Learning To Associate Observed Driver Behavior with Traffic Controls
Learning To Associate Observed Driver Behavior with Traffic Controls
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DOI：
10.3141/1679-13
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发表时间：
1999
影响因子：
1.7
通讯作者：
Christopher Pribe;Seth Rogers
中科院分区：
文献类型：
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作者：
Christopher Pribe;Seth Rogers
自适应技术支持新工具的开发，以帮助交通工程师分类和评估交叉口的交通流。一种学习将驾驶员行为与交通控制子集（例如，交通信号灯和停车标志）。在十字路口的交通控制不容易获得或未知的情况下，该工具根据观察到的驾驶员行为自动识别十字路口处存在的交通控制。此能力可用于用交通控制位置来扩充数字地图。如果交通管制已知或之前已分类，该工具会标记驾驶员行为与实际存在的交通管制不一致的情况。这种能力可以被用于驾驶员的各种服务，例如动态路由和新的安全系统。交通工程师也可以使用它来评估控制放置在真实的或模拟的道路网络中，通过发现引起不寻常的驾驶员行为的情况。该工具用已知控制的一组段的驱动数据校准。该工具首先学习识别各个路段上的控制，然后使用手工规则来验证交叉口处路段之间的控制一致性。该数据集包括在正常日常驾驶期间收集的真实位置数据。该工具准确地识别了100%通过验证的数据。这些结果鼓励相信，该系统可以提供交通工程师与驾驶员行为和交通控制之间的可靠映射。
Adaptive techniques support the development of new tools to help traffic engineers classify and evaluate traffic flow at intersections. A tool that learns to associate driver behavior with a subset of traffic controls (e.g., stoplights and stop signs) is described. In the case in which the traffic controls for an intersection are not readily available or are unknown, the tool automatically identifies the traffic controls present at an intersection from observed driver behavior. This capability may be used to augment digital maps with traffic control locations. In the case in which traffic controls are known or have previously been classified, the tool flags instances in which driver behavior is inconsistent with the traffic controls actually present. This capability might be used by various services for drivers such as dynamic routing and new safety systems. It might also be used by traffic engineers to evaluate control placement in real or simulated road networks by finding situations that elicit unusual driver behavior. The tool is calibrated with driving data for a set of segments with known controls. The tool first learns to identify the controls present on individual road segments and then uses handcrafted rules to verify control consistency across segments at intersections. The data set comprised real-world position data collected during normal daily driving. The tool accurately identified 100 percent of the data that passed verification. These results encourage belief that the system can provide traffic engineers with a reliable mapping between driver behavior and traffic controls.