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, apparatus, and system for determining average lane travel speeds is disclosed. A plurality of vehicles traveling in a same direction as the ADV in a plurality of lanes are identified. Over a first time period, the plurality of vehicles is tracked. At least a first quantity of representative vehicles within the plurality of vehicles that are representative of vehicles traveling in the lane over the first time period are identified. For each of the plurality of lanes, an average speed over the first time period of the representative vehicles associated with the lane is determined. A trajectory is planned for the ADV, wherein the planned trajectory moves toward a lane whose representative vehicles have a fastest average speed. Thereafter, control signals are generated to control operations of the ADV based on the planned trajectory.

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.

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.

In accordance with one embodiment of the present disclosure, method includes obtaining multi-level environment data corresponding to a plurality of driving environment levels, encoding the multi-level environment data at each level, extracting features from the multi-level environment data at each encoded level, fusing the extracted features from each encoded level with a spatial-temporal attention framework to generate a fused information embedding, and decoding the fused information embedding to predict driving environment information at one or more driving environment levels.

A vehicle may include a frame structure, a body mounted to the frame structure, and a vehicle navigation system. The vehicle navigation system may include a navigation sensor mounted to the frame structure, and a processor in communication with the navigation sensor. The navigation sensor may be configured to detect reference elements disposed in or on a road on which the vehicle travels. The processor may be configured to receive, from the navigation sensor, signals indicative of a sequence or pattern of detected reference elements. The processor may also be configured to determine, using the received signals, at least one of a position, velocity, or orientation of the vehicle on the road.

A flight control method includes displaying a user interface configured to receive an operation instruction including coordinates of waypoints, generating route data of a route based on the coordinates of the waypoints, sending the route data to an aircraft to instruct the aircraft to execute the route, recording an interruption point during execution of the route by the aircraft, displaying a plurality of candidate starting points including at least one of the interruption point, a last waypoint of the route before the interruption point, a next waypoint of the route after the interruption point, or a user-designated waypoint, in response to a selection instruction, selecting a target starting point from the plurality of candidate starting points, and controlling the aircraft in an interrupted state to fly to the target starting point and resume the execution of the route from the target starting point.

A controller of a machine determines jointly a sequence of control inputs defining a state trajectory of the machine and a desired knowledge of the environment by solving a multivariable constrained optimization of a model of dynamics of the machine relating the state trajectory with the sequence of control inputs subject to a constraint on admissible values of the states and the control inputs defined based on the desired knowledge of the surrounding environment represented by the state of the environment and the uncertainty of the state of the environment determined from the measurements of the environment. In such a manner, the controller performs joint but imbalance optimization of the control inputs and the sensing instructions to the sensor for learning the environment.

A system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.

A method for controlling a subsea vehicle. The method includes receiving sensor data representing a subsea environment from one or more sensors of the subsea vehicle. The method identifies one or more objects present in the subsea environment based on the sensor data using an artificial intelligence machine. The method transmits at least a portion of the sensor data, including an identification of the one or more objects, to a user interface. The method includes receiving a requested vehicle task from the user interface. The requested vehicle task being selected by a user via the user interface. The method performs the requested vehicle task without vehicle position control from the user.