A Bio-Inspired Cognitive Agent for Autonomous Urban Vehicles Routing Optimization
Authored by Giuseppe Vitello, Alfonso Alongi, Vincenzo Conti, Salvatore Vitabile
Date Published: 2017
DOI: 10.1109/tcds.2016.2608500
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Abstract
Autonomous urban vehicle prototypes are expected to be efficient even in
not explicitly planned circumstances and dynamic environments. The
development of autonomous vehicles for urban driving needs real-time
information from vehicles and road network to optimize traffic flows. In
traffic agent-based models, each vehicle is an agent, while the road
network is the environment. Cognitive agents are able to reason on the
perceived data, to evaluate the information obtained by reasoning, and
to learn and respond, preserving their selfsufficiency, independency,
self-determination, and self-reliance. In this paper, a bio-inspired
cognitive agent for autonomous urban vehicles routing optimization is
proposed. The use of selected bio-inspired analyzing techniques, which
are commonly employed to investigate the topological and functional
features of a metabolic network, allows the agent to analyze the
structural aspects of a road network, find its extreme pathways and
outline the balanced flow combinations. This approach optimizes traffic
flows over network, minimizes road congestions, and maximizes the number
of autonomous vehicles reaching their destination target. Agent behavior
has been tested using data coming from Palermo urban road network,
Italy, while the adopted bio-inspired analysis techniques have been
compared with the A{*} literature algorithm. Experimental results
demonstrate that the approach permits to find a better global routing
optimization solution. To the best of our knowledge, it is the first
time that metabolic mechanisms involved in a cell survival process have
been used to design a congestion solution.
Tags
Complex networks
models
Organization
Pathways
Autonomous urban vehicles routing optimization
Cellular metabolism
Cognitive agent
Vehicular ad hoc networks (vanets).
Flux balance analysis
Metabolic networks
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