Game theory improves modeling of driver behavior at urban intersections
Empirically Calibrated Differential Games for Simulating Driver Interactions
Multiagent Systems
Summary
When cars meet at busy city intersections, predicting their moves is tricky. The authors created a computer model that treats each driver as someone trying to be safe, keep to their route, and avoid crashes while reacting to other drivers. They tested this model on real traffic data and found it predicts where cars will go better than other methods. This could help make driving simulations and safety systems more realistic.
What this means in practice
- •For autonomous vehicle developers: Simulate realistic vehicle interactions at intersections to improve autonomous driving algorithms and safety testing.
- •For traffic simulation engineers: Create more accurate city traffic simulators by incorporating calibrated game-theoretic driver behavior models.
Authors
Amirmohammad Khakpour, Nachuan Li, Pedram Beigi, Yu, Nie
Abstract
Game-theoretic models can resolve vehicle interactions without hand-written priority rules, but whether they describe human drivers once calibrated has rarely been tested. This study formulates a general-sum differential game of vehicle interactions at urban intersections, calibrates it to observed driving, and evaluates it on data not used for calibration. Each vehicle minimizes a cost that combines speed tracking, adherence to a map-derived lane route, control effort, gap keeping based on the intelligent driver model, a predictive conflict term, and a contact barrier, while perceiving the other vehicles with a delay. The game is solved with a feedback iterative linear-quadratic algorithm, and ten behavioral parameters per vehicle are calibrated by Bayesian optimization on trajectories recorded at four intersections in Washington, D.C. The calibrated game reproduces the trajectories of 819 vehicles with a mean position error of 1.00 m. When only each vehicle's route is known, as in a simulator, it is the most accurate of the compared models on 96 held-out vehicles (7.42 m), and ten-fold cross-validation shows that it is significantly more accurate than calibrated SUMO (by 3.61 m), an inverse reinforcement learning model (by 0.93 m), and independent and cooperative variants of the same model (by 0.33 and 0.32 m). The solutions, computed in 2.1 s for a typical interaction, are verified approximate equilibria. Finally, the conflict and steering weights, which the data do not identify, can be set from safety and comfort targets; together with a stiffer contact barrier, this removes the collisions that otherwise occur in 43% of tight closed-loop conflicts.