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Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data

Yuzhi Chen et al.

Analytic Methods in Accident Research2025https://doi.org/10.1016/j.amar.2025.100370article
ABDC B
Weight
0.59

Abstract

• The proposed approach can capture the underlying behavioral patterns of drivers. • Quantifying the time-dependent treatment effect of warning messages on drivers. • The effect varies over time, initially increasing then progressively decreasing. • Identifying reaction time, brake adjustment time, and progressive braking duration. • Heterogeneity exists in various phases of driver speed reduction behavior. This paper proposes a novel functional data analysis approach to investigate the time-dependent effect of advanced driver assistance systems (ADAS), specifically forward collision warnings, on driver speed reduction behavior. Existing aggregate measures compress temporal information within driver behavior profiles and fail to explicitly reveal the temporal dependency of such effect. With the proposed approach, the functional representation method is adopted to capture the underlying driver behavior in response to warning messages and address issues of irregularly spaced observations and measurement errors; the results of the functional principal component analysis with the bootstrap-enhanced Kaiser-Guttman method reveal important patterns in driver response behaviors; and a nonparametric functional varying coefficient regression model, considering vehicle initial motions and drivers’ acceleration styles, is established. This regression model utilizes coefficient functions to estimate the time-dependent effect of ADAS. The proposed approach is evaluated based on the New York City connected vehicle dataset using forward collision warning event records. The results suggest that the treatment effect of the warning messages is time-dependent, initially increasing before progressively decreasing over time. Driver responses can be decomposed into several phases at the 95 % confidence level, including reaction time (1.3 s), brake adjustment time (1.3 s), progressive braking duration (2.7 s), and effective treatment duration (4.0 s). The time-dependent bootstrap confidence interval confirms driver heterogeneity in these distinct phases. The proposed functional data analysis approach can serve as a paradigm for quantifying the treatment effect of other ADAS applications. The findings can support the improvements of ADAS design and the development and calibration of driver behavior models accounting for ADAS.

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https://doi.org/https://doi.org/10.1016/j.amar.2025.100370

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@article{yuzhi2025,
  title        = {{Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data}},
  author       = {Yuzhi Chen et al.},
  journal      = {Analytic Methods in Accident Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.amar.2025.100370},
}

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Evidence weight

0.59

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.60 × 0.4 = 0.24
M · momentum0.82 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.