Provided herein is a computer system for creating driving challenges for drivers. The computer system may include a processor in communication with a memory device, and the processor may be programmed to: (i) receive driving data associated with a driver, (ii) generate a first model that models the driving data associated with the driver, (iii) calculate a predicted driving score for the driver based at least in part upon the first model, (iv) generate a second model that predicts a confidence of the predicted driving score, (v) calculate a confidence value of the predicted driving score, wherein the confidence value is a squared error of the predicted driving score, and (vi) generate at least one driving challenge for the driver based at least in part upon the predicted driving score and the confidence value for that predicted driving score.

A monitoring and maintenance system that utilizes imperial and theoretical data to compare parts, vehicles, users, regions, wear intensity indexes over time and tracking information to provide a sophisticated data collection system for heavy-duty equipment or rental equipment. This tracking is designed to better the specifications, designs, training, preventative maintenance, and replacement wear understanding of fleet management.

A racing coach device stores a first path of travel along a racetrack over a first time period and a second path of travel along the racetrack over a second time period. The racing coach device identifies, for each of a plurality of geolocations along the racetrack, one of the first path of travel or the second path of travel that is associated with a shorter duration of time over which the user traversed a segment of the path of travel associated with each of the plurality of geolocations. The device determines an optimal path of travel along the racetrack based on the identified first and second path of travel for each segment of the path of travel at each of the plurality of geolocations that results in a calculated lap time to traverse the racetrack that is less than the first time period and the second time period.

A racing coach device stores a first path of travel along a racetrack over a first time period and a second path of travel along the racetrack over a second time period. The racing coach device identifies, for each of a plurality of geolocations along the racetrack, one of the first path of travel or the second path of travel that is associated with a shorter duration of time over which the user traversed a segment of the path of travel associated with each of the plurality of geolocations. The device determines an optimal path of travel along the racetrack based on the identified first and second path of travel for each segment of the path of travel at each of the plurality of geolocations that results in a calculated lap time to traverse the racetrack that is less than the first time period and the second time period.

Embodiments described herein are directed to providing contextually relevant cues to users and to providing cues based on predicted conditions. In one scenario, a computer system identifies a task that is to be performed by a user. The computer system accesses data structures to identify current conditions related to the identified task. The computer system then generates, based on the identified current conditions related to the task, contextually relevant cues for the task. The contextually relevant cues provide suggestive information associated with the task. The computer system further provides the generated cue to the user. In other scenarios, the computer system identifies anticipated conditions related to the task using accessed historical information, and generates contextually relevant cues based on the identified anticipated conditions.

Methods, computer-readable media, software, and apparatuses provide a tool for use by drivers and/or coaches throughout the pre-license stage of obtaining a driver's license. A pre-license program may control a computing device to collect drive data while a driver is driving a vehicle. This drive data may be used to detect a drive event. Then, the computing device may present coaching information associated with the detected drive event. The coaching information may provide a passenger, such as a coach or parent, with real-time advice for instructing the driver how to improve his/her driving skills. Moreover, the drive data collected may be used to prepare reports providing feedback to the drivers and coaches. Further, the pre-license program may determine an expected track for a driver to follow to prepare for a driver's license test and may indicate whether the driver is on or off the track.

Various embodiments of the present disclosure are directed to test stands for generating test runs on the basis of a test drive using a vehicle along a driving route. In some embodiments, a test stand determines at least one idle operating time of an internal combustion engine and/or at least one overrun operating time of the internal combustion engine from a time curve of a vehicle speed and a time curve of a gas pedal position. The test stand may further set a specified idle control mode instead of the operating control mode during an idle operating time and/or a specified overrun control mode is set instead of the operating control mode during an overrun operating time in the test stand automation unit in order to carry out the test run.

Described is a system for tracking and displaying the position of a motor vehicle driven by a user with respect to a determined route and the position of said user with respect to said motor vehicle.

The system includes a first and a second data acquisition unit configured to instantaneously detect the position of the motor vehicle and of the user relative to the motor vehicle and to the determined route; a processing unit for generating an image of the position of the motor vehicle and of the user and a visual projection device.

The present disclosure is directed to improving safety of drivers that drive vehicles that belong to a fleet of vehicles. This may include identifying individual drivers that should receive a training or coaching session. Scores that associated vehicle type, vehicle weight class, time of day, number of stops/starts, and other factors could be evaluated to identify drivers that may have been assigned a highest risk factor. Company telematic data as well as public data that identify types of roadways, roadway conditions, experience levels, and other factors may be combined with numbers of accidents or speeding tickets when an analysis is performed. Methods described herein may be implemented according to a machine learning model based on a processor executing instructions out of a memory. This may include deep learning or the neural network technology.

An augmented reality system may include a data gathering device configured to collect data regarding an environment exterior to the vehicle. In addition, the system may include a vehicle controller including a device processor and a non-transitory computer readable medium including instructions, executable by the processor, for performing the following steps: receiving data from the data gathering device; and transmitting information to a set of augmented reality goggles in order to facilitate a projection of a modified view of an interior of the vehicle based on the data collected regarding the environment exterior to the vehicle.