期刊
IET INTELLIGENT TRANSPORT SYSTEMS
卷 14, 期 7, 页码 693-701出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-its.2019.0378
关键词
road vehicles; road traffic; road safety; adaptive control; traffic control; road accidents; collision avoidance; road traffic control; traffic engineering computing; adaptive cruise control platoon; freeway capacity; potential side effects; driving simulator study; Federal Highway Administration; United States; unique driving behaviour; CACC platoon; conventional merging model; passive decision action; proactive action; unique behaviour; future CACC simulation evaluation; longitudinal trajectory model; merging duration prediction model; CACC automated controller
资金
- National Natural Science Foundation of China [71831002]
- Fundamental Research Funds for the Central Universities [1600219316]
- Shanghai Yangfan Program [18YF1424200]
- National Key RAMP
- D Program of China [2018YFB1600600]
Cooperative adaptive cruise control (CACC) has shown great potential in improving freeway capacity. Although the benefit of CACC is obvious, its potential side effects are not yet well studied. One of the major factors that have been overlooked is merging behaviour. A driving simulator study has been recently conducted at the Federal Highway Administration of the United States and reveals that there is unique driving behaviour when joining and leaving a CACC platoon. Unlike the conventional merging model which is a passive decision action, merging into a CACC platoon is a proactive action. Without simulating this unique behaviour, any simulation evaluation on CACC is biased. To improve the validity of future CACC simulation evaluation, this research constructs a merging model. The model consists of two parts: the longitudinal trajectory model and the merging duration prediction model. The model was constructed for both human manual driver and CACC automated controller. The evaluation of the proposed model shows that the model is 96.5% accurate in terms of merging duration prediction and 95.2% accurate in terms of speed prediction.
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