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Chasing Clouds with Donkeycar: Holistic Exploration of Edge and Cloud Inferencing Trade-Offs in E2E Self-Driving Cars
SessionPoster Reception
DescriptionIn autonomous driving, computational resources are strained by inference models. The viability of offloading inference to the cloud, considering latency between the car and data center, is questioned. We introduce a Cloud-Aided Real-time Inferencing Framework, integrating with Donkeycar and distributing computational load between cloud and edge. Utilizing Raspberry Pi 4 for edge inferencing and NVIDIA Triton Inference Server for the cloud, we demonstrate the framework's advantages, particularly in RNN performance, which achieved 90% autonomy. Our study includes a scaled car navigating obstacles, assessing factors like speed, resources, latency, and autonomy score. The system's performance shows faster inference time, eliminating bottlenecks, and processing 42 frames per second in the cloud, 11 times faster than on the edge. The poster will detail the strengths, limitations, and potential of leveraging cloud resources in real-time edge environments, focusing on autonomy scores and latency trade-offs.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
Research Posters
Scientific Visualization & Data Analytics Showcase
TimeTuesday, 14 November 20235:15pm - 7pm MST
LocationDEF Concourse
TP