Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicle and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the condition of components for salvage following a collision or other loss-event. To this end, the information regarding a plurality of components may be received. A component of the plurality of components may be identified for assessment. Assessment may including causing test signals to be sent to the identified component. In response to the test signal, one or more responses may be received. The received response may be compared to an expected response to determine whether the identified component is salvageable.

Methods and systems for autonomous and semi-autonomous vehicle control relating to malfunctions are disclosed. Malfunctioning sensors or software of autonomous vehicles may be identified from operating data of the vehicle, and a component maintenance requirement status associated with such malfunctioning component may be generated. Based upon such status, usage restrictions may be enacted to limit operation of the vehicle while the component is malfunctioning. This may include disabling or restricting use of certain autonomous or semi-autonomous features of the vehicle until the component is repaired or replaced. Repair may be accomplished by automatically scheduling repair of the vehicle or installing an updated or uncorrupted version of a software program, in various embodiments.

An autonomous driving delivery system includes a first selection unit configured to select a first candidate delivery location based on a position of the user or the like, a determination unit configured to determine whether or not the autonomous driving vehicle can reach the first candidate delivery location, a second selection unit configured to select a second candidate delivery location where the autonomous driving vehicle can reach if it is determined that the autonomous driving vehicle cannot reach the first candidate delivery location, a designation unit configured to designate the first candidate delivery location or the second candidate delivery location as the delivery location, and a presentation unit configured to present the designated delivery location to the user before the autonomous driving vehicle starts the delivery of the luggage to the designated delivery location.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Malfunctions may be detected by receiving sensor data from a plurality of sensors. One of these sensors may be selected for assessment. An electronic device may obtain from the selected sensor a set of signals. When the set of signals includes signals that are outside of a determined range of signals associated with proper functioning for the selected sensor, it may be determined that the selected sensor is malfunctioning. In response, an action may be performed to resolve the malfunction and/or mitigate consequences of the malfunction.

A method for monitoring a motor vehicle having an automated driving function. Specific operating modes are each assigned, in each instance, at least one load profile, which is a function of the load circuit needed for the operating mode and normally occurs during this operating mode. At least one characteristic quantity of the energy store is predicted as a function of the load profile. The corresponding operating mode and/or the automated driving function is enabled as a function of the predicted characteristic quantity of the energy store. The predicted characteristic quantity is ascertained as a function of a base load and/or a switching-off potential of the load circuit not needed for the operating mode.

Presented are intelligent vehicle systems and control logic for driver coaching and on-demand vehicle charging, methods for making/using such systems, and motor vehicles with real-time eco-routing and automated driving capabilities. A method for controlling operation of a vehicle includes: determining an origin and destination for the vehicle; conducting a geospatial query to identify a candidate route for traversing from the origin to the destination; determining, based on current electrical characteristics of the vehicle's battery pack, an estimated driving range for the vehicle; responsive to the estimated driving range being less than the candidate route's distance, evaluating energy characteristics of the candidate route to derive an estimated energy expenditure to reach the destination; using the estimated energy expenditure, generating an action plan with vehicle maneuvering and/or accessory usage actions that extend the estimated driving range; and commanding a resident vehicle subsystem to execute a control operation based on the action plan.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the robustness of autonomous systems, including the use of virtual assessment of software components within a simulated environment. To this end, a server may retrieve one or more routines associated with autonomous operation. The server may also generate a set of test data associated with test conditions. The server may also execute an emulator that virtually simulates autonomous environment. The test data may be presented to the routines executing in the emulator to generate output data. The server may then analyze the output data to determine a quality metric.

A vehicle control device or method controls an output of a drive source of a vehicle based on a color of an illuminated signal of a traffic light in a vehicle-advancing direction during travel in autonomous driving. The vehicle control device includes a control unit that estimates the color of the currently illuminated signal of the traffic light based on oncoming vehicle information and controls the output of the drive source based on an estimation result when the color of the illuminated signal of the traffic light cannot be acquired by the onboard camera. The control unit limits the output of the drive source and reduces a vehicle speed from a current vehicle speed to a vehicle speed at which fuel efficiency is superior to that at the current vehicle speed when the color of the illuminated signal of the traffic light is estimated to be red.

A multi layer learning based control system and method for an autonomous vehicle or mobile robot. A mission planning layer, behavior planning layer and motion planning layer each having one or more neural neworks are used to develop an optimal route for the autonomous vehicle or mobile robot, provide a series of functional tasks associated with at least one or more of the neural networks to follow the planned optimal route and develop commands to implement the functional tasks.