A Context-Aware Driver Model for Determining Recommended Speed in Blind Intersection Situations
A Context-Aware Driver Model for Determining Recommended Speed in Blind Intersection Situations
复制标题
用于确定盲路口情况下建议速度的上下文感知驾驶员模型
DOI：
10.1016/j.aap.2021.106447
复制
发表时间：
2021
影响因子：
5.9
通讯作者：
Hideo Inoue
中科院分区：
文献类型：
--
作者：
Yuichi Saito;Fumio Sugaya;Shintaro Inoue;Pongsathorn Raksincharoensak;Hideo Inoue
涉及弱势道路使用者的未遂事件可能导致严重事故。安全而谨慎的专业司机会进行危险预测驾驶，他们自然会试图将当前的驾驶环境与他们已经形成的预先存在的类别相匹配，即预测可能发生的情况，从而减少不确定性。在这项研究中，我们的目标情况包括一个骑自行车的人试图在一个盲点过马路。本研究的目的是建立一个上下文感知的驾驶员模型，以确定盲十字路口的推荐驾驶速度，该模型基于对险些撞车事故发生前驾驶员行为和道路环境因素数据的分析。首先，我们使用数据库中提供的管理工具提取行车记录仪数据。其次，风险被定义为驾驶员执行规避动作以避免碰撞的时间余量，使用安全缓冲时间对提取的数据进行量化。安全缓冲时间可以作为驾驶员根据给定道路环境调整车速的结果来观察。开发上下文感知驱动模型的一个关键方面是基于风险量化将提取的近靶数据分为两个级别：低风险事件和高风险事件。低、高风险事件分别被认为是驾驶员根据给定的道路环境适当调整车速的结果，以及无法或未能调整车速的结果。第三，基于低风险事件数据集的多元线性回归分析，我们构建了一个上下文感知的驾驶员模型，根据给定的道路环境产生推荐的车速。通过逐步回归确定道路环境变量，将其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，我们定量地可视化了驾驶员根据给定的道路环境设置速度调整和增加或减少速度的基线。模型验证的决定系数（r2）为0.20,5次交叉验证的平均绝对误差（MAE）为6.54 km/h。最后，为了研究构建的驾驶员模型对安全性能的有效性，我们使用高风险事件数据集作为测试数据。从理论上讲，构建的驾驶员模型引导驾驶员按照推荐速度驾驶车辆，从而将一半以上的高风险事件转化为低风险事件。研究结果表明，基于上下文感知的驾驶员模型可以根据道路环境因素调整盲区交叉口的接近速度。
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 2) 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.