<inline-formula> <tex-math notation="LaTeX">$DBO$ </tex-math></inline-formula> Trajectory Planning and <inline-formula> <tex-math notation="LaTeX">$HAHP$ </tex-math></inline-formula> Decision-Making for Autonomous Vehicle Driving on Urban Environment
A novel driving behaviour oriented (DBO) trajectory planner and hierarchical analytic hierarchy process (HAHP) decision maker are presented for intelligent vehicle. Since driving on structural road should satisfy actuator constraints and improve comfortableness as soon as possible, which strictly ob...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8901215/ |
| _version_ | 1818935696046424064 |
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| author | Dequan Zeng Zhuoping Yu Lu Xiong Zhiqiang Fu Peizhi Zhang Hongtu Zhou |
| author_facet | Dequan Zeng Zhuoping Yu Lu Xiong Zhiqiang Fu Peizhi Zhang Hongtu Zhou |
| author_sort | Dequan Zeng |
| collection | DOAJ |
| description | A novel driving behaviour oriented (DBO) trajectory planner and hierarchical analytic hierarchy process (HAHP) decision maker are presented for intelligent vehicle. Since driving on structural road should satisfy actuator constraints and improve comfortableness as soon as possible, which strictly obeys traffic rules other than making traffic mess, it is rather than purely pursuing the shortest route/time. By analysis traffic rules, the DBO framework is employed to produce trajectories. To make trajectory drivable, cubic B-spline and clothoid curve are modeled to keep continuous curvature, and cubic polynomial curve is to schedule velocity profile satisfying stability and comfort. To pick out the best trajectory, HAHP decision maker is developed to evaluate the candidates. The first layer selects optimal paths considering smoothness and economy, and the second layer selects best trajectory taking smoothness, comfortableness and economy in account. Moreover, DBO rapidly exploring random tree (RRT) replanner is embedded to ensure algorithm completeness. Finally, several typical scenarios are designed to verify the real-time and reliability of the algorithm. The results illustrate that the algorithm has highly real-time and stability evaluated by Statistical Process Control method as the probability for the peak time less than 0.1s is 100% except three obstacles avoidance scenario is 59.31% in 1000 cycles. Since the planned trajectory is smooth enough and satisfy the constraints of the actuator, the mean lateral tracking error is less than 0.2m with 0.5m peak error, and the mean speed error less than 0.5km/h with 1.5km/h peak error for all scenarios. |
| first_indexed | 2024-12-20T05:24:16Z |
| format | Article |
| id | doaj.art-c20f1afcf73d4b76a83ee4130e457492 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:24:16Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c20f1afcf73d4b76a83ee4130e4574922022-12-21T19:51:55ZengIEEEIEEE Access2169-35362019-01-01716536516538610.1109/ACCESS.2019.29535108901215<inline-formula> <tex-math notation="LaTeX">$DBO$ </tex-math></inline-formula> Trajectory Planning and <inline-formula> <tex-math notation="LaTeX">$HAHP$ </tex-math></inline-formula> Decision-Making for Autonomous Vehicle Driving on Urban EnvironmentDequan Zeng0https://orcid.org/0000-0001-6456-9915Zhuoping Yu1https://orcid.org/0000-0002-8775-0052Lu Xiong2https://orcid.org/0000-0002-1673-2658Zhiqiang Fu3https://orcid.org/0000-0002-8104-689XPeizhi Zhang4https://orcid.org/0000-0003-0068-226XHongtu Zhou5https://orcid.org/0000-0002-0609-4226School of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaCollege of Electronics and Information Engineering, Tongji University, Shanghai, ChinaA novel driving behaviour oriented (DBO) trajectory planner and hierarchical analytic hierarchy process (HAHP) decision maker are presented for intelligent vehicle. Since driving on structural road should satisfy actuator constraints and improve comfortableness as soon as possible, which strictly obeys traffic rules other than making traffic mess, it is rather than purely pursuing the shortest route/time. By analysis traffic rules, the DBO framework is employed to produce trajectories. To make trajectory drivable, cubic B-spline and clothoid curve are modeled to keep continuous curvature, and cubic polynomial curve is to schedule velocity profile satisfying stability and comfort. To pick out the best trajectory, HAHP decision maker is developed to evaluate the candidates. The first layer selects optimal paths considering smoothness and economy, and the second layer selects best trajectory taking smoothness, comfortableness and economy in account. Moreover, DBO rapidly exploring random tree (RRT) replanner is embedded to ensure algorithm completeness. Finally, several typical scenarios are designed to verify the real-time and reliability of the algorithm. The results illustrate that the algorithm has highly real-time and stability evaluated by Statistical Process Control method as the probability for the peak time less than 0.1s is 100% except three obstacles avoidance scenario is 59.31% in 1000 cycles. Since the planned trajectory is smooth enough and satisfy the constraints of the actuator, the mean lateral tracking error is less than 0.2m with 0.5m peak error, and the mean speed error less than 0.5km/h with 1.5km/h peak error for all scenarios.https://ieeexplore.ieee.org/document/8901215/Autonomous vehicletrajectory plannerdecision makerurban environmentdriving behavior orienthierarchical analytic hierarchy process |
| spellingShingle | Dequan Zeng Zhuoping Yu Lu Xiong Zhiqiang Fu Peizhi Zhang Hongtu Zhou <inline-formula> <tex-math notation="LaTeX">$DBO$ </tex-math></inline-formula> Trajectory Planning and <inline-formula> <tex-math notation="LaTeX">$HAHP$ </tex-math></inline-formula> Decision-Making for Autonomous Vehicle Driving on Urban Environment IEEE Access Autonomous vehicle trajectory planner decision maker urban environment driving behavior orient hierarchical analytic hierarchy process |
| title | <inline-formula> <tex-math notation="LaTeX">$DBO$ </tex-math></inline-formula> Trajectory Planning and <inline-formula> <tex-math notation="LaTeX">$HAHP$ </tex-math></inline-formula> Decision-Making for Autonomous Vehicle Driving on Urban Environment |
| title_full | <inline-formula> <tex-math notation="LaTeX">$DBO$ </tex-math></inline-formula> Trajectory Planning and <inline-formula> <tex-math notation="LaTeX">$HAHP$ </tex-math></inline-formula> Decision-Making for Autonomous Vehicle Driving on Urban Environment |
| title_fullStr | <inline-formula> <tex-math notation="LaTeX">$DBO$ </tex-math></inline-formula> Trajectory Planning and <inline-formula> <tex-math notation="LaTeX">$HAHP$ </tex-math></inline-formula> Decision-Making for Autonomous Vehicle Driving on Urban Environment |
| title_full_unstemmed | <inline-formula> <tex-math notation="LaTeX">$DBO$ </tex-math></inline-formula> Trajectory Planning and <inline-formula> <tex-math notation="LaTeX">$HAHP$ </tex-math></inline-formula> Decision-Making for Autonomous Vehicle Driving on Urban Environment |
| title_short | <inline-formula> <tex-math notation="LaTeX">$DBO$ </tex-math></inline-formula> Trajectory Planning and <inline-formula> <tex-math notation="LaTeX">$HAHP$ </tex-math></inline-formula> Decision-Making for Autonomous Vehicle Driving on Urban Environment |
| title_sort | inline formula tex math notation latex dbo tex math inline formula trajectory planning and inline formula tex math notation latex hahp tex math inline formula decision making for autonomous vehicle driving on urban environment |
| topic | Autonomous vehicle trajectory planner decision maker urban environment driving behavior orient hierarchical analytic hierarchy process |
| url | https://ieeexplore.ieee.org/document/8901215/ |
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