University of Tsukuba, 1-1-1 Tennoudai, Tsukuba 305-8573, Ibaraki, Japan.
Toyota Motor Corporation, 1200 Mishuku, Susono 410-1193, Shizuoka, Japan.
Accid Anal Prev. 2021 Dec;163:106447. doi: 10.1016/j.aap.2021.106447. Epub 2021 Oct 18.
The near-miss events involving vulnerable road users can lead to serious accidents. Safe and careful expert drivers perform a hazard-anticipatory driving and they will naturally seek to reduce the uncertainty by attempting to fit their current driving context into a pre-existing category they have already developed, that is, predicting what can happen. In this study, our target situation consists of a cyclist attempting a road crossing at a blind spot. This study aims at developing a context-aware driver model for determining the recommended driving speed at blind intersections based on the analysis of near-miss-incidence database, which includes the data on driver behavior and road environmental factors just before the near-miss. First, we extracted the drive-recorder data using the management tool provided in the database. Second, risk, which is defined as the time margin for drivers to perform evasive actions to avoid a crash, was quantified for the extracted data using the safety-cushion time. The safety-cushion time can be observed as a result of the driver's adjustment to the vehicle velocity depending on the given road environment. One of the key aspects in developing the context-aware driver model is to categorize the extracted near-miss data into two levels based on the risk quantifications: low- and high-risk events. The low- and high-risk events were regarded as a result of the driver's appropriate adjustment of, and inability or failure to adjust the vehicle velocity depending on the given road environment, respectively. Third, based on a multiple linear regression analysis with low-risk event dataset, we constructed a context-aware driver model to produce the recommended vehicle speed depending on the given road environment. The road environment variables, determined by stepwise regression, were identified as factors that reduced or increased the vehicle velocity at blind intersections, and were incorporated into the model as predictors. Furthermore, we quantitatively visualized drivers setting the baseline for speed adjustment and increasing or decreasing the speed according to the given road environment context. Fourth, the model validation demonstrated a coefficient of determination (R) of 0.20, and a mean absolute error (MAE) of 6.54 km/h on average in the 5-fold cross-validation. Finally, to investigate the effectiveness of the constructed driver model on safety performance, we used the dataset of high-risk events as test data. Theoretically, the constructed driver model guided the drivers to drive the vehicle at the recommended speed, and thus convert more than half of the high-risk events into low-risk events. These results indicate that the context-aware driver model is feasible to be used to adjust the approaching speed at blind intersections in accordance with the road environment factors.
涉及弱势道路使用者的险象环生事件可能导致严重事故。安全谨慎的专业驾驶员进行危险预测性驾驶，他们会自然地试图将当前的驾驶环境纳入他们已经开发的预先存在的类别中，即预测可能发生的情况。在本研究中，我们的目标情况是一名试图在盲点处穿越道路的自行车骑手。本研究旨在开发一种情境感知驾驶员模型，根据近失事件数据库中的分析，确定在盲交叉口的推荐行驶速度，该数据库包括近失发生前驾驶员行为和道路环境因素的数据。首先，我们使用数据库中提供的管理工具提取了行车记录仪数据。其次，使用安全缓冲时间量化了提取数据的风险，风险定义为驾驶员执行避撞动作以避免碰撞的时间余量。安全缓冲时间可以观察到驾驶员根据给定的道路环境调整车辆速度的结果。开发情境感知驾驶员模型的一个关键方面是根据风险量化将提取的近失数据分为两个级别：低风险和高风险事件。低风险和高风险事件分别被视为驾驶员根据给定的道路环境适当调整和无法或未能调整车辆速度的结果。第三，基于低风险事件数据集的多元线性回归分析，我们构建了一个情境感知驾驶员模型，根据给定的道路环境产生推荐的车辆速度。通过逐步回归确定的道路环境变量被确定为降低或增加盲交叉口车辆速度的因素，并作为预测因子纳入模型。此外，我们还定量地可视化了驾驶员根据给定的道路环境背景设定速度调整基准，并根据给定的道路环境上下文增加或减少速度。第四，模型验证表明，在 5 折交叉验证中，决定系数 (R) 为 0.20，平均平均绝对误差 (MAE) 为 6.54 公里/小时。最后，为了研究构建的驾驶员模型对安全性能的有效性，我们将高风险事件数据集用作测试数据。从理论上讲，构建的驾驶员模型指导驾驶员以推荐的速度驾驶车辆，从而将超过一半的高风险事件转换为低风险事件。这些结果表明，情境感知驾驶员模型可用于根据道路环境因素调整盲交叉口的接近速度，是可行的。
