This study proposes an addition to the architecture of the context-aware Driver Assistance Systems (DASs) by introducing a context identification layer to the reasoning subsystem. The proposed layer contains two algorithms that work in sequence: the infrastructure detection algorithm and the driver classification algorithm, respectively. The infrastructure detection algorithm aims to identify intersection-related driving when the driver adjusts his or her behavior due to the presence of an intersection ahead. Then, the driver classification algorithm categorizes drivers into cautious, normal, and aggressive at both locations. Data from 64 drivers in a Naturalistic Driving Study was used to prove the concept of the proposed layer. Several behavioral measures were extracted, including following distance, relative speed, headway, acceleration, time-to-collision, and jerk. These behavioral measures were then used to train the algorithms in the context identification layer. The results of both algorithms supported the concept of the proposed layer. These results have implications related to driver behaviors including i) the intersection-related driving behavior can be detected, ii) the drivers tend to be relatively aggressive at intersections when compared to segments, and iii) the driver classification, which ignores the driver’s relative location to intersections, were more likely to misclassify drivers as aggressive when they were in high intersection density areas such as downtown cores. The findings of this study emphasized the importance of context-aware DAS architecture that acknowledges and integrates the variation in driver behavior due to both a change in the surrounding environment and drivers’ individual needs.

这项研究通过向推理子系统引入上下文识别层，为上下文感知的驾驶员辅助系统（DAS）的体系结构提出了新的建议。提议的层包含两种按顺序工作的算法：基础结构检测算法和驱动程序分类算法。基础设施检测算法旨在在驾驶员由于前方交叉路口的存在而调整其行为时识别与交叉路口有关的驾驶。然后，驾驶员分类算法在两个位置将驾驶员分类为谨慎，正常和积极。在自然驾驶研究中，来自64位驾驶员的数据被用来证明所提议层的概念。提取了几种行为指标，包括跟随距离，相对速度，前进距离，加速度，碰撞时间和混蛋。然后将这些行为措施用于在上下文识别层中训练算法。两种算法的结果都支持提出的层的概念。这些结果与驾驶员行为有关，其中包括：i）可以检测到交叉路口相关的驾驶行为； ii）与路段相比，驾驶员在十字路口时往往比较激进，并且iii）驾驶员分类，忽略了驾驶员的相对位置到十字路口时，当他们在高十字路口密度区域（例如市中心）时，更有可能将驾驶员归类为攻击性驾驶员。