Citation

Joo YJ, Park HC, Kho SY, Kim DK. Transp. Res. Rec. 2021; 2675(10): 161-173.

Copyright

(Copyright © 2021, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981211010800

PMID

unavailable

Abstract

Despite the urgent need for continuous risk assessments during autonomous driving, achieving reliable assessment results is still challenging because of the unpredictable behaviors of adjacent human drivers and the resulting complexity. Such complexity increases particularly during lane changes because several vehicles need to interact with other vehicles. Therefore, this paper proposes a new framework to analyze lane-changing risk on freeways considering the forecastability in adjacent vehicles. Virtual lane-change scenarios are constructed based on historical maneuvers in adjacent vehicles, and the risk of potential lane change is evaluated through the safety evaluation result of the scenario. Adjacent vehicles' future maneuvers are predicted using a multivariate Bayesian structural time series model, and the forecastability is estimated as the standard error of the predicted values. The failure probability of those lane-changing scenarios is obtained through the first-order reliability method, assuming that failure occurred when any time-to-collision value for adjacent vehicles was less than a threshold at the end of the lane change. This study tested two scenarios with three levels of uncertainty to show the effect of uncertainty on the level of risk. The results showed that the reduced uncertainty allowed a clearer distinction between risky situations. The proposed framework differentiates itself from existing methods by estimating higher risk in an adjacent vehicle's more significant uncertainties. It is expected that the outcome of this study will be valuable in developing reliable lane-change strategies in autonomous driving.

Language: en