NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity

NOVA, a symbolic regression framework, has been released for discovering interpretable car-following and lane-change models from raw driving data, utilizing a deterministic Rust-powered search engine to evaluate over 10,000 candidate algebraic structures. It achieved an RMSE of 1.376 m/s² on the intent-forecasting benchmark, outperforming the best symbolic-regression baseline by 0.135 m/s², and demonstrated robust transferability across freeway sites with minimal performance loss. This framework is significant for practitioners as it provides a method for deriving interpretable models that can enhance the understanding and prediction of driver behavior in autonomous systems.