Abstract: A fleet management server comprising a memory a processor, coupled to the memory, wherein the processor includes a program stored on computer readable medium configured to receive new vehicle event data, including new pre-post event (PPE) data, representing a new vehicle event that occurred when a vehicle condition threshold was exceeded while a driver was driving a vehicle; automatically predict and assign at least one annotative classification, selected among an array of pre-defined annotative classifications, to the new vehicle event based on a previous manually assigned annotative classification, based on both past image data and past PPE data, and the new PPE data, and output, to a user display, a representation of the new event data based on the automatically predicted and assigned annotative classification for the new vehicle event to reduce the user's time spent reviewing the new event data.

Abstract: A system and method controls a vehicle based on driver attention to an operation of the vehicle by receiving a first signal representative of a head pose condition of the driver during a first time period, receiving a second signal representative of a physical characteristic of the vehicle operated during the first time period, determining the driver attention to the operation of the vehicle based on the first and second signals, and generating a signal that adjusts the functional aspect of the associated vehicle, wherein the generated signal causes the associated vehicle to adjust the functional aspect of the vehicle based on the generated signal. The adjusting the functional aspect of the vehicle includes one or more of adjusting a content, timing, format, and/or style of a warning signal generated by the vehicle, and/or adjusting a parameter of one or more driver assistance systems of the vehicle.

Abstract: System and methods provide driver performance rating and driving coaching feedback and make incident and exceedance predictions. Driving event data including data spanning multiple separate driving trips is analyzed using a trend detection model to generate a trend detection result that is used to determine a driver performance rating. The trend detection model may be a functional regression model, a linear fit model and/or a polynomial fit model. Driver coaching signals representative of driving instructions are generated based results of the trend detection model applied to the driving event data. Driving incident predictions are made based on the results of the trend detection model applied to the driving event data to a predetermined threshold.

Abstract: A fleet management server comprising a memory a processor, coupled to the memory, wherein the processor includes a program stored on computer readable medium configured to receive new vehicle event data, including new pre-post event (PPE) data, representing a new vehicle event that occurred when a vehicle condition threshold was exceeded while a driver was driving a vehicle; automatically predict and assign at least one annotative classification, selected among an array of pre-defined annotative classifications, to the new vehicle event based on a previous manually assigned annotative classification, based on both past image data and past PPE data, and the new PPE data, and output, to a user display, a representation of the new event data based on the automatically predicted and assigned annotative classification for the new vehicle event to reduce the user's time spent reviewing the new event data.

Abstract: Some vehicles can be equipped with a safety system that provides audible and/or visual alerts in response to excessive speed or other safety violations. The system can also generate a report of a safety violation. In some situations, the alerts and/or reports that are generated are false alarms. In other situations, the condition that triggered a true alarm can be prevented if advance warning is given to the driver. In the embodiments herein, a system and method for geolocated in-vehicle false alarm filtering and proactive true alarm early warning are provided that can address these issues.

Abstract: A system and method for monitoring a driver's operation of a vehicle and automatically providing a lesson to a driver based on the driver's operation are provided. Input data generated by an onboard vehicle computing system specifying actions taken by the driver while operating the vehicle are received. Based on the input data, a determination is made whether a driving event has occurred that requires the lesson to be output to the driver. The driving event is analyzed based on threshold data stored in the memory, and based on the results of the analysis of the driving event one or more lessons are automatically output to the driver. Additionally, the driver's completion of the lesson can be monitored, and subsequent instances of the driver's operation of the vehicle may include controlling movement of the vehicle based on the results of the analysis of the driving event.

Abstract: A fleet management server includes a memory and a processor. The processor is configured to: receive past event data associated with at least one vehicle and at least one driver, the past event data representing at least one respective past vehicle event that occurred when one of the drivers was driving one of the vehicles; receive respective predetermined classifications of the past vehicle events that were previously manually assigned based on respective contemporaneous videos of the past vehicle events; receive novel event data representing at least one respective novel vehicle event; automatically assign respective ones of the predetermined classifications to the novel vehicle events based on the previous manually assigned classifications to the past vehicle events; and output, to a user of the fleet management server, the respective automatically assigned predetermined classifications of the novel vehicle events.

Abstract: A fleet management server is configured to receive, via a wireless transceiver, driver and vehicle information from a plurality of vehicles relating to a plurality of drivers. The server computes, based on the received driver and vehicle information, occurrence rates for predetermined vehicle events and predetermined vehicle error codes that are associated with possible vehicle tampering, and then compares, based on the received driver and vehicle information, an occurrence rate for the predetermined vehicle events and predetermined vehicle error codes of a first driver to occurrence rates for the predetermined vehicle events and predetermined vehicle error codes of one or more of the other plurality of drivers.

Abstract: A system detects tampering of an electronic system of a vehicle operated by a driver. The system receives historical occurrences of at least one diagnostic trouble code (DTC) generated by the onboard vehicle computing system based on sensor data received from a vehicle sensor during a trip. The system identifies a length of the trip and a speed of the vehicle when each DTC was generated. The system identifies a distance the vehicle had traveled when each DTC was generated. The system determines a subsequent trip was started, whether a driver operating the vehicle on the subsequent trip is a same driver or a new driver, whether DTCs were generated during the trip, and whether DTCs were generated during the subsequent trip. The system determines a tamper rating for the driver that indicates a likelihood that the driver has tampered with the vehicle.

Abstract: A system and method for monitoring a driver's operation of a vehicle and automatically providing a lesson to a driver based on the driver's operation are provided. Input data generated by an onboard vehicle computing system specifying actions taken by the driver while operating the vehicle are received. Based on the input data, a determination is made whether a driving event has occurred that requires the lesson to be output to the driver. The driving event is analyzed based on threshold data stored in the memory, and based on the results of the analysis of the driving event one or more lessons are automatically output to the driver. Additionally, the driver's completion of the lesson can be monitored, and subsequent instances of the driver's operation of the vehicle may include controlling movement of the vehicle based on the results of the analysis of the driving event.

Abstract: A system for monitoring driver performance includes a server system that generates a performance model based on driver performance related data received from a plurality of vehicles. The performance model mathematically characterizes the crowd wisdom for a driving behavior under a set of driving circumstances. The system also includes the plurality of vehicles, each having an on-vehicle system that compares driver performance related data characterizing the driving behavior performed by the driver of the vehicle under the set of driving circumstances to the performance model so as to determine a deviation score therebetween. The deviation scores for the driving behavior are accumulated over a predetermined period of time, and a divergence indicator is triggered in response to an accumulated deviation score value exceeding one or more predetermined thresholds.

Abstract: A system detects tampering of an electronic system of a vehicle operated by a driver. The system receives historical occurrences of at least one diagnostic trouble code (DTC) generated by the onboard vehicle computing system based on sensor data received from a vehicle sensor during a trip. The system identifies a length of the trip and a speed of the vehicle when each DTC was generated. The system identifies a distance the vehicle had traveled when each DTC was generated. The system determines a subsequent trip was started, whether a driver operating the vehicle on the subsequent trip is a same driver or a new driver, whether DTCs were generated during the trip, and whether DTCs were generated during the subsequent trip. The system determines a tamper rating for the driver that indicates a likelihood that the driver has tampered with the vehicle.

Abstract: A method for transmitting event data from an event detection and reporting system in a vehicle to a backend server includes storing full resolution sensor data in a vehicle buffer memory and subsampling the full resolution sensor data to produce reduced resolution of the sensor data. A vehicle event is detected based on an analysis of the reduced resolution sensor data, and the vehicle event is classified based on the reduced resolution sensor data. A determination is made as to whether the vehicle event is classified as a particular type of event with a minimum degree of confidence. The method then includes transmitting an indication that the vehicle event has not been classified with the minimum degree of confidence when the vehicle event has not been classified with the minimum degree of confidence, and transmitting reduced resolution sensor data for the detected vehicle event when the vehicle event is classified with at least the minimum degree of confidence.

Abstract: A fleet management server includes a memory and a processor. The processor is configured to: receive past event data associated with at least one vehicle and at least one driver, the past event data representing at least one respective past vehicle event that occurred when one of the drivers was driving one of the vehicles; receive respective predetermined classifications of the past vehicle events that were previously manually assigned based on respective contemporaneous videos of the past vehicle events; receive novel event data representing at least one respective novel vehicle event; automatically assign respective ones of the predetermined classifications to the novel vehicle events based on the previous manually assigned classifications to the past vehicle events; and output, to a user of the fleet management server, the respective automatically assigned predetermined classifications of the novel vehicle events.

Abstract: A method for transmitting event data from an event detection and reporting system in a vehicle to a backend server includes storing full resolution sensor data in a vehicle buffer memory and subsampling the full resolution sensor data to produce reduced resolution of the sensor data. A vehicle event is detected based on an analysis of the reduced resolution sensor data, and the vehicle event is classified based on the reduced resolution sensor data. A determination is made as to whether the vehicle event is classified as a particular type of event with a minimum degree of confidence. The method then includes transmitting an indication that the vehicle event has not been classified with the minimum degree of confidence when the vehicle event has not been classified with the minimum degree of confidence, and transmitting reduced resolution sensor data for the detected vehicle event when the vehicle event is classified with at least the minimum degree of confidence.

Abstract: A fleet management server is configured to receive, via a wireless transceiver, driver and vehicle information from a plurality of vehicles relating to a plurality of drivers. The server computes, based on the received driver and vehicle information, occurrence rates for predetermined vehicle events and predetermined vehicle error codes that are associated with possible vehicle tampering, and then compares, based on the received driver and vehicle information, an occurrence rate for the predetermined vehicle events and predetermined vehicle error codes of a first driver to occurrence rates for the predetermined vehicle events and predetermined vehicle error codes of one or more of the other plurality of drivers.