An autonomous off-road vehicle, upon encountering an obstruction while navigating a route, can apply a first machine-learned model to identify the obstruction. In the event that the first machine-learned model cannot identify the obstruction, the autonomous off-road vehicle can apply a second machine-learned model configured to determine whether or not the obstruction can be ignored, for instance based on dimensions of the obstruction. If the obstruction can be ignored, the autonomous off-road vehicle can continue navigating the route. If the obstruction cannot be ignored, the autonomous off-road vehicle can modify the route, can stop, can flag the obstruction to a remote human operator, can modify an interface of a human operator to display a notification or a video feed from the vehicle, and the like.

Disclosed herein are systems and methods for autonomous vehicle operation. A computing system can include a communication device configured to receive a plurality of event signals from at least a first autonomous vehicle that is traversing a path, and a processor in electrical communication with the communication device and configured to determine whether the event signals are indicative of an obstacle in a portion of the path. The communication device can be configured to receive, from at least a second autonomous vehicle, at least one characteristic of the obstacle captured by at least one sensor of the second autonomous vehicle, and transmit, to at least a third autonomous vehicle, at least one task to clear the obstacle from the portion of the path. The processor can be configured to determine, based on the characteristic of the obstacle, the at least one task to be transmitted by the communication device.

This disclosure provides systems and methods for controlling a vehicle by teleoperation based on a speed limiter. The method may include: receiving, at the autonomous vehicle, a teleoperation input from a teleoperation system, wherein the teleoperation input comprises a throttle control input for remotely controlling a speed of the autonomous vehicle; determining the speed of the autonomous vehicle; determining if the speed of the autonomous vehicle has reached a threshold speed below a speed limit; and upon determining that the speed of the autonomous vehicle has reached the threshold speed, reducing effect of the throttle control input from the teleoperation system such that an acceleration rate of the speed of the autonomous vehicle is reduced.

The vehicle movement control apparatus of the disclosure sets an update movement route as a target movement route when an update condition is satisfied. The apparatus acquires a turning characteristic, an acceleration characteristic, and a deceleration characteristic of a vehicle while executing an automatic movement control to cause the vehicle to move along the update movement route. The apparatus updates vehicle behavior characteristic data so as to represent actual vehicle behavior characteristics, based on the acquired turning characteristics, the acquired acceleration characteristic, and the acquired deceleration characteristic.

Methods and enhanced apparatus used in such methods are described that a dispatched logistics operation for a deliverable item from a hold-at-location (HAL) logistics facility having a secured storage and using a modular autonomous bot apparatus assembly and a dispatch server. The bot apparatus assembly picks up and delivers the item from the HAL facility in response to a delivery dispatch command from the dispatch server. In response, the MAM of the bot verifies compatibility of modular components for the operation, controls receiving of the deliverable item from the secured storage at the HAL facility, then autonomously causes movement to the delivery destination. The MAM notifies the customer before delivery of the approaching delivery, authenticates delivery is to the authorized customer, provides access to the item within the bot apparatus assembly, monitors unloading of the item, then autonomously moves back to the HAL facility.

Methods and apparatus for automatically extending aircraft wing flaps in response to detecting an excess energy steep descent condition are described. An example control system of an aircraft includes one or more processors. The one or more processors determine whether the aircraft is experiencing an excess energy steep descent (EESD) condition. In response to determining that the aircraft is experiencing the EESD condition, the one or more processors command an actuator of the aircraft coupled to a flap of the aircraft to extend the flap from a current flap position to a subsequent flap position defined by a flap extension sequence.

The present disclosure provides systems and methods that control the motion of an autonomous vehicle by rewarding or otherwise encouraging progress toward a goal, rather than simply rewarding distance travelled. In particular, the systems and methods of the present disclosure can project a candidate motion plan that describes a proposed motion path for the autonomous vehicle onto a nominal pathway to determine a projected distance associated with the candidate motion plan. The systems and methods of the present disclosure can use the projected distance to evaluate a reward function that provides a reward that is positively correlated to the magnitude of the projected distance. The motion of the vehicle can be controlled based on the reward value provided by the reward function. For example, the candidate motion plan can be selected for implementation or revised based at least in part on the determined reward value.

Described herein are technologies relating to computing a likelihood of an operation-influencing event with respect to an autonomous vehicle at a geographic location. The likelihood of the operation-influencing event is computed based upon a prediction of a value that indicates whether, through a causal process, the operation-influencing event is expected to occur. The causal process is identified by means of a model, which relates spatiotemporal factors and the operation-influencing events.

An example driver assistance system includes an object detection (OD) network, a semantic segmentation network, a processor, and a memory. In an example method, an image is received and stored in the memory. An object detection (OD) polygon is generated for each object detected in the image, and each OD polygon encompasses at least a portion of the corresponding object detected in the image. A region of interest (ROI) is associated with each OD polygon. Such method may further comprise generating a mask for each ROI, each mask configured as a bitmap approximating a size of the corresponding ROI; generating at least one boundary polygon for each mask based on the corresponding mask, each boundary polygon having multiple vertices and enclosing the corresponding mask; and reducing a number of vertices of the boundary polygons based on a comparison between points of the boundary polygons and respective points on the bitmaps.

A method of detecting sensor malfunction in an automated guided vehicle, AGV, including for at least two different pairs of wheel units in a motion state of the AGV, calculating a motion value for at least one motion variable of a body of the AGV based on sensor data from a wheel sensors and/or a steering sensors; for at least one motion variable, calculating a difference between the motion values for at least two different pairs; and determining that there is a malfunction in one or more of the wheel sensors and the steering sensors if one of the at least one difference exceeds a threshold value associated with the respective motion variable.