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Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis

机译：基于传感器的驾驶员行为分析车载信息的提取方法

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摘要

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.
机译：车辆技术的进步导致配备有传感器的车辆的开发，以从车载控制器区域网络（CAN）系统中获得诸如发动机速度和车速等标准化信息。然而，在可以框架中获取专有信息，例如制动踏板和方向盘操作，存在挑战，这对于驾驶员行为分析至关重要。这种信息提取需要电子控制单元标识符分析和附带数据解释。在本文中，我们展示了一种用于使用与所需信息相关的传感器数据自动提取专有车载信息的系统。首先，所提出的系统通过使用惯性测量单元和全球定位系统值估计通过阈值，随机森林和长的短期内存技术来估计车辆的驾驶状态。然后，车载车辆内部段可以使用估计来帧，并通过我们的评分方法评估每个段，以通过通过建议的距离匹配技术检查每个候选和其估计之间的相似性来选择合适的候选。我们在城市环境中使用真正的车辆进行拟议系统的全面实验。性能评估表明，驾驶条件的估计精度为84.20％，车载信息的提取精度为82.31％，这意味着所提出的方法对于自动提取专有车载信息是非常可行的。

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