Intelligent Driver System for Improving Fuel Efficiency in Vehicle Fleets

@article{Wickramasinghe2019IntelligentDS,
  title={Intelligent Driver System for Improving Fuel Efficiency in Vehicle Fleets},
  author={Chathurika S. Wickramasinghe and Kasun Amarasinghe and Daniel L. Marino and Zachary A. Spielman and Ira Pray and David I. Gertman and Milos Manic},
  journal={2019 12th International Conference on Human System Interaction (HSI)},
  year={2019},
  pages={34-40}
}
A viable solution for increasing fuel efficiency in vehicles is optimizing driver behavior. In our previous work, we proposed a data-driven Intelligent Driver System (IDS), which calculated an optimal driver behavior profile for a fixed route. During operation, the optimal behavior was prompted to the drivers to guide their behavior toward improving fuel efficiency. This system was proposed for fleet vehicles mainly because a small increase in fuel efficiency of fleet vehicles has a significant… 

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References

SHOWING 1-10 OF 27 REFERENCES

Improving Vehicle Fleet Fuel Economy via Learning Fuel-Efficient Driving Behaviors

  • O. LindaM. Manic
  • Engineering
    2012 5th International Conference on Human System Interactions
  • 2012
TLDR
The proposed Intelligent Driver System (IDS) utilizes vehicle performance data combined with GPS information on fixed routes to incrementally build a model of the historically most fuel efficient driving behavior, showing potential for substantial fuel economy improvements.

Data driven fuel efficient driving behavior feedback for fleet vehicles

TLDR
This paper presents a fuel efficient driving behavior identification and feedback architecture that is specific to fleet vehicles that was tested on the Idaho National Laboratory bus fleet and was shown to be able to increase the fuel economy by 9% and 20% in two different driving scenarios.

Driving behavior prompting framework for improving fuel efficiency

TLDR
A low cost framework and a hardware setup for prompting drivers on fuel efficient behavior is presented that includes an information rich, intuitive un-obstructive visualization and was implemented using low cost, commercial-off-the-shelf hardware.

Towards intelligent fleet management: Local optimal speeds for fuel and emissions

  • Xiaoliang Ma
  • Engineering
    16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)
  • 2013
TLDR
This study presents a recent Swedish R&D project for developing a dynamic fleet management system that incorporates real-time traffic information, eco-driving guidance and automated vehicle control in real- time heavy vehicle platooning.

Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management

TLDR
The proposed SMPCL approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.

Vehicle Speed Profiles to Minimize Work and Fuel Consumption

This paper addresses the question of what speed profile will minimize fuel consumption of a land transport vehicle (road or rail) in traversing a path or route. Numerous previous studies, using a

Modeling and Recognizing Driver Behavior Based on Driving Data: A Survey

TLDR
A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle.

Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

TLDR
A survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends is provided.