Continuous integration and testing for autonomous racing in simulation

Self-driving autonomous vehicles (AVs) have recently gained in popularity as a research topic. The safety of AVs is exceptionally important as failure in the design of an AV could lead to catastrophic consequences. AV systems are highly heterogeneous with many different and complex components, so it is difficult to perform end-to-end testing. One solution to this dilemma is to evaluate AVs using simulated racing competition. In this thesis, we present a simulated autonomous racing competition, Generalized RAcing Intelligence Competition (GRAIC). To compete in GRAIC, participants need to submit their controller files which are deployed on a racing ego-vehicle on different race tracks. To evaluate the submitted controller, we also developed a testing pipeline, Autonomous System Operations (AsOps). AsOps is an automated, scalable, and fair testing pipeline developed using software engineering techniques such as continuous integration, containerization, and serverless computing. In order to evaluate the submitted controller in non-trivial circumstances, we populate the race tracks with scenarios, which are pre-defined traffic situations commonly seen in the real road. We present a dynamic scenario testing strategy that generates new scenarios based on the results of the ego-vehicle passing through previous scenarios.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Jiang, Minghao

Contributors dc:contributor

• Mitra, Sayan

• Self-driving autonomous vehicle
• continuous integration and testing
• simulated autonomous racing competition

Statement dc:rights

• Copyright 2021 Minghao Jiang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/110608

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/110608