Method and systems for displaying vehicle range and remaining energy to a driver is disclosed that includes a nominal range, based on a battery state of charge, and a dynamic range, based on the battery state of charge and a vehicle characteristic. The range indicator may be a linear battery icon that presents the nominal range as a first display element and the dynamic range as a second display element. The first display element may comprise a first bar length of a faded color and the second display element may comprise a second bar length of a full color.

Method and systems for displaying vehicle range and remaining energy to a driver is disclosed that includes a nominal range, based on a battery state of charge, and a dynamic range, based on the battery state of charge and a vehicle characteristic. The range indicator may be a linear battery icon that presents the nominal range as a first display element and the dynamic range as a second display element. The first display element may comprise a first bar length of a faded color and the second display element may comprise a second bar length of a full color.

The disclosure provides a system, a method, and a computer program product for determining coverage of a vehicle. The system is configured to, for example, acquire sensor data and vehicle data from a user equipment. The sensor data is associated with a region including a plurality of roads. Further, the system is configured to determine a function class of each of the plurality of roads based on the sensor data. Further, the system is configured to determine a type of a vehicle associated with the user equipment, based on the vehicle data. The system is configured to further determine coverage data of the vehicle for each of the plurality of roads based on the function class of a respective road of the plurality of roads and the type of the vehicle. Further, a first notification associated with the coverage of the vehicle, is generated as output.

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.

Systems and methods for determining trust across mobility platforms are provided. In one embodiment, a method includes receiving first mobility data for a first automation experience of a user with a first mobility platform. The method also includes receiving a swap indication for a second automation experience of the user with a second mobility platform after the first automation experience. The method further includes selectively assigning the first mobility platform to a first mobility category and the second mobility platform to a second mobility category different than the first mobility category. The method yet further includes calculating an estimated trust score for the second automation experience by applying a trust model based on the first mobility category, the second mobility category, and a sequence of the first automation experience and the second automation experience. The method includes modifying operation of the second mobility platform based on the estimated trust score.

The present disclosure relates adaptive cruise speed control assistant systems and methods thereof provide automatic and manual control of cruise speed and optimizes Range, Mileage, Fuel cost per km, Vehicular pollution, Fuel refill cost and travel time in different geographical areas depending on the vehicle characteristics, number of passengers, Tyre pressure, Air conditioning status, Use of vehicle & Load on vehicle.

A control method for hybrid electric vehicle, includes: acquiring vehicle data and environmental data of the hybrid electric vehicle under a current operating condition; determining, based on the vehicle data and the environmental data, optimal target state of charge corresponding to the current operating condition; and controlling discharging or charging of a power battery of the hybrid electric vehicle according to the optimal target state of charge.