Trajectory clustering aided personalized driver intention prediction for intelligent vehicles
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 behaviours. Then various classifiers are evaluated to link low-level vehicle states to high-level driving behaviours. CART classifier with Bayesian optimization excels others in accuracy and computation. The proposed system is validated by a realworld driving dataset. Comparative experimental results indicate that PRM clustering can discover more in-depth driving behaviours than manually defined manoeuvres 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.
This work is jointly supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering