An HV-ECU executes processing of steps. The steps include: a step of turning on a result display permission flag when an assist condition visible to a user is established; a step of calculating energy consumption in each travel section and total energy consumption when all the assist conditions are established and look-ahead information is updated; a step of generating a travel plan when the total energy consumption is greater than remaining energy; a step of executing switching control in accordance with the travel plan; and a step of outputting a result display when the result display permission flag is in an ON state when a vehicle reaches a destination.

This technology relates to dynamically detecting, managing and mitigating driver fatigue in autonomous systems. For instance, interactions of a driver in a vehicle may be monitored to determine a distance or time when primary tasks associated with operation of the vehicle or secondary tasks issued by the vehicle computing were last performed. If primary tasks or secondary tasks are not performed within given distance thresholds or time limits, then one or more secondary tasks are initiated by the computing device of the vehicle. In another instance, potential driver fatigue, driver distraction or overreliance on an automated driving system is detected based on gaze direction or pattern of a driver. For example, a detected gaze direction or pattern may be compared to an expected gaze direction or pattern given the surrounding environment in a vicinity of the vehicle

The present disclosure relates to a method and a device for safety driving. The method includes: acquiring riding data of a current user of a self-balancing vehicle; comparing the acquired riding data with riding data corresponding to a plurality of preset user levels; and determining a user level of the current user of the self-balancing vehicle according to a result of the comparing. The riding data includes one or more of the following data: a riding time, a riding distance, a shaking frequency, a shaking arc magnitude, and a shaking time.

A driver assistance system for a motor vehicle for automated driving with automated longitudinal guidance, wherein when automated longitudinal guidance is active in an automatic mode, automated longitudinal guidance is brought about taking into account a predefinable setpoint speed. The system includes a first detection unit, configured to detect a defined stationary state situation which is set on the basis of a preceding automated braking process of the motor vehicle to the stationary state, a second detection unit, configured to detect accelerator pedal activation, and an evaluation and control unit, configured to actuate a manual mode when actuator pedal activation is detected during a defined stationary state situation.

Embodiments of the present disclosure relate to the technical field of autonomous driving, and in particular to methods for updating an autonomous driving system, autonomous driving systems, and on-board apparatuses. In the embodiments of the present disclosure, the autonomous driving system, in a manual driving mode, senses the surrounding environment of a vehicle, performs vehicle positioning, and plans a path for autonomous driving for the vehicle. However, the autonomous driving system does not issue an instruction to control the driving of the vehicle. Instead, it compares the path with a path along which a driver drives the vehicle in the manual driving mode to update a planning and control algorithm of the autonomous driving system. As such, the updated autonomous driving system better caters to the driving habits of the driver and improves the driving experience for the driver without compromising the reliability of planning and decision-making of autonomous driving.

A first method of predicting the range of an electric vehicle comprises, determining a range value during a current vehicle operating cycle using a first range model, wherein the first range model is dependent on an energy consumption rate value recorded during a previous vehicle operating cycle. A second method of predicting the range of an electric vehicle comprises, monitoring a trailer detecting means of the vehicle; and determining a first range value if the trailer detecting means detects that a trailer is attached to the vehicle.

A system and method for localization of an autonomous vehicle based on 3D point cloud are disclosed. The system includes a processor and a memory coupled to the processor, the memory tangibly storing thereon executable instructions that, when executed by the processor, cause the processor to: receive an initial position of a vehicle; receive a data file representative of a three-dimensional (3D) point cloud from a LIDAR scanning subsystem of the vehicle; divide the 3D point cloud into a plurality of tiles and compute a plurality of key points for each tile; generate an estimated low frequency 2D position and yaw angle for the vehicle based on a particle filter process and a dynamically downloaded 2D reference map; and generate a position and orientation of the vehicle.

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 utilization index IDX is calculated based on one or more parameters (the number of times of charging/the number of trips, total battery charger connection time/total vehicle stop time, total EV traveling distance/total HV traveling distance, total EV traveling time/total HV traveling time, total EV traveling distance/total traveling distance, total EV traveling time/total traveling time, total charge amount/total fuel supply quantity) obtained based on a ratio of an EV traveling utilization level to an HV traveling utilization level or a total traveling utilization level, and the utilization index IDX is stored. Since the utilization index IDX is calculated such that as each parameter is larger, utilization of charging of a battery by a battery charger is performed more sufficiently. Accordingly, the utilization index IDX can be used as an index that offers more accurate determination regarding a utilization status of the battery charging.