At least a method for determining risk behavior of a driver is described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.

Systems and methods for distracted driving detection are described. A method includes receiving proximate vehicle data about a proximate vehicle proximate to the host vehicle. The method also includes estimating one or more baselines for a predetermined future time for the proximate vehicle from the proximate vehicle data. The method further includes comparing current kinematic data of the proximate vehicle data for the predetermined future time to the one or more baselines. The method includes generating distraction flags associated with the proximate vehicle based on the comparison. The method also includes controlling one or more vehicle systems of the host vehicle based on the generated distraction flags.

Systems and methods for distracted driving detection are described. A method includes receiving proximate vehicle data about a proximate vehicle proximate to the host vehicle. The method also includes estimating one or more baselines for a predetermined future time for the proximate vehicle from the proximate vehicle data. The method further includes comparing current kinematic data of the proximate vehicle data for the predetermined future time to the one or more baselines. The method includes generating distraction flags associated with the proximate vehicle based on the comparison. The method also includes controlling one or more vehicle systems of the host vehicle based on the generated distraction flags.

An agent apparatus includes an agent, an impact detection processor, an occupant information acquisition unit, a vehicle information acquisition unit, a surrounding-environment information acquisition unit, and an agent control unit. The agent is disposed visually recognizable from an outside of a vehicle. The impact detection processor detects an impact on the vehicle. The occupant information acquisition unit acquires, if the impact is detected, occupant information regarding a state of an occupant of the vehicle. The vehicle information acquisition unit acquires, if the impact is detected, vehicle information regarding a state of the vehicle. The surrounding-environment information acquisition unit acquires surrounding-environment information regarding a surrounding environment of the vehicle. The agent control unit is configured to cause the agent to operate on the basis of any of the occupant information, the vehicle information, and the surrounding-environment information and thereby send information toward a surrounding region of the vehicle.

An agent apparatus includes an agent, an impact detection processor, an occupant information acquisition unit, a vehicle information acquisition unit, a surrounding-environment information acquisition unit, and an agent control unit. The agent is disposed visually recognizable from an outside of a vehicle. The impact detection processor detects an impact on the vehicle. The occupant information acquisition unit acquires, if the impact is detected, occupant information regarding a state of an occupant of the vehicle. The vehicle information acquisition unit acquires, if the impact is detected, vehicle information regarding a state of the vehicle. The surrounding-environment information acquisition unit acquires surrounding-environment information regarding a surrounding environment of the vehicle. The agent control unit is configured to cause the agent to operate on the basis of any of the occupant information, the vehicle information, and the surrounding-environment information and thereby send information toward a surrounding region of the vehicle.

Systems and methods are provided for predictive assessment of driver perception abilities based on driving behavior personalized to the driver in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with on embodiment, a method comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.

Systems and methods are provided for predictive assessment of driver perception abilities based on driving behavior personalized to the driver in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with on embodiment, a method comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.

Described herein are techniques for optimizing operation of a fleet of electric vehicles. In some embodiments, a fleet management platform may maintain, in relation to a plurality of drivers, driving behavior patterns determined to be associated with the each of the plurality of drivers. Upon receiving a request for optimization of at least one operation related to a fleet of electric vehicles, such techniques may comprise determining one or more factors associated with the optimization of the at least one operation, identifying a set of driving behavior patterns correlated to the one or more factors, and customizing the at least one operation based on the identified set of behavior patterns and the driving behavior patterns.

Described herein are techniques for optimizing operation of a fleet of electric vehicles. In some embodiments, a fleet management platform may maintain, in relation to a plurality of drivers, driving behavior patterns determined to be associated with the each of the plurality of drivers. Upon receiving a request for optimization of at least one operation related to a fleet of electric vehicles, such techniques may comprise determining one or more factors associated with the optimization of the at least one operation, identifying a set of driving behavior patterns correlated to the one or more factors, and customizing the at least one operation based on the identified set of behavior patterns and the driving behavior patterns.