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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.

Modern cars feature a variety of different driving assistance systems, which aim to improve driving comfort and safety as well as fuel consumption. Due to the technical advances and the possibility to consider vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, cooperative adaptive cruise control (CACC) strategies have received significant attention from both research and industrial communities. The performance of such systems can be enhanced if the future velocity of the surrounding traffic can be predicted. Generally, human driving behavior is a complex process and influenced by several environmental impacts. In this work a stochastic model of the velocity of a preceding vehicle based on the incorporation of available information sources such as V2I, V2V and radar information is presented. The main influences on the velocity prediction considered in this approach are current and previous velocity measurements and traffic light signals.

Adaptive cruise control (ACC) systems allow a safe and reliable driving by adapting the velocity of the vehicle to velocity setpoints and the distance from preceding vehicles. This substantially reduces the effort of the driver especially in heavy traffic conditions. However, standard ACC systems do not necessarily take in account comfort and fuel efficiency. Recently some work has been done of the latter aspect. This paper extends previous works for CI engines by incorporating a prediction model of the surrounding traffic and a simplified control law capable for real time use in experiments. The prediction model itself uses sinusoidal functions as the traffic measurements often show periodic behavior and is adapted in every sample instant with respect to the predecessor's velocity. Furthermore, the controlled vehicle is forced to stay within a specific inter-vehicle distance corridor to avoid collisions and ensure safe driving.