Aspects of the disclosure relate to evaluating pullovers for autonomous vehicles. In one instance, a set of potential pullover locations within a predetermined distance of a destination may be identified. Whether any of the potential pullover locations of the set include one or more of a plurality of predetermined types of regions of interest where a vehicle should not park for an extended period of time may be determined. A pullover location is identified based on the determination. The identified pullover location may be compared to a pullover location identified by autonomous vehicle control software in order to evaluate the pullover location identified by the autonomous vehicle control software.

An autonomous vehicle is described herein. The autonomous vehicle comprises a first sensor and a second sensor having limited fields of view, an articulation system, and a computing system. The computing system determines a first region and a second region external to the autonomous vehicle based on a sensor prioritization scheme comprising a ranking of regions surrounding the autonomous vehicle. The computing system then causes the articulation system to orient the first sensor towards the first region and the second region towards the second region. Responsive to receiving a sensor signal from the first sensor indicating that an object has entered a field of view of the first sensor, the computing system determines a third region having a higher ranking than the second region within the sensor prioritization scheme. The computing system then causes the articulation system to orient the second sensor towards the third region.

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.

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.

The present disclosure is directed to validating motion plans for autonomous vehicles. In particular, the methods, devices, and systems of the present disclosure can: receive data indicating a motion plan of an autonomous vehicle through an environment of the autonomous vehicle; receive data indicating one or more inputs utilized in generating the motion plan; and determine, based at least in part on the data indicating the motion plan and the data indicating the input(s), whether execution of the motion plan by the autonomous vehicle would violate one or more predetermined constraints applicable to motion plans for the autonomous vehicle.

A slip determination system is provided that is capable of providing appropriate control information to a traveling vehicle when the traveling vehicle has proceeded to an area where a slip is likely to occur during automatic travel. The slip determination system includes: a vehicle position detection module for detecting a vehicle position; and an automatic travel control portion for enabling automatic travel based on the vehicle position and a set travel path; a slip amount calculation portion for calculating a slip amount of the traveling vehicle body, using an estimated movement distance of the traveling vehicle body calculated based on the number of revolutions of a driving axle of the traveling vehicle body, and an actual movement distance of the traveling vehicle body calculated based on the vehicle position; an appropriateness determination portion for performing appropriateness determination to determine, based on the slip amount, whether or not a state of a traveling ground surface is appropriate for automatic travel; and an automatic travel stop portion for stopping automatic travel based on a determination result.

Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Autonomous vehicles are an exciting prospect to the future of driving. However, concerns about the decision-making made by the AI controlling a vehicle has been of concern, particularly in light of high-profile accidents. We can alleviate some concern, introduce better decisions, and also train an AI to make better decisions by introducing a remote viewer's, e.g., a human's, reaction to a possibly complex environment surrounding a vehicle that includes a potential threat to the vehicle. One or more remote viewer may provide a recommended response to the threat that may be incorporated in whole or in part in how the vehicle reacts. Various ways to engage and utilize remote viewers are proposed to improve the likelihood of receiving useful recommendations, including modifying how the environment is presented to a remote viewer to best suit the remote viewer, e.g., perhaps present the threat in a game.