Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving
The autonomous driving is increasingly mounting, promoting, and promising the future of fully autonomous and, correspondingly presenting new challenges in the field of safety assurance. The unexpected and sudden lane change are extremely serious causes of traffic accident and, such an accident schem...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31549/ http://umpir.ump.edu.my/id/eprint/31549/8/Learning-Based%20Conceptual%20framework%20for%20Threat%20ieee.pdf |
| _version_ | 1848823796545355776 |
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| author | Muzahid, Abu Jafar Md Syafiq Fauzi, Kamarulzaman Rahim, Md Abdur |
| author_facet | Muzahid, Abu Jafar Md Syafiq Fauzi, Kamarulzaman Rahim, Md Abdur |
| author_sort | Muzahid, Abu Jafar Md |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The autonomous driving is increasingly mounting, promoting, and promising the future of fully autonomous and, correspondingly presenting new challenges in the field of safety assurance. The unexpected and sudden lane change are extremely serious causes of traffic accident and, such an accident scheme leads the multiple vehicle collisions.Extensive evaluation of recent crash data we found a crucial indication that autonomous driving systems are most prone to rear-end collision, which is the leading factor of chain crash. Learning based self-developing assessment assists the operators in providing the necessary prediction operations or even replace them. Here we proposed a Reinforcement learning-based conceptual framework for threat assessment system and scrutinize critical situations that leads to multiple vehicle collisions in autonomous driving. This paper will encourage our transport community to rethink the existing autonomous driving models and reach out to other disciplines, particularly robotics and machine learning, to join forces to create a secure and effective system. |
| first_indexed | 2025-11-15T03:02:50Z |
| format | Conference or Workshop Item |
| id | ump-31549 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:02:50Z |
| publishDate | 2020 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-315492021-08-11T08:33:54Z http://umpir.ump.edu.my/id/eprint/31549/ Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving Muzahid, Abu Jafar Md Syafiq Fauzi, Kamarulzaman Rahim, Md Abdur QA75 Electronic computers. Computer science QA76 Computer software TJ Mechanical engineering and machinery The autonomous driving is increasingly mounting, promoting, and promising the future of fully autonomous and, correspondingly presenting new challenges in the field of safety assurance. The unexpected and sudden lane change are extremely serious causes of traffic accident and, such an accident scheme leads the multiple vehicle collisions.Extensive evaluation of recent crash data we found a crucial indication that autonomous driving systems are most prone to rear-end collision, which is the leading factor of chain crash. Learning based self-developing assessment assists the operators in providing the necessary prediction operations or even replace them. Here we proposed a Reinforcement learning-based conceptual framework for threat assessment system and scrutinize critical situations that leads to multiple vehicle collisions in autonomous driving. This paper will encourage our transport community to rethink the existing autonomous driving models and reach out to other disciplines, particularly robotics and machine learning, to join forces to create a secure and effective system. IEEE 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31549/8/Learning-Based%20Conceptual%20framework%20for%20Threat%20ieee.pdf Muzahid, Abu Jafar Md and Syafiq Fauzi, Kamarulzaman and Rahim, Md Abdur (2020) Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving. In: IEEE Emerging Technology in Computing, Communication and Electronics (ETCCE 2020) , 21-22 December 2020 , United International University (UIU)-Virtual, Dhaka, Bangladesh. pp. 1-7., 20. ISBN 78-1-6654-1962-8 (Published) https://doi.org/10.1109/ETCCE51779.2020.9350869 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software TJ Mechanical engineering and machinery Muzahid, Abu Jafar Md Syafiq Fauzi, Kamarulzaman Rahim, Md Abdur Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving |
| title | Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving |
| title_full | Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving |
| title_fullStr | Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving |
| title_full_unstemmed | Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving |
| title_short | Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving |
| title_sort | learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving |
| topic | QA75 Electronic computers. Computer science QA76 Computer software TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/31549/ http://umpir.ump.edu.my/id/eprint/31549/ http://umpir.ump.edu.my/id/eprint/31549/8/Learning-Based%20Conceptual%20framework%20for%20Threat%20ieee.pdf |