Abstract: An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

Abstract: An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

Abstract: Sensor data collected via an autonomous vehicle can be labeled using sensor data collected via an additional vehicle, such as a non-autonomous vehicle mounted with a vehicle agnostic removable hardware pod. A training instance can include an instance of data collected by an autonomous vehicle sensor suite and one or more corresponding labels.

Abstract: A downwardly-directed optical array sensor may be used in an autonomous vehicle to enable a velocity (e.g., an overall velocity having a direction and magnitude, or a velocity in a particular direction, e.g., along a longitudinal or lateral axis of a vehicle) to be determined based upon images of a ground or driving surface captured from multiple downwardly-directed optical sensors having different respective fields of view.

Abstract: An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: Sensor data collected via an autonomous vehicle can be labeled using sensor data collected via an additional vehicle, such as a non-autonomous vehicle mounted with a vehicle agnostic removable hardware pod. A training instance can include an instance of data collected by an autonomous vehicle sensor suite and one or more corresponding labels.

Abstract: A control system of a self-driving vehicle (SDV) can dynamically determine a set of autonomous driving actions to be performed by the SDV, and generate a set of intention outputs using a light output system of the SDV based on the set of autonomous driving actions, where the set of intention outputs indicating the set of autonomous driving actions prior to the SDV executing the set of autonomous driving actions. The control system can then execute the set of autonomous driving actions using acceleration, braking, and steering systems of the SDV. While executing the set of autonomous driving actions, the control system can generate a corresponding set of reactive outputs using the light output system to indicate the set of autonomous driving actions being executed, where the corresponding set of reactive outputs replacing the set of intention outputs.

Abstract: A control system of a self-driving vehicle (SDV) can dynamically determine a set of autonomous driving actions to be performed by the SDV, and generate a set of intention outputs using a light output system of the SDV based on the set of autonomous driving actions, where the set of intention outputs indicating the set of autonomous driving actions prior to the SDV executing the set of autonomous driving actions. The control system can then execute the set of autonomous driving actions using acceleration, braking, and steering systems of the SDV. While executing the set of autonomous driving actions, the control system can generate a corresponding set of reactive outputs using the light output system to indicate the set of autonomous driving actions being executed, where the corresponding set of reactive outputs replacing the set of intention outputs.

Abstract: A control system of a self-driving vehicle (SDV) can process sensor data from a sensor system of the SDV to autonomously control acceleration, steering, and braking systems of the SDV along a current route. Based on the current route, the control system can dynamically determine a set of immediate actions to be performed by the SDV. Based on the set of immediate actions, the control system can generate a set of intention outputs on a lighting strip of the SDV, the set of intention outputs indicating the set of immediate actions prior to the SDV executing the set of immediate actions.

Abstract: An autonomous vehicle operates to obtain sensor data for a road segment that is in front of the vehicle. The autonomous vehicle can include a control system which processes the sensor data to determine an interference value that reflects a probability that at least a detected object will interfere with a selected path of the autonomous vehicle at one or more points of the road segment. The control system of the autonomous vehicle can adjust operation of the autonomous vehicle based on the determined interference value.

Abstract: A control system of a self-driving vehicle (SDV) can process sensor data from a sensor system of the SDV to autonomously control acceleration, steering, and braking systems of the SDV along a current route. Based on the current route, the control system can dynamically determine a set of immediate actions to be performed by the SDV. Based on the set of immediate actions, the control system can generate a set of intention outputs on a lighting strip of the SDV, the set of intention outputs indicating the set of immediate actions prior to the SDV executing the set of immediate actions.

Abstract: An agricultural harvesting machine (harvester) is automatically controlled and steered so as to completely harvest all crop in a field. A Global Positioning System (GPS) and an Inertial Navigation System provide data regarding the position and orientation of the harvester in a field. A Field Coverage Planner constructs a field coverage plan which defines a path for the harvester to follow in cutting crop from a field. Video images of the field in front of the harvester are derived alternately from two video cameras mounted on opposite sides of the harvester. The video images are processed in a Video Processing Computer to detect the crop line between cut and uncut crop and generate steering signals for steering the harvester along the crop line. Video images are also processed to detect the end of a row at the end of a field and supply a measure of the distance to the end of the row.