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Enabling Level-4 Autonomous Driving on a Single 1kOff−the−ShelfCard1k Off-the-Shelf Card1kOff−the−ShelfCard

12 October 2021
Hsin-Hsuan Sung
Yuanchao Xu
Jiexiong Guan
Wei Niu
Shaoshan Liu
Bin Ren
Yanzhi Wang
Xipeng Shen
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Abstract

Autonomous driving is of great interest in both research and industry. The high cost has been one of the major roadblocks that slow down the development and adoption of autonomous driving in practice. This paper, for the first-time, shows that it is possible to run level-4 (i.e., fully autonomous driving) software on a single off-the-shelf card (Jetson AGX Xavier) for less than 1k,anorderofmagnitudelessthanthestate−of−the−artsystems,whilemeetingalltherequirementsoflatency.Thesuccesscomesfromtheresolutionofsomeimportantissuessharedbyexistingpracticesthroughaseriesofmeasuresandinnovations.Thestudyoverturnsthecommonperceptionsofthecomputingresourcesrequiredbylevel−4autonomousdriving,pointsoutapromisingpathfortheindustrytolowerthecost,andsuggestsanumberofresearchopportunitiesforrethinkingthearchitecture,softwaredesign,andoptimizationsofautonomousdriving.1k, an order of magnitude less than the state-of-the-art systems, while meeting all the requirements of latency. The success comes from the resolution of some important issues shared by existing practices through a series of measures and innovations. The study overturns the common perceptions of the computing resources required by level-4 autonomous driving, points out a promising path for the industry to lower the cost, and suggests a number of research opportunities for rethinking the architecture, software design, and optimizations of autonomous driving.1k,anorderofmagnitudelessthanthestate−of−the−artsystems,whilemeetingalltherequirementsoflatency.Thesuccesscomesfromtheresolutionofsomeimportantissuessharedbyexistingpracticesthroughaseriesofmeasuresandinnovations.Thestudyoverturnsthecommonperceptionsofthecomputingresourcesrequiredbylevel−4autonomousdriving,pointsoutapromisingpathfortheindustrytolowerthecost,andsuggestsanumberofresearchopportunitiesforrethinkingthearchitecture,softwaredesign,andoptimizationsofautonomousdriving.

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