HPC-Enabled Deep-Learning and Simulation for AV Development

Robert Patton, Peter Graf

Research output: NRELManagement


DOE’s most powerful and innovative High Performance Computing ( HPC) resources will be applied to the challenge of developing efficient and safe perception and control algorithms for autonomous vehicle operation in a greatly expedited process. The core approach will be first to conduct deep-learning analysis of vehicle sensor data sets with new methods such as ORNL’s MENNDL, using high performance computing, to accelerate the discovery of efficient analysis and control algorithms. A second phase will develop a virtual environment to test the algorithms for millions of scenarios and miles in faster than real time simulations. This virtual environment will be developed with support of human- and hardware-in-the-loop systems. Additional benefits will be derived from adapting the analytics methods to prognostics of truck and car electronic systems (whether autonomous or not) for vehicle “self-awareness” regarding fuel efficiency and cyber security.
Original languageAmerican English
Number of pages6
StatePublished - 2020

Bibliographical note

See the Vehicle Technologies Office Energy Efficient Mobility Systems 2019 Annual Progress Report at https://www.energy.gov/sites/prod/files/2020/06/f76/VTO_2019_APR_EEMS_COMPILED_REPORT_FINAL_compliant_.pdf

NREL Publication Number

  • NREL/MP-2C00-78656


  • automated vehicles
  • high performance computing (HPC)


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