Calculating Emergent Properties of Particle Interactions using Convolutional Neural Networks

Abstract

With the rise of autonomous vehicles (AV), there is reason to believe that they will be a promising solution to the problem of urban traffic congestion that is rapidly growing with escalating economic and environmental cost. However, integrating AVs into a current traffic system is challenging, especially in complex traffic intersections or roundabouts. In this article we study cutting edge control strategy to optimize technique for AV of traffic flow, safety, and efficiency in mixed traffic condition. The vehicle automation and communication system have been rapidly evolving to modernize traffic management. With such a tipping point of transportation around the corner, this calls on us to create robust control strategies that seamlessly marry AVs with human driven vehicles, while at the same time mitigating any challenges that arise from this anticipated revolution. In this study, we cover a lot of things about av control optimization, starting from the theory and ending at the applications. We will explore new intersection management approaches, cooperative merging strategies as well as adaptive control systems to increase overall traffic performance. Through the synthesis of the current state and future potential of AVs in complex traffic environment based on what has been recently researched and implemented, this article attempts to characterise a holistic view of the current state and the future possibilities of optimal control strategies for AVs.