Prediction of Preceding Driver Behavior for Fuel Efficient Cooperative Adaptive Cruise Control

Abstract

Advanced driver assistance systems like cooperative adaptive cruise control (CACC) are designed to exploit information provided by vehicle-to-vehicle (V2V) and/or infrastructure-to-vehicle (I2V) communication systems to achieve desired objectives such as safety, traffic fluidity or fuel economy. In a day to day traffic scenario, the presence of unknown disturbances complicates achieving these objectives. In particular, CACC benefits in terms of fuel economy require the prediction of the behavior of a preceding vehicle during a finite time horizon. This paper suggests an estimation method based on actual and past inter-vehicle distance data as well as on traffic and upcoming traffic lights. This information is used to train a set of nonlinear, autoregressive (NARX) models. Two scenarios are investigated, one of them assumes a V2V communication with the predecessor, the other uses only data acquired by on-board vehicle sensors. Depending on the applied approach and the moving space of the controlled vehicle, the thus obtained (imperfect) prediction allows fuel benefits in a range of 5% to 25% in the case of moderate, non-congested traffic. This is confirmed both by simulation and measurement. Compared to existing prediction methods, the proposed strategy delivers quite promising results. Copyright © 2014 SAE International.