Development and testing of a fully Adaptive Cruise Control system

• Published in: Transportation research. Part C, Emerging technologies Emerging technologies, 2013-04, Vol.29, p.156-170
• Main Authors: Bifulco, Gennaro Nicola, Pariota, Luigi, Simonelli, Fulvio, Di Pace, Roberta
• Format: Article
• Language: English
• Subjects: Adaptive Cruise Control (ACC); Advanced Driving Assistance Systems (ADAS); Applied sciences; Car-following; Dynamic systems; Exact sciences and technology; Ground, air and sea transportation, marine construction; Intelligent Transportation Systems (ITS); Learning-machine; Road transportation and traffic
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2011.07.001

► We model a new cruise control system for on-board electronic control units. ► The system adapts itself in real-time and on-demand by means of short driving sessions. ► Full details are modelled to produce a suitable, behaviourally consistent and safe cruising. ► The modelling framework has shown great capabilities in reproducing observed driving behaviours. ► The modelling framework has been shown to be simple, effective and robust. Adaptive Cruise Control systems have been developed and introduced into the consumer market for over a decade. Among these systems, fully-adaptive ones are required to adapt their behaviour not only to traffic conditions but also to drivers’ preferences and attitudes, as well as to the way such preferences change for the same driver in different driving sessions. This would ideally lead towards a system where an on-board electronic control unit can be asked by the driver to calibrate its own parameters while he/she manually drives for a few minutes (learning mode). After calibration, the control unit switches to the running mode where the learned driving style is applied. The learning mode can be activated by any driver of the car, for any driving session and each time he/she wishes to change the current driving behaviour of the cruise control system. The modelling framework which we propose to implement comprises four layers (sampler, profiler, tutor, performer). The sampler is responsible for human likeness and can be calibrated while in learning mode. Then, while in running mode, it works together with the other modelling layers to implement the logic. This paper presents the overall framework, with particular emphasis on the sampler and the profiler that are explained in full mathematical detail. Specification and calibration of the proposed framework are supported by the observed data, collected by means of an instrumented vehicle. The data are also used to assess the proposed framework, confirming human-like and fully-adaptive characteristics.