Evaluating operator reliance on vehicle alerts

    公开(公告)号:US11842300B1

    公开(公告)日:2023-12-12

    申请号:US16992198

    申请日:2020-08-13

    IPC分类号: G06Q40/08

    CPC分类号: G06Q40/08

    摘要: A system and computer-implemented method detect and act upon operator reliance to vehicle alerts. The system and method include receiving user profile data of an operator that includes a baseline of at least one driving activity aided by activation of an alert from a feature of an Advanced Driver Assistance System (ADAS). The system and method may include receiving historical ADAS alert frequency data including a history of at least one driving activity aided by activation of the alert from the ADAS feature. The system and method may compare the user profile data with the historical ADAS alert frequency data, determine a reliance level based upon the comparing, and set at least a portion of an operator profile associated with the operator with the reliance level. As a result, a risk averse driver, and/or proper responsiveness to vehicle alerts may be rewarded with insurance-cost savings, such as increased discounts.

    AUTOMATICALLY TRACKING DRIVING ACTIVITY

    公开(公告)号:US20210053579A1

    公开(公告)日:2021-02-25

    申请号:US17092589

    申请日:2020-11-09

    摘要: A system and computer-implemented method detect and act upon deactivated vehicle components. The system and method include receiving measurements data associated with driving activity. The measurements data includes an indication that at least one feature of an Advanced Driver Assistance System (ADAS) of a vehicle has been deactivated for a driving activity. The system and method may include receiving historical driving data including a history of at least one driving activity aided by activation of the alert from the ADAS feature. The system and method may compare the measurements data to the historical driving data, determine a likelihood level that the feature of the ADAS would have provided the alert had the feature been activated based upon the comparing, and set, based at least upon the determining, at least a portion of an operator profile associated with an operator of the vehicle with the likelihood level.

    AGGREGATION AND CORRELATION OF DATA FOR LIFE MANAGEMENT PURPOSES
    18.
    发明申请
    AGGREGATION AND CORRELATION OF DATA FOR LIFE MANAGEMENT PURPOSES 审中-公开
    生活管理目的数据的聚合和关联

    公开(公告)号:US20150286929A1

    公开(公告)日:2015-10-08

    申请号:US14245839

    申请日:2014-04-04

    IPC分类号: G06N5/02

    CPC分类号: G06Q30/0631 G06Q30/0207

    摘要: A method and computer system aggregates and correlates various types of data from an individual for life management purposes. As an example, the various types of data may include home data, vehicle data, and personal health data associated with the individual. The method and system may receive the various types of data from the individual and analyze each type of data to determine patterns associated with each type of data. The method and system may then compare each of the determined patterns with one another to determine any underlying factors and correlations that may be affecting each of the determined patterns. Based on the determined underlying factors and correlations for each of the determined patterns, the method and system may provide the individual with various benefits such as personalized recommendations, insurance discounts, and other added values or services that the individual can use to better manage and improve his or her life.

    摘要翻译: 方法和计算机系统聚合并关联来自个人的各种类型的数据以用于生命管理目的。 作为示例,各种类型的数据可以包括与个人相关联的家庭数据,车辆数据和个人健康数据。 方法和系统可以从个体接收各种类型的数据,并分析每种类型的数据以确定与每种类型的数据相关联的模式。 然后,方法和系统可以将所确定的模式中的每一个彼此进行比较,以确定可能影响每个确定模式的任何潜在因素和相关性。 根据确定的每个确定模式的基本因素和相关性,该方法和系统可以向个人提供各种益处,例如个性化建议,保险折扣以及个人可用于更好地管理和改进的其他附加价值或服务 他或她的生命。

    ANALYSIS OF CUSTOMER DRIVER DATA
    19.
    发明公开

    公开(公告)号:US20240362686A1

    公开(公告)日:2024-10-31

    申请号:US18238779

    申请日:2023-08-28

    IPC分类号: G06Q30/0282 G06Q30/0217

    CPC分类号: G06Q30/0282 G06Q30/0217

    摘要: A computer-implemented method for providing feedback on driving behavior of a driver based upon driving behavior data associated with the driver may include, by one or more processors, (i) receiving driving behavior data associated with the driver; (ii) inputting the driving behavior data associated with the driver into a generative AI model to generate feedback about the driving behavior of a driver, wherein the generative AI model is trained using historical driving behavior to identify behavioral patterns in driving and configured to (a) correlate driving behavioral patterns with suggestions to improve driving, (b) analyze input driving behavior data associated with the driver to determine suggestions to improve the driving behavior of the driver, and (c) generate feedback regarding the driving behavior associated with the driver; and (iii) presenting, by the one or more processors, the feedback to the driver.