Early driver intention prediction plays a significant role in intelligent vehicles. Drivers exhibit various driving characteristics impairing the performance of conventional algorithms using all drivers' data indiscriminatingly. This paper develops a personalized driver intention prediction system at unsignalized T intersections by seamlessly integrating clustering and classification. Polynomial regression mixture (PRM) clustering and Akaike's information criterion are applied to individual drivers trajectories for learning in-depth driving behaviors. Then, various classifiers are evaluated to link low-level vehicle states to high-level driving behaviors. CART classifier with Bayesian optimization excels others in accuracy and computation. The proposed system is validated by a real-world driving dataset. Comparative experimental results indicate that PRM clustering can discover more in-depth driving behaviors than manually defined maneuver due to its fine ability in accounting for both spatial and temporal information; the proposed framework integrating PRM clustering and CART classification provides promising intention prediction performance and is adaptive to different drivers.
- Driver behavior prediction
- intelligent vehicle
- polynomial regression mixture (PRM)
- trajectory clustering
Yi, D., Su, J., Liu, C., & Chen, W-H. (2019). Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles. IEEE Transactions on Industrial Informatics, 15(6), 3693-3702. https://doi.org/10.1109/TII.2018.2890141