Probabilistically Guaranteeing End-to-End Latencies in Autonomous Vehicle Computing Systems
- Authors
- Lee, Hyoeun; Choi, Youngjoon; Han, Taeho; Kim, Kanghee
- Issue Date
- Dec-2022
- Publisher
- IEEE COMPUTER SOC
- Keywords
- Autonomous vehicles; autoware; end-to-end latency; probabilistic guarantee; response time analysis
- Citation
- IEEE TRANSACTIONS ON COMPUTERS, v.71, no.12, pp.3361 - 3374
- Journal Title
- IEEE TRANSACTIONS ON COMPUTERS
- Volume
- 71
- Number
- 12
- Start Page
- 3361
- End Page
- 3374
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/43246
- DOI
- 10.1109/TC.2022.3152105
- ISSN
- 0018-9340
- Abstract
- Good responsiveness of autonomous vehicle computing systems is crucial to safety and performance of the vehicles. For example, an autonomous vehicle (AV) may cause an accident if the end-to-end latency from sensing a pedestrian to emergency stop is too high. However, the AV software stacks are too complex to probabilistically analye the end-to-end latency on a multi-core system. They consist of a graph of tasks with different periods, and have a large variability in the task execution times, which may lead to the maximum core utilization $U<^>{\max }$Umax greater than 1.0 on some cores. This paper proposes a novel stochastic analysis of the end-to-end latency over the AV stacks that allows $U<^>{\max }$Umax to exceed 1.0 on each core. The proposed analysis models the entire stack as a graph of task graphs under a multi-core partitioned scheduling and provides a probabilistic guarantee that the analyzed latency distribution upper-bounds the one observed from a real system under the assumption of independent task execution times. Using the Autoware stack with inter-task dependent execution times, it is shown that our analysis, combined with a task grouping to mitigate the inter-task correlations, can give a latency distribution for each task path that almost upper-bounds the observed one.
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