A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles
As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and be...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/137254 |
| _version_ | 1826125035261132800 |
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| author | Lu, Chao Gong, Jianwei Lv, Chen Chen, Xin Cao, Dongpu Chen, Yimin |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Lu, Chao Gong, Jianwei Lv, Chen Chen, Xin Cao, Dongpu Chen, Yimin |
| author_sort | Lu, Chao |
| collection | NTU |
| description | As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness. |
| first_indexed | 2024-10-01T06:30:09Z |
| format | Journal Article |
| id | ntu-10356/137254 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:30:09Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1372542020-03-12T02:37:48Z A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles Lu, Chao Gong, Jianwei Lv, Chen Chen, Xin Cao, Dongpu Chen, Yimin School of Electrical and Electronic Engineering School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Autonomous Driving Driving Behavior As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness. Published version 2020-03-12T02:37:47Z 2020-03-12T02:37:47Z 2019 Journal Article Lu, C., Gong, J., Lv, C., Chen, X., Cao, D., & Chen, Y. (2019). A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles. Sensors, 19(17), 3672-. doi:10.3390/s19173672 1424-8220 https://hdl.handle.net/10356/137254 10.3390/s19173672 31450826 2-s2.0-85071756671 17 19 1 of 19 19 of 19 en Sensors © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering::Mechanical engineering Autonomous Driving Driving Behavior Lu, Chao Gong, Jianwei Lv, Chen Chen, Xin Cao, Dongpu Chen, Yimin A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles |
| title | A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles |
| title_full | A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles |
| title_fullStr | A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles |
| title_full_unstemmed | A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles |
| title_short | A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles |
| title_sort | personalized behavior learning system for human like longitudinal speed control of autonomous vehicles |
| topic | Engineering::Mechanical engineering Autonomous Driving Driving Behavior |
| url | https://hdl.handle.net/10356/137254 |
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