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公开(公告)号：US20190325740A1
公开(公告)日：2019-10-24
申请号：US16401286
申请日：2019-05-02
摘要： 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.
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公开(公告)号：US11554776B2
公开(公告)日：2023-01-17
申请号：US16401286
申请日：2019-05-02
摘要： A system 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. The proposed system provides a grid-based, technically new way of automation of risk-prediction related to motor accidents using environment based factors including socio-economic factors that are impacting motor traffic and are location dependent received from appropriate measuring devices and systems. 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.
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公开(公告)号：US10850731B2
公开(公告)日：2020-12-01
申请号：US16347718
申请日：2017-11-07
IPC分类号： G06Q40/08 , B60W30/095 , B60W40/09 , G08G1/16 , G08G1/01
摘要： 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.
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