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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Guofa Li Shengbo Eben Li Yuan Liao Wenjun Wang Bo Cheng Fang Chen |
| Copyright Year | 2015 |
| Description | Author affiliation: Div. of Interaction design, Chalmers Univ., Gothenburg, Sweden (Fang Chen) || Dept. of Automotive Eng., Tsinghua Univ., Beijing, China (Guofa Li; Shengbo Eben Li; Yuan Liao; Wenjun Wang; Bo Cheng) |
| Abstract | 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. |
| Starting Page | 865 |
| Ending Page | 870 |
| File Size | 845918 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781467372664 |
| DOI | 10.1109/IVS.2015.7225793 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-28 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Vehicles Hidden Markov models Wheels Acceleration Roads Feature extraction hidden Markov model (HMM) Active safety lane change maneuver recognition feature selection |
| Content Type | Text |
| Resource Type | Article |
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