Crowdsensing-Based Personalized Dynamic Route Planning for Smart Vehicles

Abstract

Current route planning systems report to the driver routes based on expected travel time and distance. However, these systems do not provide individualized routing options. With the current routing systems lacking the provision of individualized routing choices, a routing framework which provides a personalized route option not solely based on time and distance would be a step up. With the expanding sensing and computing capabilities in both vehicles and smart devices along with the promising low-latency of 5G networks, a real-time personalized route planner is achievable. In this article, a route planning framework that utilizes the in-vehicle and smartphone sensors to build a crowdsensed database on road surface quality and the driver's personalized skillfulness in different driving environments is proposed. Such databases are leveraged to provide drivers with routing options based on their personal preferences. This framework is tested and validated through a case study of a real driving scenario in Kingston, Ontario to show the framework capabilities compared to conventional route planning.