A Bio-Inspired Cognitive Agent for Autonomous Urban Vehicles Routing Optimization

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 self-sufficiency, 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.