Leveraging Cloud Computing to Make Autonomous Vehicles Safer

Use the cloud to improve autonomous driving safety.

With Speculative Cloud Execution, operators process inputs both in the cloud and locally. To ensure real-time execution, operators set a deadline for the responses from the cloud. At the deadline, operators maximize accuracy by generating their output using the available responses from the cloud and the results of local execution.

Overview

• Autonomous vehicles (AVs) use highly accurate ML models to process sensor data in real time, but have limited processing power available on-board.
• While the cloud is resource-rich, accessing the cloud relies on unreliable cellular networks.
• With Speculative Cloud Execution, AVs use the cloud to generate more accurate results when possible and seamlessly fall back to local computation when network conditions degrade.
• We find that integrating the cloud can improve AV safety by avoiding collisions on complex, real-world crash scenarios from the NHTSA.

Abstract

The safety of autonomous vehicles (AVs) depends on their ability to perform complex computations on high-volume sensor data in a timely manner. Their ability to run these computations with state-of-the-art models is limited by the processing power and slow update cycles of their onboard hardware. In contrast, cloud computing offers the ability to burst computation to vast amounts of the latest generation of hardware. However, accessing these cloud resources requires traversing wireless networks that are often considered to be too unreliable for real-time AV driving applications.

Our work seeks to harness this unreliable cloud to enhance the accuracy of an AV’s decisions, while ensuring that it can always fall back to its on-board computational capabilities. We identify three mechanisms that can be used by AVs to safely leverage the cloud for accuracy enhancements, and elaborate why current execution systems fail to enable these mechanisms. To address these limitations, we provide a system design based on the speculative execution of an AV’s pipeline in the cloud, and show the efficacy of this approach in simulations of complex real-world scenarios that apply these mechanisms.

Cite

@inproceedings{schafhalter2023leveraging,
  title={Leveraging cloud computing to make autonomous vehicles safer},
  author={Schafhalter, Peter and Kalra, Sukrit and Xu, Le and Gonzalez, Joseph E and Stoica, Ion},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={5559--5566},
  year={2023},
  organization={IEEE}
}