Vehicular Fuel Cost for Trip Prediction Analysis
Publication Date: 2019-Apr-15
The IP.com Prior Art Database
Vehicular Fuel Cost for Trip Prediction Analysis Driving is perpetually driven by data. Vehicle-based data is growing and will become a major consideration within all vehicles on the road at some point in the future. The automotive population comprises fully autonomous, semi-autonomous, and manually driven vehicles. Planning and methods are needed to aid the total population of automobiles as the number of autonomous vehicles in use grows. To assist people with travel decisions (e.g., where, when, etc.) a system is needed that can determine and educate people about the cost of trips. For example, many people are very interested in understanding the operational/driving costs of much gas-powered vehicles. Knowing this information allows the user to make more informed decisions about future commutes. In short, an easy method is needed to answer the question, “How much is this trip really costing?” The novel contribution is a system that uses cognitive analysis of potential routes, dynamically derived in real time, to determine the actual cost to the user for manual or autonomous mode driving. The system comprises methods to:
Gather cost factors pertaining to gas along multiple potential route(s) Determine a cost factor for each potential route (based upon above data and
analysis) Identify the least costly route amongst potential route options Communicate the selected personalized route (based on the cost of the route) to
the manual driver for manual vehicle operation or program the updated cost- factored route for autonomous driving
This system focuses on the scenario in which the driver wants the system to derive the most cost effective route to the destination, reached by either manual driving or autonomous vehicle, but not by conventional means. The driver or autonomous vehicle must calculate the current route on the road, and then conduct a system analysis to derive the projected cost of the trip and...