Timing of autonomous driving software: problem analysis and prospects for future solutions
Document typeConference report
Rights accessOpen Access
European Commission's projectSuPerCom - Sustainable Performance for High-Performance Embedded Computing Systems (EC-H2020-772773)
The software used to implement advanced functionalities in critical domains (e.g. autonomous operation) impairs software timing. This is not only due to the complexity of the underlying high-performance hardware deployed to provide the required levels of computing performance, but also due to the complexity, non-deterministic nature, and huge input space of the artificial intelligence (AI) algorithms used. In this paper, we focus on Apollo, an industrial-quality Autonomous Driving (AD) software framework: we statistically characterize its observed execution time variability and reason on the sources behind it. We discuss the main challenges and limitations in finding a satisfactory software timing analysis solution for Apollo and also show the main traits for the acceptability of statistical timing analysis techniques as a feasible path. While providing a consolidated solution for the software timing analysis of Apollo is a huge effort far beyond the scope of a single research paper, our work aims to set the basis for future and more elaborated techniques for the timing analysis of AD software.
CitationAlcon, M. [et al.]. Timing of autonomous driving software: problem analysis and prospects for future solutions. A: Real-Time and Embedded Technology and Applications, IEEE Symposium. "2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS): proceedings: Sydney, Australia: April, 21st-24th, 2020". IEEE, 2020, p. 267-280.
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