Understanding Driver Car-Following Behavior Using a Fuzzy Logic Car-Following Model

Sato, Tomomasa; Akamatsu, Motoyuki

doi:10.5772/36126

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…These indicate a satisfactory model-to-data fit compared to other real-world data [14]. Additionally, the results are similar to the errors calculated in the other car-following data on the same road section [22]. Figure 8 presents relative speed-acceleration mapping resulting from the fuzzy logic model estimation.…”

Section: Resultssupporting

confidence: 77%

See 1 more Smart Citation

Applying Fuzzy Logic to Understanding Driving Modes in Real Road Environments

Sato¹,

Akamatsu²

2015

Fuzzy Logic - Tool for Getting Accurate Solutions

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Section: Resultssupporting

confidence: 77%

“…The defuzzification method is a weighted average. See reference [22] for details of the fuzzy logic car-following model, including input variable validation and model validation.…”

Section: Fuzzy Logic Car-following Modelmentioning

confidence: 99%

Applying Fuzzy Logic to Understanding Driving Modes in Real Road Environments

Sato¹,

Akamatsu²

2015

Fuzzy Logic - Tool for Getting Accurate Solutions

Self Cite

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“…In this study, the highest frequency of time gap in car-following situations is within the range of 1.5-2.0s in the China dataset, which is consistent with the results obtained by [27]. The highest frequency of time gap to the preceding vehicle in the Sweden dataset is in the range of 1.0-1.5s, which is similar to the results observed in [28] (1.0-1.5s in the speed range of 30-69 km/h in the UK, 1.0-1.5s in the speed range of 30-49 km/h in Japan, 0.5-1.0s in the speed range of 50-69 km/h in Japan). The distribution of gap distance in this study is similar to the results in [29].…”

Section: Comparison Of Ttc and Time Gap In Various Relative Speed supporting

confidence: 91%

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Tong

Selpi

2020

IEEE Trans. Intell. Transport. Syst.

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Understanding car-following behaviour in different countries is essential for the design and development of autonomous driving and further development of active safety systems that can function well worldwide, in particular in mixed traffic conditions. However, very few studies exist that compare car-following behaviours in different countries based on real driving data. This paper analyses the similarities and differences of drivers' car-following behaviour, in terms of time gap, gap distance, and time to collision, using both China and Sweden datasets from real road driving studies, in a bid to identify how these indicators affect drivers' speed control in car-following situations. Results indicate that the highest frequency of gap distance is observed in the same value-range in both datasets, while the highest frequency of time gap in the Sweden dataset is found at a lower value-range than the corresponding value-range in the China dataset. For both datasets, time gap is observed to be a more reliable indicator for car-following analysis than gap distance, since it is less sensitive to speed variations. Furthermore, time to collision (TTC) in the low travel speed ranges (v<50 km/h) tends to be steady in comparison with TTC at other speed ranges, so is the time gap in the high speed ranges (v>90 km/h). Therefore, time gap is recommended as the safety indicator for car-following analysis in high speed conditions, while a combination of time gap and TTC is recommended for low speed conditions, especially on urban roads.

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“…A common measure to describe drivers’ interaction and reactions to each other is the distance between them [24, 25]. Drivers attempt to maintain a certain headway in a car‐following situation and thus correct if necessary [26]. When they expect manoeuvres of a preceding vehicle, they increase the distance [4, 5].…”

Section: Analysis Of Multi‐driver Simulator Datamentioning

confidence: 99%