Lane-change detection

自2013年底以来，Intact Insurance提供了基于使用的保险计划，旨在利用 ** 数据驱动技术和改进保险产品，通过使用连接到车载计算机的GPS加密狗或智能手机应用程序从客户那里收集驾驶数据。在这种情况下，** 车辆位置和其他传感器数据，如加速度、车轮旋转和磁方位角，在可用时被记录 **。这些数据用于描述驾驶行为，特别是检测危险事件，如超速、急刹车、急加速、急转弯或突然变道。沿着其他上下文 ** 信息，例如给定道路的历史风险、道路状况、其他汽车的行为和速度限制，** 这些数据有可能提供更好的客户细分，沿着风险和成本预测，** 这反过来又可以帮助Intact Insurance为其客户提供最有竞争力的定价。为了 ** 根据i）客户驾驶的地点和时间 **（背景信息）以及ii）客户驾驶的方式（驾驶档案）** 建立客户特定的道路安全指标和风险因素，必须 ** 开发复杂的自动化程序。本研究的主要目标是通过开发和验证车道变换检测措施来解决与驾驶员行为分析相关的问题。具体来说，我们建议在这个项目中调查如何使用通过完整的智能手机应用程序“我的驾驶折扣”或“automerit”收集的加速度计和陀螺仪数据 ** 可以提高车道变化检测。我们还建议评估实现合理的 ** 检测所需的最低数据频率。

Since the end of 2013, Intact Insurance has offered usage-based insurance programs designed to leverage**data-driven technologies and improve insurance products, by collecting driving data from customers using**either a GPS dongle connected to the vehicle's on-board computer or a smartphone application. In this context,**vehicle position and other sensor data, such as acceleration, wheel rotation, and magnetic azimuth, are recorded**when available. This data is used to characterize driving behavior and, in particular, to detect risky events such**as speeding, harsh breaking, hard acceleration, sharp turns or abrupt lane changes. Along with other contextual**information such as historical risk of a given road, road conditions, other cars' behaviour and speed limits,**these data have the potential to provide better customer segmentation along with risk and cost prediction,**which in turn could help Intact Insurance provide the most competitive pricing to their customers. In order to**build customer-specific road safety indicators and risk factors based on i) where and when the client drives**(contextual information), and ii) how the client drives (driving profile), complex automated procedures must be**developed. The main objective of this research is to tackle issues related to driver's behaviour profiling through**the development and validation of a measure for lane change detection.**Specifically, we propose in this project to investigate how the use of accelerometers and gyroscopes data**collected through the Intact smartphone applications "my Driving Discount" or "automerit" can improve lane**change detection. We also propose to evaluate the minimum data frequency required to achieve a reasonable**detection.