Augmenting anomaly detection for autonomous vehicles with symbolic rules

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Chen, Tianye,M.EngMassachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Lalana Kagal.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123013

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123013