A teleoperations system that collaboratively works with an autonomous vehicle guidance system to generate a path for controlling the autonomous vehicle may comprise generating one or more trajectories at the teleoperations system based at least in part on environment data received from the autonomous vehicle and presenting the one or more trajectories to a teleoperator (e.g., a human user, machine-learned model, or artificial intelligence component). A selection of one of the trajectories may be received at the teleoperations system and transmitted to the autonomous vehicle. The one or more trajectories may be generated at the teleoperations system and/or received from the autonomous vehicle. Regardless, the autonomous vehicle may generate a control trajectory based on the trajectory received from teleoperations, instead of merely implementing the trajectory from the teleoperations system.

A control command is provided to a vehicle control module that identifies one or more vehicle actions to be performed by the vehicle control module to control a position of an autonomous vehicle (AV) with respect to an external environment. Whether one or more conditions pertaining to the position of the AV with respect to the external environment are satisfied is determined. Responsive to determining that the one or more conditions pertaining to the position of the AV with respect to the external environment are satisfied, a first instruction is provide to the vehicle control module that permits the vehicle control module to deviate from the one or more vehicle actions identified in the control command and to perform an energy saving action with respect to the AV.

Disclosed is a vehicle performing autonomous driving. The vehicle includes: a display, a driver to drive the vehicle, an inputter configured to receive destination information related to a destination of the vehicle, a power supply including a battery for supplying electric power to the vehicle, a first sensor module to detect a capacity of the battery, and a controller to determine an expected driving route of the vehicle based on the destination information. The controller calculates an expected power consumption of the vehicle based on the expected driving route, and changes an autonomous driving state of the vehicle in the expected driving route based on the expected power consumption and the detected capacity of the battery.

According to one aspect, an autonomous vehicle includes hardware systems which receive relatively low voltage from a low voltage power distribution unit (LVPDU). An LVPDU includes a power source such as a DC-DC converter and a plurality of backup batteries. The plurality of backup batteries is configured to provide backup power to subsets of components arranged to effectively all be powered by the power source onboard the LVPDU. The backup batteries may be tested, substantially while LVPDU is being used to provide power. The backup batteries may be charged substantially in parallel.

The disclosure generally pertains to minimizing battery power consumption while employing local wireless communication to activate an operation of an autonomous vehicle. In an example implementation, a vehicle controller of an autonomous vehicle transitions to a powered-down state after the autonomous vehicle is parked at a parking spot that lacks cellular communication coverage. The vehicle controller may transition to a powered-up state at a scheduled time to execute an autonomous operation based on a directive stored in a cloud-based device. In no directive has been stored, the vehicle controller wakes up periodically in a partially powered-up state and transmits a query in a local wireless communications format to the cloud-based device to check for a directive. If no directive is present, the vehicle controller transitions back to the powered-down state. If a directive is present, the vehicle controller transitions to a fully powered-up state to execute the autonomous operation.

A method includes identifying, based on grade data of a route of an autonomous vehicle (AV), a segment of the route that has a grade value that meets a threshold grade value. Responsive to identifying the segment, the method further includes generating, based on the grade data and physical vehicle data of the AV, driving constraint data for the segment of the route. The method further includes causing a routing module of the AV to generate, based on the driving constraint data for the segment of the route, short time horizon routing data corresponding to a portion of the segment. The AV is to travel the portion of the segment of the route based on the short time horizon routing data.

A method includes identifying route data including a threshold arrival time for a route for an autonomous vehicle (AV) and calculating, based on the route data and a fuel-efficient speed value for each segment of the route, an estimated arrival time. Responsive to the estimated arrival time not meeting the threshold arrival time, the method includes identifying at least a subset of segments that each represent a candidate for speed increase, computing, for each segment in the subset and based on the fuel economy data, a correlation metric that indicates a correlation between a change in fuel economy and a change in speed for a corresponding segment in the subset, and increasing, for at least one segment from the subset and based on a respective correlation metric, a fuel-efficient speed value of the corresponding segment from the subset to provide a speed profile reflecting the increased fuel-efficient speed value.

A method of operating a vehicle includes generating initial state of charge and vehicle velocity profiles for a travel route, for each initial velocity setpoint defining the vehicle velocity profile, generating a plurality of updated state of charge setpoints, for each of the initial velocity setpoints, selecting one of the updated state of charge setpoints to define an updated state of charge profile, for each of the updated state of charge setpoints that defines the updated state of charge profile, generating a plurality of updated velocity setpoints, for each of the updated state of charge setpoints that defines the updated state of charge profile, selecting one of the updated velocity setpoints to define an updated velocity profile, and controlling operation of an electric machine and engine according to the updated state of charge profile and updated velocity profile over the travel route.

The present invention is a method for determining an optimal speed of a first vehicle preceded by a second vehicle. Position, speed and acceleration of the second vehicle are measured in order to determine a trajectory thereof, and a dynamic model of the first vehicle is constructed. The optimal speed is then determined by minimizing the energy consumption of the vehicle by use of the dynamic model by minimization being constrained by the trajectory of the second vehicle.

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.