Abstract: The system assigns a plurality of drivers to one or more identified routes. An onboard computing system of a vehicle collects or generates a plurality of vehicle events, which are received at a server via a network. The onboard computing system includes a plurality of sensors and subsystems that are connected to monitor various vehicle components, such as braking, steering, and radar, as well as one or more cameras. A difficulty of each of the identified routes is characterized based on the plurality of vehicle events. A performance of each driver is characterized based on a subset of the plurality of vehicle events collected or received from the vehicle of each driver.

Abstract: A system for identifying a change in the operator of a vehicle includes a controller configured to determine, during operation of the vehicle by a current operator of the vehicle over a period of time, a plurality of values for at least two variables associated with the current operator. The controller correlates the values for the two variables to form a plurality of data points which together form a point cloud associated with the current operator. The controller performs a comparison of the point cloud to a point cloud stored in a memory and associated with a prior operator of the vehicle and determines, responsive to the comparison, whether the current operator of the vehicle is the prior operator of the vehicle.

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: Vehicle fleet management includes processing event-based data corresponding to detected events to generate event data sets that include: at least video corresponding to the event and data identifying an event-type for the event. One or more pedagogical event pairs are identified, each of which includes a first event data set reflecting a “good” response to an event-type and a second event data set reflecting a “bad” response to the event-type. At least one cluster of drivers having similar occurrence rates for the event-type is identified. One or more training lessons are generated for the cluster based on the event type, which training lessons include at least the video of the first event data set and the video of the second event data set. The training lessons are administered to a driver of the cluster via a computing device. The autonomous control of one or more vehicle systems is instituted based on the administered training lessons.

Abstract: A method of providing a driver of a vehicle with feedback for self-coaching, via a driver electronic device includes querying a server from access software on the driver electronic device, wherein the querying comprises querying the server with a query comprising a predefined driver; receiving, by the access software, driver coaching data associated with the predefined driver, the driver coaching data being based on an output from an active safety system on the vehicle; listing, by the access software, a user-selectable item representing the received driver coaching data via a display of the driver electronic device; and conveying the driver coaching data to the driver.

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: The system assigns a plurality of drivers to one or more identified routes. An onboard computing system of a vehicle collects or generates a plurality of vehicle events, which are received at a server via a network. The onboard computing system includes a plurality of sensors and subsystems that are connected to monitor various vehicle components, such as braking, steering, and radar, as well as one or more cameras. A difficulty of each of the identified routes is characterized based on the plurality of vehicle events. A performance of each driver is characterized based on a subset of the plurality of vehicle events collected or received from the vehicle of each driver.

Abstract: A system detects vehicle safety system issues present within a fleet vehicle. A plurality of diagnostic trouble codes (DTCs) collected or generated by an onboard computing system of the vehicle are received via a network. The system determines two or more of the DTCs occurred within a predetermined threshold. The two or more DTCs correspond to different sources within the vehicle. A common theme is determined between the plurality of DTCs having different sources. The two or more DTCs that occurred within the predetermined threshold are correlated with a vehicle safety system issue stored in an issue database. An alert is transmitted based on the correlated vehicle safety system issue to a recipient via the network.

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 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.