A server device can obtain historical location data, concerning a vehicle, captured by a global positioning system (GPS) device of the vehicle and historical engine control unit (ECU) data concerning the vehicle captured by an ECU of the vehicle. The server device can process the historical location data and the historical ECU data to train a machine learning model to determine a relationship between the historical location data and the historical ECU data. The server device can receive location data and ECU data concerning the vehicle and update the machine learning model based on the location data and the ECU data. The server device can receive real-time location data concerning the vehicle and derive an equivalent real-time ECU speed using the machine learning model. The server device can generate a message regarding the equivalent real-time ECU speed of the vehicle and send the message to a remote device for display.

A motor vehicle driving style estimating system comprises: a measuring apparatus of a motion quantity of the motor vehicle; a processing apparatus connected to the measuring apparatus and configured to: define a value of a space corresponding to a measuring path; calculate, based on the motion quantity, an estimate of an energy consumed by the motor vehicle along the measuring path; calculate a reference energy associated to a reference behavior of the motor vehicle along the measuring path; and calculate an indication of the driving style dependent on: a difference between the consumed energy and reference energy, and on the space value.

A driving support method and apparatus are disclosed. The driving support method according to an example embodiment includes collecting personalized vehicle data, predicting a danger related to driving based on the personalized vehicle data, and supporting a driver of a vehicle based on the predicted danger.

A method for measuring an engine load of a drive motor in a vehicle. Here, at least one parameter which characterizes the engine load is allocated a driving distance equivalent. This driving distance equivalent is then incremented by a driving distance counter. If the vehicle is equipped with a sailing functionality, the driven distance is counted only if the vehicle is moving and the drive train is coupled to the drive motor without slip. A rate of rotation sensor, a clutch sensor and a driving distance counter able to be switched on and off are required for running the method of the present invention. A computer program is used in addition.

An automated method of controlling a speed of a vehicle includes identifying parameters of a driving instance of the vehicle; identifying a predetermined profile that is applicable to the driving instance based on the identified parameters; identifying a predetermined speed policy applicable to the driving instance based on the identified profile; and implementing the identified speed policy during the driving instance. The method may be repeated during the driving instance, whereby the speed policy that is implemented is automatically updated when one or more changes in the identified parameters cause a different predetermined speed policy to be identified. Parameter may include driver parameters (e.g., driver age and driver experience); vehicle parameters (e.g., vehicle age, mileage, and tire wear) tire maintenance information); behavior parameters (e.g., speed, acceleration, hard braking of the vehicle, following distance, swerving, and cornering); and circumstance parameters (e.g., time of day, road information, inclement weather, and traffic congestion).

A method for operating a vehicle that includes an internal combustion engine that may be automatically stopped and started is described. In one example, selection of an engine starting device is based on a value of an engine starting torque reserve. The engine starting torque reserve may be dynamically adjusted so that life spans of engine starting devices may meet expectations.

An embodiment vehicle includes a navigation device, a communication device, a processor, a non-transitory memory storing software that, when executed by the processor, causes the processor to set a reference traffic light and a front traffic light based on a current location, receive first operation information of the reference traffic light and second operation information of the front traffic light from a server, predict an operation of the front traffic light at a time point at which the vehicle is to pass through the front traffic light based on a vehicle speed, the first operation information, and the second operation information, and control driving according to the predicted operation.

A traveling control apparatus is mounted on a vehicle that includes an electric motor and an internal combustion engine as power sources. The traveling control apparatus includes an electronic control unit configured to create a speed profile obtained by predicting speed of the vehicle at each time, derive, based on at least the speed profile, a coefficient profile that is a coefficient at each time used at the time of predicting an amount of regenerative energy recoverable by regenerative braking of the electric motor, approximate the speed profile with a predetermined approximation model and estimate a predicted amount of regenerative energy based on an approximation result and the coefficient profile, and determine the power source used for traveling based on the predicted amount of regenerative energy.

An embodiment autonomous driving control apparatus includes a memory storing data and algorithms and a processor configured to execute the algorithms stored in the memory to estimate an end of an operational design domain of an autonomous driving function in advance and to calculate a distance from an end point of the operational design domain to a start point of a transition demand, wherein the transition demand is a point at which a transition demand section starts based on a current speed of a vehicle depending on a driving strategy of the transition demand section for requiring a driver to drive the vehicle and a minimum risk maneuver section.

A server device can obtain historical location data, concerning a vehicle, captured by a global positioning system (GPS) device of the vehicle and historical engine control unit (ECU) data concerning the vehicle captured by an ECU of the vehicle. The server device can process the historical location data and the historical ECU data to train a machine learning model to determine a relationship between the historical location data and the historical ECU data. The server device can receive location data and ECU data concerning the vehicle and update the machine learning model based on the location data and the ECU data. The server device can receive real-time location data concerning the vehicle and derive an equivalent real-time ECU speed using the machine learning model. The server device can generate a message regarding the equivalent real-time ECU speed of the vehicle and send the message to a remote device for display.