公开(公告)号：WO2018083352A1

公开(公告)日：2018-05-11

申请号：PCT/EP2017/078510

申请日：2017-11-07

Applicant: SWISS REINSURANCE COMPANY LTD.

Inventor： NAGEL, Uwe ,  VENKATESWARAN, Ramya ,  LARKIN, Peter ,  ELSASSER, Christian

IPC: G06Q40/08

Abstract: Proposed are a measuring apparatus(1) and a measuring method for automated traffic and driving pattern recognition and location-dependent measurement and forecast of absolute and relative risks for car accidents based on exclusively non-insurance related measuring data and based on automated traffic pattern recognition and associated with the traffic and driving pattern providing a high degree of temporal and spatial resolution. The proposed apparatus(1) provides a grid- based (2121, 2122, 2123, 2124), technically new way of automation of automated traffic and driving pattern recognition and risk-prediction related to motor accidents using environment based factors (elevation, road network, traffic data, weather conditions including socio-economic factors)that are impacting motor traffic and are location dependent received from appropriate measuring devices (41,…,45). In this way, predictions of the accident risk for arbitrary areas can be provided. The measuring apparatus(1) is calibrated by comparing features of areas or road segments with the number and type of accidents that have measured or registered there, linking the features and accident data e.g. using the disclosed machine learning techniques.

Abstract translation: 提出了一种测量设备（1）和测量方法，用于基于非保险相关测量的自动交通和驾驶模式识别以及与位置相关的测量和预测车辆事故的绝对和相对风险 数据并基于自动化交通模式识别并与交通和驾驶模式相关联提供高度的时间和空间分辨率。 所提出的装置（1）提供基于网格的（2121,2122,2123,2124）技术上的自动化交通和驾驶模式识别的自动化以及与使用基于环境的因素（高程，道路网络）相关的运动事故的风险预测 ，交通数据，天气状况（包括社会经济因素）），这些因素会影响机动车交通量，并且依赖于合适的测量设备（41，...，45）的位置。 通过这种方式，可以提供对任意区域事故风险的预测。 测量装置（1）通过比较区域或道路段的特征与在那里测量或记录的事故的数量和类型进行比较，将特征和事故数据关联起来进行校准，例如， 使用所公开的机器学习技术。

公开(公告)号：WO2018082784A1

公开(公告)日：2018-05-11

申请号：PCT/EP2016/076783

申请日：2016-11-07

Applicant: SWISS REINSURANCE COMPANY LTD.

Inventor： CHATZIPRODROMOU, Iordanis ,  NAGEL, Uwe ,  VENKATESWARAN, Ramya ,  LARKIN, Peter ,  ELSASSER, Christian

IPC: G06Q40/08

Abstract: Proposed are a system (1) and a method for the determination and forecast of absolute and relative risks for car accidents based on exclusively non-insurance related measuring data and based on automated traffic pattern recognition, wherein data records of accident events are generated and location-dependent probability values for specific accident conditions associated with the risk of car accident are determined. Thus, the proposed system (1) provides a grid-based (2121, 2122, 2123, 2124), technically new way of automation of risk-prediction related to motor accidents using environment based factors (elevation, road network, traffic data, weather conditions) including socio-economic factors that are impacting motor traffic and are location dependent received from appropriate measuring devices and systems (41,...,45). In this way, predictions of the accident risk for arbitrary areas can be provided. The system is calibrated by comparing features of areas or road segments with the number and type of accidents that have measured or registered there, linking the features and accident data e.g. using the below discussed machine learning techniques.

Abstract translation: 提出了基于非保险相关测量数据并基于自动化交通模式识别来确定和预测车辆事故的绝对和相对风险的系统（1）和方法，其中数据 确定事故事件记录并确定与车祸风险相关的特定事故状况的位置相关概率值。 因此，所提出的系统（1）使用基于环境的因素（海拔，道路网络，交通数据，天气）提供基于网格的（2121,2122,2123,2124）技术上的与运动事故相关的风险预测自动化的新方法 条件），包括影响汽车交通的社会经济因素，并且从适当的测量设备和系统（41，...，45）接收位置。 通过这种方式，可以提供对任意区域事故风险的预测。 该系统通过比较区域或路段的特征与在那里测量或登记的事故的数量和类型，将特征和事故数据关联起来进行校准，例如， 使用下面讨论的机器学习技术。