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.

尽管迫切需要在自动驾驶过程中进行连续的风险评估，但是由于相邻人类驾驶员的行为无法预测以及由此导致的复杂性，实现可靠的评估结果仍然具有挑战性。由于若干车辆需要与其他车辆进行交互，因此这种复杂性尤其在车道变更期间增加。因此，本文提出了一种新框架，该模型考虑了相邻车辆的可预测性来分析高速公路上的变道风险。基于相邻车辆的历史操作构造虚拟车道变更场景，并通过该场景的安全评估结果评估潜在车道变更的风险。使用多元贝叶斯结构时间序列模型预测相邻车辆的未来机动性，并且将可预测性估计为预测值的标准误差。假设当相邻车辆的任何碰撞时间值小于车道变更结束时的阈值时发生故障，则通过一阶可靠性方法获得这些车道变更场景的故障概率。这项研究测试了具有三种不确定性水平的两种情况，以显示不确定性对风险水平的影响。结果表明，不确定性的降低使危险情况之间的区别更加清晰。所提出的框架通过估计相邻车辆的较大不确定性中的较高风险，将自己与现有方法区分开来。预计这项研究的结果将对开发自动驾驶中可靠的车道变更策略很有用。