MARC details
| 000 -LEADER |
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| fixed length control field |
03598ntm a2200385 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125110727.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230410t20222022my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0009591 (Local) |
| Qualifying information |
Hardback |
| 039 ## - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
|---|
| Level of rules in bibliographic description |
UMP |
| 040 ## - CATALOGING SOURCE |
|---|
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .J34 2022 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Abu Jafar Md Muzahid, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Reinforcement learning based decision-making model in autonomous vehicle control for cooperation and mitigation of collision among multiple vehicles / |
| Statement of responsibility, etc. |
Abu Jafar Md Muzahid |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
©2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 121 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
computer |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
computer disc |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2022 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Self-driving cars have become a popular research topic in recent years. Autonomous driving is a complicated field of study that involves a variety of disciplines, such as electronics, computer vision, geo-location, decision-making, or control. Autonomous vehicles are an example of non-linear technologies being used in the real world. Controlling this kind of device in particular situations in the context of multi-agent traffic systems is difficult because of instability. This type of equipment demands expertise, and it is even more difficult to create this understanding of talent as an independent control system. Because each agent has its own self-determined protocol decision management, it is hard to coordinate several autonomous devices on a single job. Over the last decade, there has been a lot of attention on sequential decision-making under ambiguity and uncertainty, which is a distinct range of challenges requiring an agent to interact with an uncertain environment to achieve a target. Reinforcement learning methods applied to these challenges have resulted in recent AI achievements in robotics, game playing, and other areas. In response to these empirical testimonies, this project confronts the problem of multiple vehicle control decisions and performs control strategies for the avoidance of severe multiple vehicle collisions in autonomous vehicles. These control techniques rely on the reinforcement learning model and deploy two distinct traffic scenarios for progressing research flow. An extensive taxonomy conveyed the existing protocols and solutions, and a conceptual model for MVCCA was formulated first. Then, using the Reinforcement Learning-based Decision- Making (RLDM) model, the system is developed and implemented. An extensive simulation gives us the best outcomes for the development of optimum driving strategies in a multi-agent traffic environment. We extensively evaluate the training performance, driving performance, and the ability of collision avoidance as well. We investigated the training performance of both the single vehicle and multiple vehicle environments. Validation of the decision-making scheme would create new opportunities for autonomous driving, as well as new concepts and applications. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |