Learning driver-specific behavior for overtaking : a combined learning framework
Learning-based methods have gained increasing attention in the intelligent vehicle community for developing highly autonomous vehicles and advanced driving assistance systems (ADAS). However, traditional offline learning methods lack the ability to adapt to individual driving behavior. To overcome t...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/142721 |
| _version_ | 1824453688431738880 |
|---|---|
| author | Lu, Chao Wang, Huaji Lv, Chen Gong, Jianwei Xi, Junqiang Cao, Dongpu |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Lu, Chao Wang, Huaji Lv, Chen Gong, Jianwei Xi, Junqiang Cao, Dongpu |
| author_sort | Lu, Chao |
| collection | NTU |
| description | Learning-based methods have gained increasing attention in the intelligent vehicle community for developing highly autonomous vehicles and advanced driving assistance systems (ADAS). However, traditional offline learning methods lack the ability to adapt to individual driving behavior. To overcome this limitation, a combined learning framework (CLF) based on the Natural Actor Critic (NAC) learning and general regression neural network (GRNN) is developed in this paper. GRNN can be trained offline based on the historical data, while NAC is carried out online. In this way, the general behavior learned by the offline module can be reused and adjusted by the online module to capture the driver-specific behavior. Driving data collected from human drivers through a driving simulator are used to test the proposed learning framework. The complex overtaking behavior is selected to formulate the learning problem and test scenarios. Experimental results show that the proposed system performs well on learning driver-specific behavior for overtaking, and compared with the Gaussian mixture model-maximum-a-posterior method, CLF shows a more flexible performance when newly-involved drivers are considered. |
| first_indexed | 2025-02-19T03:10:24Z |
| format | Journal Article |
| id | ntu-10356/142721 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:10:24Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1427212020-06-29T03:38:04Z Learning driver-specific behavior for overtaking : a combined learning framework Lu, Chao Wang, Huaji Lv, Chen Gong, Jianwei Xi, Junqiang Cao, Dongpu School of Electrical and Electronic Engineering School of Mechanical and Aerospace Engineering Engineering::Electrical and electronic engineering Driving Behaviour Natural Actor Critic Learning-based methods have gained increasing attention in the intelligent vehicle community for developing highly autonomous vehicles and advanced driving assistance systems (ADAS). However, traditional offline learning methods lack the ability to adapt to individual driving behavior. To overcome this limitation, a combined learning framework (CLF) based on the Natural Actor Critic (NAC) learning and general regression neural network (GRNN) is developed in this paper. GRNN can be trained offline based on the historical data, while NAC is carried out online. In this way, the general behavior learned by the offline module can be reused and adjusted by the online module to capture the driver-specific behavior. Driving data collected from human drivers through a driving simulator are used to test the proposed learning framework. The complex overtaking behavior is selected to formulate the learning problem and test scenarios. Experimental results show that the proposed system performs well on learning driver-specific behavior for overtaking, and compared with the Gaussian mixture model-maximum-a-posterior method, CLF shows a more flexible performance when newly-involved drivers are considered. 2020-06-29T03:38:04Z 2020-06-29T03:38:04Z 2018 Journal Article Lu, C., Wang, H., Lv, C., Gong, J., Xi, J., & Cao, D. (2018). Learning driver-specific behavior for overtaking : a combined learning framework. IEEE Transactions on Vehicular Technology, 67(8), 6788 - 6802. doi:10.1109/TVT.2018.2820002 0018-9545 https://hdl.handle.net/10356/142721 10.1109/TVT.2018.2820002 2-s2.0-85044846242 8 67 6788 6802 en IEEE Transactions on Vehicular Technology © 2018 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Driving Behaviour Natural Actor Critic Lu, Chao Wang, Huaji Lv, Chen Gong, Jianwei Xi, Junqiang Cao, Dongpu Learning driver-specific behavior for overtaking : a combined learning framework |
| title | Learning driver-specific behavior for overtaking : a combined learning framework |
| title_full | Learning driver-specific behavior for overtaking : a combined learning framework |
| title_fullStr | Learning driver-specific behavior for overtaking : a combined learning framework |
| title_full_unstemmed | Learning driver-specific behavior for overtaking : a combined learning framework |
| title_short | Learning driver-specific behavior for overtaking : a combined learning framework |
| title_sort | learning driver specific behavior for overtaking a combined learning framework |
| topic | Engineering::Electrical and electronic engineering Driving Behaviour Natural Actor Critic |
| url | https://hdl.handle.net/10356/142721 |
| work_keys_str_mv | AT luchao learningdriverspecificbehaviorforovertakingacombinedlearningframework AT wanghuaji learningdriverspecificbehaviorforovertakingacombinedlearningframework AT lvchen learningdriverspecificbehaviorforovertakingacombinedlearningframework AT gongjianwei learningdriverspecificbehaviorforovertakingacombinedlearningframework AT xijunqiang learningdriverspecificbehaviorforovertakingacombinedlearningframework AT caodongpu learningdriverspecificbehaviorforovertakingacombinedlearningframework |