Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections
Semantic understanding of drivers' behavior features at intersections plays a pivotal role in the proper decision-making of a platoon. This paper presents a flexible framework to automatically extract the driver's driving features from observed temporal sequences of driving raw data and tr...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8957524/ |
| _version_ | 1819276431903948800 |
|---|---|
| author | Junjie Chen Jian Sun |
| author_facet | Junjie Chen Jian Sun |
| author_sort | Junjie Chen |
| collection | DOAJ |
| description | Semantic understanding of drivers' behavior features at intersections plays a pivotal role in the proper decision-making of a platoon. This paper presents a flexible framework to automatically extract the driver's driving features from observed temporal sequences of driving raw data and traffic light information. An approach, which contains two key sub-problems, is proposed to select the separated vehicles from the platoon in the vicinity of the intersection. Then, the first sub-problem, accurately capturing the drivers' driving behavior features under the impact of traffic lights, is addressed by using the Bayesian nonparametric approach, which could segment drivers' driving raw data temporal sequences into small analytically interpretable components (called driving primitives) without using prior knowledge. In addition, the extracted driving primitives are used to obtain the vehicle separation strategy (which is also the second sub-problem) by considering safety, efficiency, and energy consumption. Finally, 200 groups of raw data of human-driven vehicles approaching the intersection are used to validate the effectiveness of the proposed primitive-based framework. Experimental results demonstrate that the acceleration indeterminacy of separated vehicles could be decreased 37%-72% by segmenting the captured driving behavior features into 3 × 15 patterns. Moreover, the vehicle separation strategy could not only increase the efficiency, but also the safety, and the energy consumption could be decreased. |
| first_indexed | 2024-12-23T23:40:07Z |
| format | Article |
| id | doaj.art-eff54419298349ecb0bef50957109383 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:40:07Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eff54419298349ecb0bef509571093832022-12-21T17:25:43ZengIEEEIEEE Access2169-35362020-01-018177791779110.1109/ACCESS.2020.29662368957524Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized IntersectionsJunjie Chen0https://orcid.org/0000-0002-3536-3501Jian Sun1School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi’an Jiaotong University, Xi’an, ChinaSemantic understanding of drivers' behavior features at intersections plays a pivotal role in the proper decision-making of a platoon. This paper presents a flexible framework to automatically extract the driver's driving features from observed temporal sequences of driving raw data and traffic light information. An approach, which contains two key sub-problems, is proposed to select the separated vehicles from the platoon in the vicinity of the intersection. Then, the first sub-problem, accurately capturing the drivers' driving behavior features under the impact of traffic lights, is addressed by using the Bayesian nonparametric approach, which could segment drivers' driving raw data temporal sequences into small analytically interpretable components (called driving primitives) without using prior knowledge. In addition, the extracted driving primitives are used to obtain the vehicle separation strategy (which is also the second sub-problem) by considering safety, efficiency, and energy consumption. Finally, 200 groups of raw data of human-driven vehicles approaching the intersection are used to validate the effectiveness of the proposed primitive-based framework. Experimental results demonstrate that the acceleration indeterminacy of separated vehicles could be decreased 37%-72% by segmenting the captured driving behavior features into 3 × 15 patterns. Moreover, the vehicle separation strategy could not only increase the efficiency, but also the safety, and the energy consumption could be decreased.https://ieeexplore.ieee.org/document/8957524/Platoon separationnonparametric Bayesplatoon operating optimization at intersectionincreased operating safety and efficiency |
| spellingShingle | Junjie Chen Jian Sun Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections IEEE Access Platoon separation nonparametric Bayes platoon operating optimization at intersection increased operating safety and efficiency |
| title | Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections |
| title_full | Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections |
| title_fullStr | Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections |
| title_full_unstemmed | Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections |
| title_short | Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections |
| title_sort | platoon separation strategy optimization method based on deep cognition of a driver x2019 s behavior at signalized intersections |
| topic | Platoon separation nonparametric Bayes platoon operating optimization at intersection increased operating safety and efficiency |
| url | https://ieeexplore.ieee.org/document/8957524/ |
| work_keys_str_mv | AT junjiechen platoonseparationstrategyoptimizationmethodbasedondeepcognitionofadriverx2019sbehavioratsignalizedintersections AT jiansun platoonseparationstrategyoptimizationmethodbasedondeepcognitionofadriverx2019sbehavioratsignalizedintersections |