An infrastructure is provided for improving the safety of autonomous systems. An autonomous vehicle management system (AVMS) controls one or more autonomous functions or operations performed by a vehicle or machine such that the autonomous operations are performed in a safe manner. The AVMS is capable of dynamically controlling the behavior of sensors associated with a vehicle. For example, for a sensor, the AVMS can dynamically change and control what sensor data is captured by the sensor and/or communicated from the sensor to the AVMS (e.g., granularity/resolution, field of view, control zoom), when the data is captured by the sensor and/or communicated by the sensor to the AVMS (e.g., on-demand, according to a schedule), and how the data is captured by the sensor and/or communicated from the sensor to the AVMS (e.g., communication format, communication protocol, rate of data communication).

A robotic cleaner may include a body, one or more driven wheels configured to urge the body across a surface to be cleaned, one or more distance sensors disposed at least partially within the body such that the one or more distance sensors face the surface to be cleaned and a processor. The one or more distance sensors may be configured to output a measure of a detection distance that extends in a direction of the surface to be cleaned. The processor may be configured to determine whether an abnormality has been detected based, at least in part, on the measure of the detection distance and may be configured to determine a first velocity estimate based, at least in part, on the detection of the abnormality.

Systems for collision avoidance by an autonomous vehicle include a navigational controller adapted to (i) control a driving path of the autonomous vehicle, (ii) process sensor signals from a first sensor system, and (iii) determine whether an object is present in the driving path of the autonomous vehicle based on the sensor signals from the first sensor system. The systems can also include a processor, operationally independent from the navigational controller, adapted to (a) process sensor signals from a second sensor system and (b) determine whether an object is present in the driving path of the autonomous vehicle based on the sensor signals from the second sensor system.

In one embodiment, a driving environment is perceived based on sensor data obtained from a variety of sensors, including determining a current speed of an ADV. In response to a request for driving with an idle speed, a speed guideline is generated based on an idle speed curve in view of the current speed of the ADV. A speed planning operation is performed by optimizing a cost function based on the speed guideline to determine the speeds of the trajectory points at different points in time along a trajectory planned to drive the ADV. One or more control commands are then generated to control the ADV with the planned speeds along the planned trajectory, such that the ADV moves according to an intended idle speed.

A method and system are provided for controlling braking a vehicle in an autonomous driving mode. For instance, the vehicle is controlled in the autonomous driving mode according to a first braking control mode using a first model to adjust the position of a vehicle relative to an expected position of a current trajectory of the vehicle. Using a second model how close to a maximum deviation threshold the vehicle would come if a maximum braking strength for the vehicle was applied is predicted. The maximum deviation threshold provides an allowed forward deviation from the current trajectory. Based on the prediction, the vehicle is controlled in the autonomous driving mode according to a second braking control mode by automatically applying the maximum braking strength.

A sensor arrangement for an agricultural vehicle includes a first electro-optical sensor including a first field of view having an optical axis, and a second electro-optical sensor including a second field of view having an optical axis. The first and second sensors are spaced apart from one another and oriented such that the optical axes of the two sensors intersect at a distance from the two sensors.

The present application provides a method for autonomously controlling a vehicle performed by a control system of the vehicle on the basis of a mission received from a mission controller, the method comprising: receiving a mission comprising a set of instructions from the mission controller; validating the mission by checking whether the mission meets a first set of requirements; executing the mission if the mission meets the first set of requirements and rejecting the mission if the mission does not meet the first set of requirements.

A caster device includes a caster wheel configured to rotate around a horizontal rotation axis; and a case configured to expose a lower surface of the caster wheel, cover the caster wheel and have an inclined surface from a top of the horizontal rotation axis toward a bottom of the horizontal rotation axis.

The described techniques relate to modifying a trajectory of a vehicle, such as an autonomous vehicle, based on a latency associated with one or more systems of the vehicle. In examples, a planning system of the vehicle may predict a future latency (e.g., based on an interval between receipt of sensor data and/or object predictions), and use the future latency to determine a time at which to predict object behavior. Additionally, in some cases, the described techniques may include associating a predetermined acceleration with a predicted future location of the object to create a safety distance around the object, where the predetermined acceleration may be based on a maximum expected acceleration of the object. The safety distance may account for the object potentially accelerating in one or more directions at the future time.

Systems and methods for self-supervised depth estimation using image frames captured from a camera mounted on a vehicle comprise: receiving a first image from the camera mounted at a first location on the vehicle; receiving a second image from the camera mounted at a second location on the vehicle; predicting a depth map for the first image; warping the first image to a perspective of the camera mounted at the second location on the vehicle to arrive at a warped first image; projecting the warped first image onto the second image; determining a loss based on the projection; and updating the predicted depth values for the first image.