NOVA Framework Discovers Interpretable Models of Driver Behavior from Highway Trajectory Data
Researchers developed NOVA, a symbolic regression framework that automatically identifies interpretable mathematical models of car-following and lane-changing behavior from nearly 5 million driving observations. The system discovered a compact two-term acceleration model that outperforms previous approaches and transfers across different highway locations. The work bridges machine learning and traffic modeling, potentially improving autonomous vehicle design and traffic simulation.
NOVA is an autonomous symbolic regression framework designed to extract interpretable mathematical structures describing human driving behavior from raw trajectory data. Applied to 4.76 million driving observations from the NGSIM I-80 and US-101 freeway datasets, the system evaluated over 10,000 candidate algebraic structures using a deterministic Rust-powered search engine. For car-following, NOVA identified a compact two-term acceleration model achieving RMSE of 1.376 m/s² (R² = 15.57%), outperforming the previous best symbolic-regression baseline by 0.135 m/s². A single dominant nonlinear term emerged consistently across experiments and linked to established psychophysical collision-avoidance theory. For lane-changing, NOVA achieved 67.4% balanced accuracy on unseen drivers, surpassing existing baselines by 29.8 percentage points. The discovered models transfer between freeway sites with minimal performance loss, suggesting they capture generalizable aspects of human driving.
What's missing
The study does not discuss potential limitations in generalization to non-freeway driving (urban, residential), different vehicle types, or driving conditions beyond those in the NGSIM datasets. The relatively modest R² value (15.57%) for car-following suggests substantial unexplained variance, which may reflect inherent driver variability or missing contextual factors not addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity
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