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.

An apparatus includes a front detection sensor detecting presence of a pedestrian on a driving lane of the vehicle, gaze information of the pedestrian, and a distance and a relative speed between the pedestrian and the vehicle; a vehicle sensor detecting at least one of a speed, an acceleration, a steering angle, a steering angular velocity, or a pressure of a master cylinder of the vehicle; an electronic control unit activating a function of a pedestrian detection and collision mitigation system based on information detected by the front detection sensor and the vehicle sensor; and a warning unit operated to inform a driver of a collision of the pedestrian with the vehicle by controlling the electronic control unit.

A method may include determining time-variable risk profiles for plural separate vehicle systems that are remotely controlled by operators that are located off-board the separate vehicle systems. The time-variable risk profiles represent one or more risks to travel of the separate vehicle systems. The method may include assigning the operators to remotely monitor or control the separate vehicle systems during the trips based on the time-variable risk profiles. The operator assigned changes with respect to time while the one or more separate vehicle systems is moving along one or more routes during the trip. A system may include one or more processors that may determine time-variable risk profiles for plural separate vehicle systems that are remotely controlled and assign the operators to remotely monitor or control the separate vehicle systems during the trips based on the time-variable risk profiles.

The disclosure generally pertains to systems and methods to classify a road based on a level of support offered by the road for autonomous driving operations. An example method may involve a computer receiving sensor data from a vehicle, the sensor data containing information about a current functional condition of a road. The computer may predict a future functional condition of the road by using a deterioration model to evaluate the sensor data. The computer may then determine a level of support offered by the road for autonomous driving operations based on the future functional condition of the road, and assign a classification to the road based on the level of support offered by the road for autonomous driving operations. The level of support offered by the road for autonomous driving operations may also be based on items such as road markings, traffic signs, traffic signals, and/or infrastructure elements.

Apparatuses and methods for evaluating the risk factors of a proposed vehicle maneuver using remote data are disclosed. In embodiments, a computer-assisted/autonomous driving vehicle communicates with one or more remote data sources to obtain remote sensor data, and process such remote sensor data to determine the risk of a proposed vehicle maneuver. A remote data source may be authenticated and validated, such as by correlation with other remote data sources and/or local sensor data. Correlation may include performing object recognition upon the remote data sources and local sensor data. Risk evaluation is performed on the validated data, and the results of the risk evaluation presented to a vehicle operator or to an autonomous vehicle navigation system.

A vehicle controller device including: a communication section configured to receive operation information to operate a vehicle from an operation device located externally to the vehicle; a first memory; and a first processor, the first processor being configured to: acquire peripheral information regarding a periphery of the vehicle from a peripheral information detection section; generate a travel plan for the vehicle based on the peripheral information of the vehicle; control autonomous driving in which the vehicle travels based on the generated travel plan and also control remote driving in which the vehicle travels based on the received operation information; predict that a compromised state in which autonomous driving of the vehicle becomes compromised will arise based on environmental information including meteorological information received by the communication section; and notify the operation device of the compromised state in a case in which the compromised state has been predicted to arise.

Embodiments described herein provide a method for using one or more audio signals from one or more sensors to establish the presence and severity of precipitation at a particular location. Methods may include: receiving at least one first audio signal from a first audio sensor of a vehicle; extracting acoustical features including frequency and amplitude from the at least one first audio signal; receiving at least one second audio signal from a second audio sensor of the vehicle; extracting acoustical features including frequency and amplitude from the at least one second audio signal; processing the frequency and amplitude from the at least one first audio signal and the frequency and amplitude from the at least one second audio signal as inputs to an algorithm to generate an output from the algorithm; and determining, from the output of the algorithm, a precipitation condition and a confidence measure of the precipitation condition.

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling routing of an autonomous vehicles (AV) by identifying routes from a first location to a second location, identifying a target efficiency value of autonomous driving along a respective route, determining, in view of historical data for the respective route and using one or more randomized conditions, a confidence level associated with the target efficiency value, selecting, based on the target efficiency values and the associated confidence level, a preferred route, and causing the AV to select the first route for travel from the first location to the second location.

Vision based guidance system and method for lawn mowing devices are disclosed. An exemplary method for operating an autonomous lawn mower includes receiving, via a receiver, a perimeter data set from a handheld computer. The perimeter data set includes a perimeter outline of at least one perimeter that is determined utilizing a GPS unit of the handheld computer. The exemplary method also includes collecting, via at least one camera, images of a set area within the perimeter outline and mowing, via a mowing blade, grass within the set area. The exemplary method also includes autonomously steering, via a controller, the autonomous lawn mower based on the perimeter outline of the at least one perimeter and the images captured by the at least one camera.

A system and method automatically sets vehicle functions of a vehicle, while taking account of external influencing factors. The system includes at least one back-end server which is configured to receive vehicle data of the vehicle and, while taking account of at least part of the vehicle data, to determine data with regard to external influencing factors. The back-end server is configured to determine optimal settings of the vehicle functions of the vehicle, while taking account of the vehicle data and the data with regard to the external influencing factors, and to transmit the optimum settings of the vehicle functions to the vehicle. The vehicle is configured to control the vehicle functions such that the optimum vehicle functions determined by the back-end server are adopted.