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Genie: Smart ROS-based Caching for Connected Autonomous Robots

Zexin Li, Soroush Bateni, Cong Liu

TL;DR

Genie tackles latency in edge-enabled autonomous driving under SWaP constraints by introducing a non-intrusive ROS-based caching mechanism with distributed caches and a driving-focused 3D object map. The approach enables transparent caching without modifying Autoware, supports cross-vehicle data sharing, and builds a local/global object map to boost decision accuracy. Experimental results on Jetson TX2 and AGX Xavier show substantial tail-latency reductions (average ~82%) and improved object reusability (up to ~67%), along with higher data confidence through multi-vehicle observations. This demonstrates practical benefits of locality-aware edge caching for real-time autonomous driving and points to future work extending to ROS2 and broader robotics domains.

Abstract

Despite the promising future of autonomous robots, several key issues currently remain that can lead to compromised performance and safety. One such issue is latency, where we find that even the latest embedded platforms from NVIDIA fail to execute intelligence tasks (e.g., object detection) of autonomous vehicles in a real-time fashion. One remedy to this problem is the promising paradigm of edge computing. Through collaboration with our industry partner, we identify key prohibitive limitations of the current edge mindset: (1) servers are not distributed enough and thus, are not close enough to vehicles, (2) current proposed edge solutions do not provide substantially better performance and extra information specific to autonomous vehicles to warrant their cost to the user, and (3) the state-of-the-art solutions are not compatible with popular frameworks used in autonomous systems, particularly the Robot Operating System (ROS). To remedy these issues, we provide Genie, an encapsulation technique that can enable transparent caching in ROS in a non-intrusive way (i.e., without modifying the source code), can build the cache in a distributed manner (in contrast to traditional central caching methods), and can construct a collective three-dimensional object map to provide substantially better latency (even on low-power edge servers) and higher quality data to all vehicles in a certain locality. We fully implement our design on state-of-the-art industry-adopted embedded and edge platforms, using the prominent autonomous driving software Autoware, and find that Genie can enhance the latency of Autoware Vision Detector by 82% on average, enable object reusability 31% of the time on average and as much as 67% for the incoming requests, and boost the confidence in its object map considerably over time.

Genie: Smart ROS-based Caching for Connected Autonomous Robots

TL;DR

Genie tackles latency in edge-enabled autonomous driving under SWaP constraints by introducing a non-intrusive ROS-based caching mechanism with distributed caches and a driving-focused 3D object map. The approach enables transparent caching without modifying Autoware, supports cross-vehicle data sharing, and builds a local/global object map to boost decision accuracy. Experimental results on Jetson TX2 and AGX Xavier show substantial tail-latency reductions (average ~82%) and improved object reusability (up to ~67%), along with higher data confidence through multi-vehicle observations. This demonstrates practical benefits of locality-aware edge caching for real-time autonomous driving and points to future work extending to ROS2 and broader robotics domains.

Abstract

Despite the promising future of autonomous robots, several key issues currently remain that can lead to compromised performance and safety. One such issue is latency, where we find that even the latest embedded platforms from NVIDIA fail to execute intelligence tasks (e.g., object detection) of autonomous vehicles in a real-time fashion. One remedy to this problem is the promising paradigm of edge computing. Through collaboration with our industry partner, we identify key prohibitive limitations of the current edge mindset: (1) servers are not distributed enough and thus, are not close enough to vehicles, (2) current proposed edge solutions do not provide substantially better performance and extra information specific to autonomous vehicles to warrant their cost to the user, and (3) the state-of-the-art solutions are not compatible with popular frameworks used in autonomous systems, particularly the Robot Operating System (ROS). To remedy these issues, we provide Genie, an encapsulation technique that can enable transparent caching in ROS in a non-intrusive way (i.e., without modifying the source code), can build the cache in a distributed manner (in contrast to traditional central caching methods), and can construct a collective three-dimensional object map to provide substantially better latency (even on low-power edge servers) and higher quality data to all vehicles in a certain locality. We fully implement our design on state-of-the-art industry-adopted embedded and edge platforms, using the prominent autonomous driving software Autoware, and find that Genie can enhance the latency of Autoware Vision Detector by 82% on average, enable object reusability 31% of the time on average and as much as 67% for the incoming requests, and boost the confidence in its object map considerably over time.
Paper Structure (16 sections, 8 figures, 1 table, 2 algorithms)

This paper contains 16 sections, 8 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Design
  4. Design Considerations
  5. Genie ROS Node for Non-Intrusive Caching
  6. Distributed Collective Cache
  7. Driving-specific Caching
  8. Evaluation
  9. Experimental Setup
  10. Tail-Latency Performance
  11. Image Caching
  12. Object Caching
  13. Confidence Boost
  14. Case Study: Vision-Assisted Driving
  15. Related Work
  16. ...and 1 more sections

Figures (8)

  • Figure 1: Left: an autonomous car connected to the edge. Right: an example of applying Genie in an autonomous vehicle.
  • Figure 2: Overview of the Genie.
  • Figure 3: An example of applying Genie to object detection across autonomous vehicles.
  • Figure 4: Cumulative distribution function for the response time of Genie(DG), versus remote (R) and local (L) ROS execution on homogeneous and heterogeneous configurations.
  • Figure 5: Image reusability ratio for local Genies and remote Genies under the three scenarios.
  • ...and 3 more figures