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Lane change maneuver recognition via vehicle state and driver operation signals - Results from naturalistic driving data

G. Li ; S. E. Li ; Y. Liao ; W. Wang ; B. Cheng ; Fang Chen (Institutionen för tillämpad informationsteknologi, Interaktionsdesign (Chalmers))
Proceedings IEEE Intelligent Vehicles Symposium, IV 2015 Vol. 2015-August (2015), p. 865-870.
[Konferensbidrag, refereegranskat]

- 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.

Nyckelord: Active safety; lane change; maneuver recognition; feature selection; hidden Markov model (HMM)

Denna post skapades 2016-01-13. Senast ändrad 2016-10-28.
CPL Pubid: 230551


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Institutioner (Chalmers)

Institutionen för tillämpad informationsteknologi, Interaktionsdesign (Chalmers) (2011-2017)



Chalmers infrastruktur