IEEE Access · 2020 · 68 citations · 34 references
ComputingAutonomous SystemsComputer EngineeringAutonomous VehiclesEdge ComputingEmbedded SystemsEdge ArchitectureEdge Computing InfrastructureEdge Resource ManagementEdge-cloud ArchitectureEdge-cloud IntegrationTask OffloadingBattery Life ManagementEdge Computing For Vehicular NetworksEdge Computing OffloadingPower ConsumptionPower Consumption OptimizationRuntime ArchitectureVehicular Edge ComputingVehicular Task Offloading
To simultaneously enable multiple autonomous driving services on affordable embedded systems, we designed and implemented LoPECS, a Low-Power Edge Computing System for real-time autonomous robots and vehicles services. The contributions of this paper are three-fold: first, we developed a Heterogeneity-Aware Runtime Layer to fully utilize vehicle's heterogeneous computing resources to fulfill the real-time requirement of autonomous driving applications; second, we developed a vehicle-edge Coordinator to dynamically offload vehicle tasks to edge cloudlet to further optimize user experience in the way of prolonged battery life; third, we successfully integrated these components into LoPECS system and implemented it on Nvidia Jetson TX1. To the best of our knowledge, this is the first complete edge computing system in a production autonomous vehicle. Our implementation on Nvidia Jetson demonstrated that it could successfully support multiple autonomous driving services with only 11 W of power consumption, and hence proves the effectiveness of the proposed LoPECS system.
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Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun · 2012 · 14.4K citations
Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller et al. · The International Journal of Robotics Research · 2013 · 9.7K citations
Kaldi Speech Recognition Toolkit
Daniel Povey · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2024 · 4.9K citations · Full text
Computing, Linguistics, Acoustics +15