Citation

Ro JW, Roop PS, Malik A, Ranjitkar P. IEEE Trans. Intel. Transp. Syst. 2018; 19(2): 639-648.

Copyright

(Copyright © 2018, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2017.2759273

PMID

unavailable

Abstract

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.

Language: en

Keywords

automata theory; compositional car-following model; Computational modeling; Cyber-physical systems; Data models; Delays; discrete mode switches; distinct human factors; driver distraction; driver information systems; driving scenarios; estimation error; Full Velocity Difference Model; HIOA models; human car-following behavior; human drives; human factors; Human factors; hybrid input output automata; imperfect distance perception; Intelligent Driver Model; leading vehicle; Mathematical model; mean square error methods; modal car-following model; model validation; physical system; piece-wise continuous phenomena; reaction delay; root mean square error; simplistic human factors; temporal anticipation; vehicle dynamics; Vehicle dynamics; vehicle position; Vehicles