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… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 27 REFERENCES
Improving Vehicle Fleet Fuel Economy via Learning Fuel-Efficient Driving Behaviors
  • O. Linda, M. 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.
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.
Curve speed model for driver assistance based on driving style classification
TLDR
The authors’ model reflects the speed preferences of three different types of drivers on the premise of driving safety on curves and shows a stationary speed transition when the road adhesion coefficient exceeds 0.8, which indicates that rollover, instead of sideslip, becomes the primary cause for lateral instability crashes on curves.
Three Decades of Driver Assistance Systems: Review and Future Perspectives
TLDR
This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems, and examines the progress incented by the use of exteroceptive sensors such as radar, video, or lidar in automated driving in urban traffic and in cooperative driving.
...
...