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An Analysis of Software Latency for a High-Speed Autonomous Race Car—A Case Study in the Indy Autonomous Challenge
- Tobias Betz - Technical University of Munich, Institute of Automotive Technology, TUM School of Engineering and Design, Germany ,
- Phillip Karle - Technical University of Munich, Institute of Automotive Technology, TUM School of Engineering and Design, Germany ,
- Frederik Werner - Technical University of Munich, Institute of Automotive Technology, TUM School of Engineering and Design, Germany ,
- Johannes Betz - Technical University of Munich, Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Germany
Journal Article
12-06-03-0018
ISSN: 2574-0741, e-ISSN: 2574-075X
Sector:
Topic:
Citation:
Betz, T., Karle, P., Werner, F., and Betz, J., "An Analysis of Software Latency for a High-Speed Autonomous Race Car—A Case Study in the Indy Autonomous Challenge," SAE Intl. J CAV 6(3):283-296, 2023, https://doi.org/10.4271/12-06-03-0018.
Language:
English
Abstract:
Autonomous driving faces the difficulty of securing the lowest possible software
execution times to allow a safe and reliable application. One critical variable
for autonomous vehicles is the latency from the detection of obstacles to the
final actuation response of the vehicle, especially in the case of high-speed
driving. A prerequisite for autonomy software is that it enables low execution
times to achieve superhuman reaction times. This article presents an in-depth
analysis of a full self-driving software stack for autonomous racing. A modular
software stack especially developed for high-speed autonomous driving is used
and the latency of the software is analyzed in four main autonomy modules:
perception, prediction, planning, and control. With the help of a trace point
measurement method, it is possible to investigate the end-to-end latency and
runtimes of the individual modules. This analysis is conducted for different
scenarios (high-speed runs, object avoidance, and multi-vehicle racing) and is
based on the two sensor pipelines. With this evaluation, insights are provided
that highlight the importance of software execution times for several parts of
the software architecture. Real data from an actual vehicle and simulation data
show that both complex and linear relationships exist, from which optimization
potential for future software stacks can be derived.