Traffic congestion-aware graph-based vehicle rerouting framework from aerial imagery

Abstract

In digital era, being stranded from very basic telecommunication protocols and internet makes vehicle rerouting-like crucial tools more difficult or even impossible, especially in times of disaster and emergency. In this study, we propose a modular rerouting framework for only one single vehicle composed of visual perception, property estimation and trajectory optimization, which enables to generate optimum paths exploiting aerial imagery. Once the deep network, which is fine-tuned on newly introduced dataset herein named MaVefAI, processes the input, the following module estimates pose, motion direction and speed of detected vehicles. Afterwards, we link the appropriate vehicles via graphs to obtain group properties that pave the way for estimating the traffic congestion level. In the end, we get the output as the optimum path from the independent trajectory optimization module to which required inputs are already sent by preceding modules. We solve the multi-objective cost function subject to velocity and congestion intervals, which comprises distance, traffic congestion level, and angle inconsistency. We employ Dijkstra, A*, RRT, and RRT* to optimize the cost while vast majority of existing works focus to optimize via single method. The fine-tuned segmentation model accuracy becomes more than 98% for vehicle groups thanks to MaVefAI. The extensive experiments reveal that all algorithms follow the same path. However, RRT* achieves the fastest result by examining most of the possible options in less time, which also appears to be the most robust method comparing with the alternatives for route optimization. Our dataset MaVefAI is publicly available here: https://precisioncomputing.sakarya.edu.tr.