Real-Time Monitoring and Driver Feedback to Promote Fuel Efficient Driving

• URL: http://arxiv.org/abs/2007.02728v1
• Date: Fri, 3 Jul 2020 09:23:53 GMT
• Title: Real-Time Monitoring and Driver Feedback to Promote Fuel Efficient Driving
• Authors: Sandareka Wickramanayake, H.M.N Dilum Bandara, Nishal A. Samarasekara
• Abstract summary: We propose a novel framework to promote fuel-efficient driving behaviors through real-time automatic monitoring and driver feedback. A random-forest based classification model developed using historical data is used to identify fuel-inefficient driving behaviors. When an inefficient driving action is detected, a fuzzy logic inference system is used to determine what the driver should do to maintain fuel-efficient driving behavior.
• Score: 0.7087237546722617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving the fuel efficiency of vehicles is imperative to reduce costs and protect the environment. While the efficient engine and vehicle designs, as well as intelligent route planning, are well-known solutions to enhance the fuel efficiency, research has also demonstrated that the adoption of fuel-efficient driving behaviors could lead to further savings. In this work, we propose a novel framework to promote fuel-efficient driving behaviors through real-time automatic monitoring and driver feedback. In this framework, a random-forest based classification model developed using historical data to identifies fuel-inefficient driving behaviors. The classifier considers driver-dependent parameters such as speed and acceleration/deceleration pattern, as well as environmental parameters such as traffic, road topography, and weather to evaluate the fuel efficiency of one-minute driving events. When an inefficient driving action is detected, a fuzzy logic inference system is used to determine what the driver should do to maintain fuel-efficient driving behavior. The decided action is then conveyed to the driver via a smartphone in a non-intrusive manner. Using a dataset from a long-distance bus, we demonstrate that the proposed classification model yields an accuracy of 85.2% while increasing the fuel efficiency up to 16.4%.

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