A method for drivable area detection and autonomous obstacle avoidance of unmanned haulage equipment in deep confined spaces is disclosed, which includes the following steps: acquiring 3D point cloud data of a roadway; computing a 2D image drivable area of the coal mine roadway; acquiring a 3D point cloud drivable area of the coal mine roadway; establishing a 2D grid map and a risk map, and performing autonomous obstacle avoidance path planning by using a particle swarm path planning method designed for deep confined roadways; and acquiring an optimal end point to be selected of a driving path by using a greedy strategy, and enabling an unmanned auxiliary haulage vehicle to drive according to the optimal end point and an optimal path. Images of a coal mine roadway are acquired actively by use of a single-camera sensor device.

A method for determining a pullover spot for a vehicle is described. The method includes using a computing device to detect information related to a system of the vehicle or an environment surrounding the vehicle using a sensor of a vehicle and determine a local failure of the vehicle based on the information. The computing device may then be used to determine that the vehicle should pullover before completing a current trip related to transporting a passenger or good by comparing vehicle requirements for the trip with the local failure and determine a pullover spot by identifying a first area for the vehicle to park in part based on a second area being available for a second vehicle to pick up the passenger or good. The computing device may operate the vehicle to the pullover spot and transmit a request for a second vehicle.

The technology relates to an exterior sensor system for a vehicle configured to operate in an autonomous driving mode. The technology includes a close-in sensing (CIS) camera system to address blind spots around the vehicle. The CIS system is used to detect objects within a few meters of the vehicle. Based on object classification, the system is able to make real-time driving decisions. Classification is enhanced by employing cameras in conjunction with lidar sensors. The specific arrangement of multiple sensors in a single sensor housing is also important to object detection and classification. Thus, the positioning of the sensors and support components are selected to avoid occlusion and to otherwise prevent interference between the various sensor housing elements.

Various systems and methods for providing a vehicle control system are described herein. A system for managing a vehicle comprises: a vehicle control system of a vehicle having access to a network, including: a communication module to interface with at least one of: a mobile device, the vehicle, and environmental sensors coupled to the vehicle; and a configuration module to identify a mitigation operation to be taken when predetermined factors exist; wherein the vehicle control system is to identify a potential obstacle in a travel route of the vehicle and initiate a mitigation operation at the vehicle.

An electronic control device is mounted on a vehicle equipped with a plurality of hardware capable of operation, and comprises an information collection unit which collects external information of the vehicle, a storage unit which stores a plurality of processing specifications which prescribe processing to be executed by each of the plurality of hardware and the external information to be used by the plurality of hardware for performing operation, and an applied condition, which is a condition related to the external information and a status of the plurality of hardware for applying each of the plurality of processing specifications, and a processing control unit which determines one processing specification among the plurality of processing specifications from a correspondence to the condition based on the collected external information and the status of the plurality of hardware, and controls the plurality of hardware based on the determined processing specification.

Methods and systems for controlling navigation of an autonomous vehicle for traversing a drivable area are disclosed. The methods include receiving information relating to a drivable area that includes a plurality of polygons, identifying a plurality of logical edges that form a boundary of the drivable area, sequentially and repeatedly analyzing concavities of each the plurality of logical edges until identification of a first logical edge that has a concavity greater than a threshold, creating a first logical segment of the boundary of the drivable area. This segmentation may be repeated until each of the plurality of logical edges has been classified. The method may include creating and adding (to a map) a data representation of the drivable area that comprises an indication of the plurality of logical segments, and adding the data representation to a road network map comprising the drivable area.

Methods and systems for controlling navigation of an autonomous vehicle for traversing a drivable area are disclosed. The methods include receiving information relating to a drivable area that includes a plurality of polygons, identifying a plurality of logical edges that form a boundary of the drivable area, sequentially and repeatedly analyzing concavities of each the plurality of logical edges until identification of a first logical edge that has a concavity greater than a threshold, creating a first logical segment of the boundary of the drivable area. This segmentation may be repeated until each of the plurality of logical edges has been classified. The method may include creating and adding (to a map) a data representation of the drivable area that comprises an indication of the plurality of logical segments, and adding the data representation to a road network map comprising the drivable area.

A method includes obtaining, from an operator of a robot, a return execution lease associated with one or more commands for controlling the robot that is scheduled within a sequence of execution leases. The robot is configured to execute commands associated with a current execution lease that is an earliest execution lease in the sequence of execution leases that is not expired. The method includes obtaining an execution lease expiration trigger triggering expiration of the current execution lease. After obtaining the trigger, the method includes determining that the return execution lease is a next current execution lease in the sequence. While the return execution lease is the current execution lease, the method includes executing the one or more commands for controlling the robot associated with the return execution lease which cause the robot to navigate to a return location remote from a current location of the robot.

A monitoring system and method are provided to monitor ground movement of aircraft driven with electric taxi drive systems and movement of ground service vehicles and equipment and personnel within airport ramp areas. Monitor and sensor devices, including those that are intelligent and employ scanning technology to generate image, positional, and other data may be mounted in locations on aircraft, ground service vehicles and equipment, passenger loading bridges, an airport terminal, and other ramp locations to generate a constant stream of data as the aircraft moves into, within, and out of an airport ramp area. The data stream is transmitted to an artificial intelligence-based processing system to identify and communicate possible safety hazards to multiple locations so that the aircraft's ground travel or a ground vehicle's travel may be altered to avoid identified safety hazards and to avoid collisions.

According to one aspect, a method is provided to determine whether an autonomous system is ready to be deployed or is otherwise ready for use, scene-based metrics, or metrics based on instances of scenarios. Scene-based metrics are mapped, or otherwise translated, to distance-based metrics such that substantially standard distance-based metrics may be used to gate the readiness of an autonomy system for deployment.