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.
Bibliographical noteThis work was supported by the U.K.
Engineering and Physical Sciences Research Council Autonomous and
Intelligent Systems program under Grant EP/J011525/1 with BAE Systems as the leading industrial partner. The work of D. Yi was supported by the Chinese Scholarship Council for his study in the U.K. Paper no. TII18-1273.
- Driver behavior prediction
- intelligent vehicle
- polynomial regression mixture (PRM)
- trajectory clustering