Feasibility Study Of Fully Autonomous Vehicles Using Decision-theoretic Control
Jeffrey Forbes and
Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley
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.
Keywords: Automobiles--Automatic control; Bayesian statistical decision theory; Real-time control (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:cdl:itsrrp:qt4nz42165
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