University of Illinois Urbana-Champaign

Continuous integration and testing for autonomous racing in simulation

Jiang, Minghao

Content Files
JIANG-THESIS-2021.pdf

Permalink

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

Description

Title
Continuous integration and testing for autonomous racing in simulation
Author(s)
Jiang, Minghao
Issue Date
2021-04-30
Director of Research (if dissertation) or Advisor (if thesis)
Mitra, Sayan
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2021-09-17T01:13:35Z
Keyword(s)
  • Self-driving autonomous vehicle
  • continuous integration and testing
  • simulated autonomous racing competition
Abstract
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.
Graduation Semester
2021-05
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/110608
Copyright and License Information
Copyright 2021 Minghao Jiang

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