Evolutionary Automation of Coordinated Autonomous Vehicles

Huang, A and Nitschke, G (2020) Evolutionary Automation of Coordinated Autonomous Vehicles, Proceedings of IEEE Congress on Evolutionary Computation (IEEE CEC 2020).

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Recently, there has been increased research on adaptive control systems for vehicles that operate on autonomous vehicle only roads. Specifically, roads without current infrastructure constraints of traffic lights, stop signals at intersections or vehicle lanes. This study investigates controller automation for vehicles that must navigate and coordinate with each other on such autonomous vehicle only roads. We comparatively evaluate fitness-function (objective) versus behavior-based (novelty search) versus hybridized objective-novelty evolutionary search for synthesizing autonomous vehicle coordinated driving behavior. The goal of such evolved coordinated driving behavior is to maximize effective (safe) and efficient (expedient) autonomous vehicle traffic throughput for given roads. Results indicate that while novelty and hybrid search evolved effective and efficient driving behaviors, these behaviors did not generalize to new roads as well as driving behaviors evolved with objective-based search.

Item Type: Conference paper
Subjects: Computing methodologies > Machine learning
Computing methodologies > Machine learning > Learning paradigms
Date Deposited: 03 Dec 2021 11:14
Last Modified: 11 Dec 2021 10:40
URI: https://pubs.cs.uct.ac.za/id/eprint/1485

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