Research on risk detection of autonomous vehicle based on rapidly-exploring random tree
© 2023 by the authors.There is no doubt that the autonomous vehicle is an important developing direction of the auto industry, and, thus, more and more scholars are paying attention to doing more research in this field. Since path planning plays a key role in the operation of autonomous vehicles, sc...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
MDPI
2023
|
| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/36054/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/36054/2/FULL%20TEXT.pdf |
| _version_ | 1825715263617630208 |
|---|---|
| author | Yincong Ma Lim, Kit Guan Tan, Min Keng Sin, Helen Ee Chuo Ali Farzamnia Tze, Kenneth Kin Teo |
| author_facet | Yincong Ma Lim, Kit Guan Tan, Min Keng Sin, Helen Ee Chuo Ali Farzamnia Tze, Kenneth Kin Teo |
| author_sort | Yincong Ma |
| collection | UMS |
| description | © 2023 by the authors.There is no doubt that the autonomous vehicle is an important developing direction of the auto industry, and, thus, more and more scholars are paying attention to doing more research in this field. Since path planning plays a key role in the operation of autonomous vehicles, scholars attach great importance to this field. Although it has been applied in many fields, there are still some problems, such as low efficiency of path planning and collision risk during driving. In order to solve these problems, an automotive vehicle-based rapid exploration random tree (AV-RRT)-based non-particle path planning method for autonomous vehicles is proposed. On the premise of ensuring safety and meeting the requirements of the vehicle’s kinematic constraints through the expansion of obstacles, the dynamic step size is used for random tree growth. A non-particle collision detection (NPCD) collision detection algorithm and path modification (PM) path modification strategy are proposed for the collision risk in the turning process, and geometric constraints are used to represent the possible security threats, so as to improve the efficiency and safety of vehicle global path driving and to provide reference for the research of driverless vehicles. |
| first_indexed | 2024-03-06T03:24:15Z |
| format | Article |
| id | ums.eprints-36054 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:24:15Z |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | dspace |
| spelling | ums.eprints-360542023-07-21T06:42:24Z https://eprints.ums.edu.my/id/eprint/36054/ Research on risk detection of autonomous vehicle based on rapidly-exploring random tree Yincong Ma Lim, Kit Guan Tan, Min Keng Sin, Helen Ee Chuo Ali Farzamnia Tze, Kenneth Kin Teo TL1-484 Motor vehicles. Cycles © 2023 by the authors.There is no doubt that the autonomous vehicle is an important developing direction of the auto industry, and, thus, more and more scholars are paying attention to doing more research in this field. Since path planning plays a key role in the operation of autonomous vehicles, scholars attach great importance to this field. Although it has been applied in many fields, there are still some problems, such as low efficiency of path planning and collision risk during driving. In order to solve these problems, an automotive vehicle-based rapid exploration random tree (AV-RRT)-based non-particle path planning method for autonomous vehicles is proposed. On the premise of ensuring safety and meeting the requirements of the vehicle’s kinematic constraints through the expansion of obstacles, the dynamic step size is used for random tree growth. A non-particle collision detection (NPCD) collision detection algorithm and path modification (PM) path modification strategy are proposed for the collision risk in the turning process, and geometric constraints are used to represent the possible security threats, so as to improve the efficiency and safety of vehicle global path driving and to provide reference for the research of driverless vehicles. MDPI 2023-03 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/36054/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/36054/2/FULL%20TEXT.pdf Yincong Ma and Lim, Kit Guan and Tan, Min Keng and Sin, Helen Ee Chuo and Ali Farzamnia and Tze, Kenneth Kin Teo (2023) Research on risk detection of autonomous vehicle based on rapidly-exploring random tree. Computation, 11 (61). pp. 1-26. https://doi.org/10.3390/computation11030061 |
| spellingShingle | TL1-484 Motor vehicles. Cycles Yincong Ma Lim, Kit Guan Tan, Min Keng Sin, Helen Ee Chuo Ali Farzamnia Tze, Kenneth Kin Teo Research on risk detection of autonomous vehicle based on rapidly-exploring random tree |
| title | Research on risk detection of autonomous vehicle based on rapidly-exploring random tree |
| title_full | Research on risk detection of autonomous vehicle based on rapidly-exploring random tree |
| title_fullStr | Research on risk detection of autonomous vehicle based on rapidly-exploring random tree |
| title_full_unstemmed | Research on risk detection of autonomous vehicle based on rapidly-exploring random tree |
| title_short | Research on risk detection of autonomous vehicle based on rapidly-exploring random tree |
| title_sort | research on risk detection of autonomous vehicle based on rapidly exploring random tree |
| topic | TL1-484 Motor vehicles. Cycles |
| url | https://eprints.ums.edu.my/id/eprint/36054/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/36054/2/FULL%20TEXT.pdf |
| work_keys_str_mv | AT yincongma researchonriskdetectionofautonomousvehiclebasedonrapidlyexploringrandomtree AT limkitguan researchonriskdetectionofautonomousvehiclebasedonrapidlyexploringrandomtree AT tanminkeng researchonriskdetectionofautonomousvehiclebasedonrapidlyexploringrandomtree AT sinheleneechuo researchonriskdetectionofautonomousvehiclebasedonrapidlyexploringrandomtree AT alifarzamnia researchonriskdetectionofautonomousvehiclebasedonrapidlyexploringrandomtree AT tzekennethkinteo researchonriskdetectionofautonomousvehiclebasedonrapidlyexploringrandomtree |