Development and testing of a fully Adaptive Cruise Control system |
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| Institution: | 1. Department of Transportation Engineering, University of Naples Federico II, via Claudio 21, 80125 Napoli, Italy;2. Department of Engineering, University of Sannio, Corso Garibaldi 107, 82100 Benevento, Italy;3. Department of Civil Engineering, University of Salerno, via Ponte Don Melillo 21, 84084 Fisciano (SA), Italy;1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, P.R. China;2. Department of Geography, SUNY New Paltz, United States;3. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, 611756, P.R. China;1. Collaborative Transport Hub, City, University of London, United Kingdom;2. University of Southampton, United Kingdom;3. City University London, United Kingdom;1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China;2. Traffic Management Research Institute of the Ministry of Public Security, Wuxi, Jiangsu 214151, China;3. Department of Civil & Environmental Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA;4. Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI, 53706, USA;1. Partners for Advanced Transportation Technology (PATH), Institute of Transportation Studies, University of California, Berkeley, Richmond Field Station Building 452, Richmond, CA 94804, United States;2. Turner-Fairbank Highway Research Center, Federal Highway Administration, McLean, VA 22101, United States |
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| Abstract: | 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. |
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| Keywords: | Adaptive Cruise Control (ACC) Car-following Learning-machine Advanced Driving Assistance Systems (ADAS) Intelligent Transportation Systems (ITS) Dynamic systems |
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