Lane change maneuver recognition via vehicle state and driver operation signals - Results from naturalistic driving data
Paper i proceeding, 2015

- Lane change maneuver recognition is critical in driver characteristics analysis and driver behavior modeling for active safety systems. This paper presents an enhanced classification method to recognize lane change maneuver by using optimized features exclusively extracted from vehicle state and driver operation signals. The sequential forward floating selection (SFFS) algorithm was adopted to select the optimized feature set to maximize the k-nearest-neighbor classifier performance. The hidden Markov models (HMMs), based on the optimized feature set, were developed to classify driver lane change and lane keeping maneuvers. Fifteen drivers participated in the road test for validation with an accumulation of 2,200 km naturalistic driving data, from which 372 lane changes were extracted. Results show that the recognition rate of lane change maneuver achieves 88.2%. The numbers are 87.6% and 88.8% for left and right lane change maneuvers, respectively, superior to the results from conventional classifiers. © 2015 IEEE.

maneuver recognition

hidden Markov model (HMM)

feature selection

lane change

Active safety


G. Li

Tsinghua University

Shengbo Li

Tsinghua University

Y. Liao

Tsinghua University

W. Wang

Tsinghua University

B. Cheng

Tsinghua University

Fang Chen

Chalmers, Tillämpad informationsteknologi, Interaktionsdesign (Chalmers)

Proceedings IEEE Intelligent Vehicles Symposium, IV 2015

Vol. 2015-August 865-870