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Feasibility Study Of Fully Autonomous Vehicles Using Decision-theoretic Control

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  • Forbes, Jeffrey
  • Et. al.,

Abstract

This project studied the feasibility of constructing an autonomous vehicle controller based on probabilistic inference and utility maximization. Several theoretical and algorithmic advances were required in order to create an inference system capable of handling vehicle monitoring in a real-time fashion. New methods were also developed for learning probabilistic models from data, and for learning control policies given reward/penalty feedback.

Suggested Citation

  • Forbes, Jeffrey & Et. al.,, 1997. "Feasibility Study Of Fully Autonomous Vehicles Using Decision-theoretic Control," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4nz42165, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt4nz42165
    as

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    File URL: https://www.escholarship.org/uc/item/4nz42165.pdf;origin=repeccitec
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    References listed on IDEAS

    as
    1. Tomizuka, Masayoshi & Hedrick, J. Karl & Pham, Hung, 1995. "Integrated Maneuvering Control For Automated Highway Systems Based On A Magnetic Reference/sensing System," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7bh3f4gf, Institute of Transportation Studies, UC Berkeley.
    2. Hedrick, J. K. & McMahnon, D. H. & Swaroop, D., 1993. "Vehicle Modeling And Control For Automated Highway Systems," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt81q709jn, Institute of Transportation Studies, UC Berkeley.
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