Timing of Autonomous Driving Software: Problem Analysis and Prospects for Future Solutions

• Published in: 2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) pp. 267 - 280
• Main Authors: Alcon, Miguel, Tabani, Hamid, Kosmidis, Leonidas, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2020
• Subjects: Complexity theory; Hardware; Real-time systems; Scalability; Software; Software algorithms; Timing
• ISSN: 2642-7346
• DOI: 10.1109/RTAS48715.2020.000-1

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.