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摘要
Car-following is the activity of safely driving behind a leading vehicle. Traditional mathematical car-following models capture vehicle dynamics without considering human factors, such as driver distraction and the reaction delay. Consequently, the resultant model produces overly safe driving traces during simulation, which are unrealistic. Some recent work incorporate simplistic human factors, though model validation using experimental data is lacking. In this paper, we incorporate three distinct human factors in new compositional car-following model called modal car-following model, which is based on hybrid input output automata (HIOA). HIOA have been widely used for the specification and verification of cyber-physical systems. HIOA incorporate the modeling of the physical system combined with discrete mode switches, which is ideal for describing piece-wise continuous phenomena. Thus, HIOA models offer a succinct framework for the specification of car-following behavior. The human factors considered in our approach are estimation error (due to imperfect distance perception), reaction delay, and temporal anticipation. Two widely used car-following models called Intelligent Driver Model (IDM) and Full Velocity Difference Model (FVDM) are used for extension and comparison purpose. We evaluate the root mean square (rms) error of the following vehicle position using the traces obtained from human drives through different driving scenarios. The result shows that our model reduces the rms error in IDM and FVDM by up to 48.8% and 7.41%, respectively.
- 出版日期2018-2
