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

Advancing Deductive Program-Level Verification for Real-World Application: Lessons Learned from an Industrial Case Study

This thesis is concerned with practicability of deductive program verification on source code level. As part of a case study for the verification of real-world software, the specification and verification approach to show correctness of the virtualizing kernel PikeOS is presented. Issues within the verification process using current tools and methodologies are discussed and several aspects of these problems are then addressed in detail to improve the verification process and tool usability