Comprehensive Autonomous Vehicle Optimal Routing With Dynamic Heuristics

TL;DR

This paper proposes a hybrid autonomous vehicle network model with dynamic heuristics to optimize routing, improve traffic efficiency, and enhance passenger safety and comfort in real-time environments.

Contribution

It introduces a novel optimal routing framework using a custom informed search and heuristics for autonomous vehicle networks, addressing real-time traffic adaptation.

Findings

Enhanced routing efficiency demonstrated through simulations

Improved passenger safety and comfort metrics

Effective real-time traffic condition adaptation

Abstract

Auto manufacturers and research groups are working on autonomous driving for long period and achieved significant progress. Autonomous vehicles (AV) are expected to transform road traffic reduction from current conditions, avoiding accidents and congestion. As the implementation of an autonomous vehicle ecosystem includes complex automotive technology, ethics, passenger behaviour, traffic management policies and liability etc., the maturity of AV solutions are still evolving. The proposed model to improve AV user experience, uses a hybrid AV Network of multiple connected autonomous vehicles which communicate with each other in an environment shared by human driven vehicles. The proposed Optimal AV Network (OAVN) solution provides better coordination and optimization of autonomous vehicles, improved Transportation efficiency, improved passenger comfort and safety, real-time dynamic adaption of traffic & road conditions along with improved in-cabin assistance with inputs from various sensors. The true optimal solution for this problem, is to devise an automated guidance system for vehicles in an AV network, to reach destinations in best possible routes along with passenger comfort and safety. A custom informed search model is proposed along with other heuristic goals for better user experience. The results are analysed and compared to evaluate the effectiveness of the solution and identify gaps and future enhancements.