A method for operating a driver assistance function to support a lateral control of a vehicle is provided. A permissible range for a steering torque component which the driver assistance function is able to exert on the steering of the vehicle is predefined. The permissible range is specified by upper and lower limits. The upper and lower limits of the permissible range be adapted as a function of a current vehicle state. The vehicle state is given relative to a lane center, for example, by the position, the velocity, the acceleration and the sudden motion, by the respective component of this variable in the lateral direction. An adjustable range of the driver feedback is determined based on the vehicle state and the lateral acceleration. From this and the consideration of a disturbance compensation, the permissible range of the steering torque component of the assistance function is ascertained.

A method for operating a driver assistance function to support a lateral control of a vehicle is provided. A permissible range for a steering torque component which the driver assistance function is able to exert on the steering of the vehicle is predefined. The permissible range is specified by upper and lower limits. The upper and lower limits of the permissible range be adapted as a function of a current vehicle state. The vehicle state is given relative to a lane center, for example, by the position, the velocity, the acceleration and the sudden motion, by the respective component of this variable in the lateral direction. An adjustable range of the driver feedback is determined based on the vehicle state and the lateral acceleration. From this and the consideration of a disturbance compensation, the permissible range of the steering torque component of the assistance function is ascertained.

A virtual sound provision apparatus for an electric vehicle includes a driving information detector configured to detect vehicle driving information for outputting a virtual driving sound, a microphone configured to detect an actual driving sound generated from the electric vehicle, a controller configured to determine a characteristic of a target sound based on an acceleration and deceleration driving pattern of a driver from an accelerator pedal input value and a brake pedal input value of the driver, and generating and outputting a sound control signal for outputting the virtual driving sound having the characteristic of the target sound based on the determined characteristic information of the target sound and characteristic information of the actual driving sound, and a sound device configured to output a virtual engine sound that simulates an engine sound from the electric vehicle at the time of acceleration and deceleration according to the sound control signal.

A virtual sound provision apparatus for an electric vehicle includes a driving information detector configured to detect vehicle driving information for outputting a virtual driving sound, a microphone configured to detect an actual driving sound generated from the electric vehicle, a controller configured to determine a characteristic of a target sound based on an acceleration and deceleration driving pattern of a driver from an accelerator pedal input value and a brake pedal input value of the driver, and generating and outputting a sound control signal for outputting the virtual driving sound having the characteristic of the target sound based on the determined characteristic information of the target sound and characteristic information of the actual driving sound, and a sound device configured to output a virtual engine sound that simulates an engine sound from the electric vehicle at the time of acceleration and deceleration according to the sound control signal.

A method of driver behavior that includes generating, by a mobile device located inside a vehicle, telematics data associated with operation of the vehicle, and transmitting, from the mobile device to one or more processors via wireless communication, the telematics data in real-time. The method also includes analyzing the telematics data to determine whether a driving score for a driver, determining whether the driving score is greater than a threshold value, determining a discount based upon the driving score being higher than the threshold, and generating a communication based upon the discount. The method further includes transmitting the communication.

A method of driver behavior that includes generating, by a mobile device located inside a vehicle, telematics data associated with operation of the vehicle, and transmitting, from the mobile device to one or more processors via wireless communication, the telematics data in real-time. The method also includes analyzing the telematics data to determine whether a driving score for a driver, determining whether the driving score is greater than a threshold value, determining a discount based upon the driving score being higher than the threshold, and generating a communication based upon the discount. The method further includes transmitting the communication.

An apparatus includes processing circuitry configured to determine an actual driving performance of a driver in a manual driving mode and a projected driving performance if the vehicle had been operated in an autonomous driving mode, compare the driving performance in the manual driving mode and the autonomous driving mode, and transmit a feedback to the driver based on the comparison. The processing circuitry can be further configured to determine a driver state of the driver in the manual driving mode, determine environmental driving condition of the vehicle, and establish a baseline behavior of the driver as a function of the driver state and the environmental driving condition.

An apparatus includes processing circuitry configured to determine an actual driving performance of a driver in a manual driving mode and a projected driving performance if the vehicle had been operated in an autonomous driving mode, compare the driving performance in the manual driving mode and the autonomous driving mode, and transmit a feedback to the driver based on the comparison. The processing circuitry can be further configured to determine a driver state of the driver in the manual driving mode, determine environmental driving condition of the vehicle, and establish a baseline behavior of the driver as a function of the driver state and the environmental driving condition.

A system and a method for the determination and forecast of absolute and relative risks for car accidents based on exclusively non-insurance related measuring data and based on automated traffic pattern recognition, wherein data records of accident events are generated and location-dependent probability values for specific accident conditions associated with the risk of car accident are determined. The proposed system provides a grid-based, technically new way of automation of risk-prediction related to motor accidents using environment based factors including socio-economic factors that are impacting motor traffic and are location dependent received from appropriate measuring devices and systems. In this way, predictions of the accident risk for arbitrary areas can be provided. The system is calibrated by comparing features of areas or road segments with the number and type of accidents that have measured or registered there, linking the features and accident data e.g. using the below discussed machine learning techniques.

A system and a method for the determination and forecast of absolute and relative risks for car accidents based on exclusively non-insurance related measuring data and based on automated traffic pattern recognition, wherein data records of accident events are generated and location-dependent probability values for specific accident conditions associated with the risk of car accident are determined. The proposed system provides a grid-based, technically new way of automation of risk-prediction related to motor accidents using environment based factors including socio-economic factors that are impacting motor traffic and are location dependent received from appropriate measuring devices and systems. In this way, predictions of the accident risk for arbitrary areas can be provided. The system is calibrated by comparing features of areas or road segments with the number and type of accidents that have measured or registered there, linking the features and accident data e.g. using the below discussed machine learning techniques.