The present teaching relates to method, system, and medium, for operating a vehicle. The method includes the steps of receiving Real-time data related to the vehicle are received. A current mode of operation of the vehicle is determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data in accordance with a risk model. If the first risk satisfies a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined. The vehicle is switched from the current mode to the different mode if the second risk satisfies a second criterion.

A mileage prediction and optimization system (FIG. 1) is disclosed for optimizing the mileage of a vehicle. The mileage prediction and optimization system comprises a prediction and optimization module (300) adapted to determine variation in mileage of the vehicle at least based on the current fuel volume, current speed, current fuel density, the current tire air pressure, the current tire air temperature, current fetched values from an ECU module (700) and the pre-defined mileage data of the vehicle. Further, the prediction and optimization module (300) is adapted to optimize the mileage of the vehicle by reducing variation of fuel density, reducing variation of tire air pressure and informing optimal gear-speed combinations to a user regardless of whether the vehicle is stationary or in motion.

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.

Disclosed are methods, apparatuses, and computer implemented methods to improve the reliability and safety of the autonomous vehicles. In one aspect, a method for determining an environmental exposure of an autonomous vehicle is disclosed. The method includes obtaining sensor data from one or more shock or vibration sensors mounted to the autonomous vehicle and determining a level of vibrations or a level of shock at the autonomous vehicle based on the obtained sensor data. The method further includes adding the level of vibrations of the autonomous vehicle to an accumulated level of vibrations of the autonomous vehicle and determining whether the accumulated level of vibrations of the autonomous vehicle exceeds a predetermined threshold value for the accumulated level of vibrations of the autonomous vehicle.

The disclosed technology provides solutions for facilitating the selection of a parking space by a user of a parking application. A process of the disclosed technology can include steps for monitoring sensor data and location data associated with a user device, retrieving, based on the location data associated with a user device, listing data associated with one or more parking spaces in a vicinity of the user device, and capturing image data that includes at least a portion of the one or more parking spaces. In some aspects, the process may further include steps for overlaying one or more graphical objects onto the image data, wherein the one or more graphical objects are based on the listing data associated with the one or more parking spaces. Systems and machine-readable media are also provided.

The disclosed technology provides solutions for facilitating the selection of a parking space by a user of a parking application. A process of the disclosed technology can include steps for monitoring sensor data and location data associated with a user device, retrieving, based on the location data associated with a user device, listing data associated with one or more parking spaces in a vicinity of the user device, and capturing image data that includes at least a portion of the one or more parking spaces. In some aspects, the process may further include steps for overlaying one or more graphical objects onto the image data, wherein the one or more graphical objects are based on the listing data associated with the one or more parking spaces. Systems and machine-readable media are also provided.

A feedback server includes a 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 method of controlling a vehicle having wheels provided with tires resting on a surface, the method using a model of the physical behavior of each tire as a function of a sideslip angle (β.sub.ij) for each tire relative to the surface. The model is obtained by implementing an adaptive algorithm that selectively applies an affABREGEine model (Z1), a DUGOFF model (Z2), or a constant model (Z3).

A trip energy estimation system includes a memory and a control module. The memory stores average traffic speed, average traffic acceleration, driver speed, and driver acceleration data. The control module executes an algorithm to estimate an amount of energy for an electric vehicle to travel between two locations. The algorithm includes: determining, based on the average traffic speed data and the average traffic acceleration data, a baseline amount of energy for the electric vehicle to travel between the two locations; determining, based on the driver speed data and the driver acceleration data, a dynamic amount of energy corresponding to at least one of stop and go events, over speeding, or under speeding; and determining a total amount of energy based on the baseline amount of energy and the dynamic amount of energy. The control module, based on the total amount of energy, performs an operation including indicating a trip estimate.