavior from trajectory data. Applied to millions of driving observations, NOVA identified a robust two-term acceleration model and achieved high accuracy in predicting car-following and lane-changing actions. The framework's discovered operators demonstrated strong zero-shot transferability between different freeway locations and significantly outperformed existing lane-change baselines. AI
IMPACT Introduces a novel framework for discovering interpretable AI models in complex domains like autonomous driving.