Augmenting anomaly detection for autonomous vehicles with symbolic rules
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/123013 |
| _version_ | 1811094851277553664 |
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| author | Chen, Tianye,M.EngMassachusetts Institute of Technology. |
| author2 | Lalana Kagal. |
| author_facet | Lalana Kagal. Chen, Tianye,M.EngMassachusetts Institute of Technology. |
| author_sort | Chen, Tianye,M.EngMassachusetts Institute of Technology. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T16:06:31Z |
| format | Thesis |
| id | mit-1721.1/123013 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:06:31Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1230132019-11-22T03:03:07Z Augmenting anomaly detection for autonomous vehicles with symbolic rules Chen, Tianye,M.EngMassachusetts Institute of Technology. Lalana Kagal. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 53-54). My research investigates the issues in anomaly detection as applied to autonomous driving created by the incompleteness of training data. I address these issues through the use of a commonsense knowledge base, a predefined set of rules regarding driving behavior, and a means of updating the base set of rules as anomalies are detected. In order to explore this problem I have built a hardware platform that was used to evaluate existing anomaly detection developed within the lab and that will serve as an evaluation platform for future work in this area. The platform is based on the open-source MIT RACECAR project that integrates the most basic aspect of an driving autonomous vehicle - lidar, camera, accelerometer, and computer - onto the frame of an RC car. We created a set of rules regarding traffic light color transitions to test the car's ability to navigate cones (which represent traffic light colors) and detect anomalies in the traffic light transition order. Anomalies regularly occurred in the car's driving environment and its driving rules were updated as a consequence of the logged anomalies. The car was able to successfully navigate the course and the rules (plausible traffic light color transitions) were updated when repeated anomalies were seen. by Tianye Chen. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-11-22T00:02:15Z 2019-11-22T00:02:15Z 2019 2019 Thesis https://hdl.handle.net/1721.1/123013 1127579900 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 54 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Chen, Tianye,M.EngMassachusetts Institute of Technology. Augmenting anomaly detection for autonomous vehicles with symbolic rules |
| title | Augmenting anomaly detection for autonomous vehicles with symbolic rules |
| title_full | Augmenting anomaly detection for autonomous vehicles with symbolic rules |
| title_fullStr | Augmenting anomaly detection for autonomous vehicles with symbolic rules |
| title_full_unstemmed | Augmenting anomaly detection for autonomous vehicles with symbolic rules |
| title_short | Augmenting anomaly detection for autonomous vehicles with symbolic rules |
| title_sort | augmenting anomaly detection for autonomous vehicles with symbolic rules |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/123013 |
| work_keys_str_mv | AT chentianyemengmassachusettsinstituteoftechnology augmentinganomalydetectionforautonomousvehicleswithsymbolicrules |