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

Hyeon, Eunjeong; Kim, Youngki; Ersal, Tulga; Stefanopoulou, Anna

doi:10.1115/1.4052272

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

Journal Article · Mon Sep 27 00:00:00 EDT 2021 · Journal of Dynamic Systems, Measurement, and Control

DOI:https://doi.org/10.1115/1.4052272· OSTI ID:1980671

Hyeon, Eunjeong ^[1]; Kim, Youngki; Ersal, Tulga; Stefanopoulou, Anna

OSTI

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, Journal Name: Journal of Dynamic Systems, Measurement, and Control Journal Issue: 1 Vol. 144; ISSN 0022-0434

Publisher:: ASME

Country of Publication:: United States

Language:: English

References (11)

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