Modeling the development of vehicle lateral control skills in a cognitive architecture |
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| Institution: | 1. Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China;2. Department of Systems Design Engineering, University of Waterloo, Waterloo N2L 3G1, Canada;3. Koguan Law School, Shanghai Jiao Tong University, 200030 Shanghai, China;4. Department of Psychology, Carnegie Mellon University, Pittsburgh 15213, United States;5. The National Key Laboratory of Human Factors, Hangzhou 310028, China;1. Plasma Processed Materials Group, CSIR-National Physical Laboratory, Dr. K.S. Krishnan Road, New Delhi 110012, India;2. Department of Physics, Indian Institute of Technology Delhi, New Delhi 110016, India;1. Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada;2. School of Life Sciences, Arizona State University, Tempe, AZ, USA;1. The Petroleum Institute, Chemical Engineering Department, P.O. Box 2533, Abu Dhabi, United Arab Emirates;2. Eindhoven University of Technology, Department of Chemical Engineering and Chemistry, Separation Technology Group, Den Dolech 2, 5600 MB Eindhoven, Netherlands |
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| Abstract: | The development of lateral control skills is crucial to driving safety. The current study examined a computational method using a cognitive architecture to model the learning process of vehicle lateral control. In a fixed-base driving simulator, an experiment compared the lateral control performance of non-drivers, novices, and experienced drivers. A cognitive model using Adaptive Control of Thought-Rational (ACT-R) was built to model the learning process of lateral control skills. The modeling results were compared with the human results. The drivers with more experience had better lateral control performance. The model produced similar results as the human results and modeled the progress of learning. The model provided a computational explanation for the mechanisms of lateral control skill learning. Implication and future studies were discussed. |
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| Keywords: | Driving performance Cognitive modeling Driving skills Experience ACT-R |
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