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Office of Scientific and Technical Information
 

Data-Driven Forgetting and Discount Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control

Journal Article · · Journal of Dynamic Systems, Measurement, and Control
DOI:https://doi.org/10.1115/1.4052272· OSTI ID:1980671
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Abstract

This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.

Research Organization:
Southwest Research Institute, San Antonio, TX (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
DOE Contract Number:
AR0000837
OSTI ID:
1980671
Journal Information:
Journal of Dynamic Systems, Measurement, and Control, Vol. 144, Issue 1; ISSN 0022-0434
Publisher:
ASME
Country of Publication:
United States
Language:
English

References (11)

ARIMA-Based Road Gradient and Vehicle Velocity Prediction for Hybrid Electric Vehicle Energy Management June 2019
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Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications February 2017
Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management May 2014
Short Term Prediction of a Vehicle's Velocity Trajectory Using ITS April 2015
Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks July 2017
Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control January 2016
Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy May 2013
Congested traffic states in empirical observations and microscopic simulations August 2000
Trip Based Near Globally Optimal Power Management of Plug-in Hybrid Electric Vehicles Using Gas-Kinetic Traffic Flow Model January 2008
Car-Following Models Based on Driving Strategies October 2012

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