Density-scaling traffic management for autonomous vehicle environment—predictive learning-based technique

Abstract

In this paper, traffic and vehicle density optimization issues are discussed, and a suitable solution for autonomous driving environments is proposed. The problem of scalable traffic flow management is hindered by the unpredictable vehicle density and its navigation. This problem increases the flow congestion at the intersection during the change of lane in particular. This paper introduces density-scaling traffic management for autonomous vehicle driving scenarios. The proposed technique relies on navigation output and the autonomous vehicles' trajectory decisions for predicting their availability and time duration. The traffic flow is streamlines based on the predictions; the prediction error is mitigated by rescheduling the successive traffic flow. In this process, predictive iterated learning is employed for improving the scalable traffic flows in an intersection. The predictive learning handles navigation assistance output change of trajectory independently and correlates the decisions' match. The process of prediction matching is performed for improving the flow management with the time factor. The traffic management technique is useful in providing latency-less travel and errorless decision and flow complexity and collision probability.