Systems and methods enable an autonomous vehicle to perform an iterative task of transferring material from a source location to a destination location, such as moving dirt from a pile, in a more efficient manner, using a combination of reinforcement learning techniques to select a motion path for a particular iteration and visual servo control to guide the motion of the vehicle along the selected path. Lifting, carrying, and depositing of material by the autonomous vehicle can also be managed using similar techniques.

Disclosed herein are apparatuses and methods for managing building pathway information. A building and/or security management system can detect, based on receiving input from multiple sensor devices, a possible safety issue in a first pathway to an egress of a building, determine, based on detecting the possible safety issue and based on information regarding other pathways in the building, a second pathway to the egress of the building, and indicate at least the second pathway to another system.

A method may include obtaining sensor data about a total measurable world around an autonomous vehicle. The sensor data may be captured by sensor units co-located with the autonomous vehicle. The method may include generating a mapping dataset including the obtained sensor data and identifying data elements that each represents a point in the mapping dataset. The method may include sorting the data elements according to a structural data categorization that is a template for a high-definition map of the total measurable world and determining a mapping trajectory of the autonomous vehicle. The mapping trajectory may describe a localization and a path of motion of the autonomous vehicle. The method may include generating the high-definition map based on the structural data categorization and relative to the mapping trajectory of the autonomous vehicle, and the high-definition map may be updated based on the path of motion of the autonomous vehicle.

To provide an autonomous traveling control method for a crawler vehicle capable of accurately computing a predicted slide-down amount of a crawler vehicle when the crawler vehicle travels on a slope, and enabling an autonomous traveling control based on the predicted slide-down amount. An autonomous traveling control method for a crawler vehicle includes the steps of setting a target trajectory of a crawler vehicle; and computing a predicted slide-down amount of the crawler vehicle when the crawler vehicle travels on a slope on the basis of a target trajectory, using a center of gravity position of the crawler vehicle, an angle of the slope and a traveling direction of the crawler vehicle in the slope.

An autonomous vehicle control system includes one or more sensors configured for coupling with an agricultural vehicle, the one or more sensors configured to determine kinematics of the agricultural vehicle relative to a crop row. The system includes a guidance control module configured to coordinate steering of one or more steering mechanisms of the agricultural vehicle. The guidance control module includes a sensor input configured to receive kinematics of the agricultural vehicle, a vehicle kinematics comparator configured to determine one or more error values using the received vehicle kinematics, a crop curvature generator configured to determine crop row curvature using the one or more error values, and a steering interface configured to provide instructions to a vehicle steering controller to guide the agricultural vehicle using the crop row curvature.

According to one embodiment, a system receives a captured image perceiving an environment of an autonomous driving vehicle (ADV) from an image capturing device of the ADV capturing a plurality of obstacles near the ADV. The system generates a first tunnel based on a width of a road lane for the ADV, where the first tunnel represents a passable lane for the ADV to travel through. The system generates one or more additional tunnels based on locations of the obstacles, where the one or more additional tunnels modify a width of the passable lane according to a level of invasiveness of the obstacles. The system generates a trajectory of the ADV based on the first and the additional tunnels to control the ADV according to the trajectory to navigate around the obstacles without collision.

A trajectory for an autonomous vehicle (AV) can be generated using curvature segments. A decision planner component can receive a reference trajectory for the AV to follow in an environment. A number of subdivisions (frames) of the reference trajectory may be associated with a curvature value and a tangent vector. Starting with an initial position of the AV, a candidate trajectory can be determined by continuously intersecting a segment with an origin at the initial position of the AV and a reference line associated with a particular frame. The reference line can be substantially perpendicular to the tangent vector of the particular frame. A location of the intersection between the segment and the reference line can be based on a curvature value of the segment. Optimizing a candidate trajectory can include varying curvature values associated with various segments and determining costs of the various candidate trajectories.

An automatic steering control device includes a forward recognition device, a traveling state detector, a lateral positional deviation calculator, a steering angle controller. The lateral positional deviation calculator calculates a first lateral positional deviation that is the lateral positional deviation ahead of the vehicle by a first distance, and a second lateral positional deviation that is the lateral positional deviation ahead of the vehicle by a second distance larger than the first distance. The steering angle controller performs first control on the steering angle so that an absolute value of the first lateral positional deviation decreases, and second control on the steering angle based on the second lateral positional deviation so that a difference between a change amount of the steering angle in the first control and a change amount of an actual steered angle that is a steered angle of wheels of the vehicle decreases.

Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes receiving data indicative of an operating mode of the vehicle, wherein the vehicle is configured to operate in a plurality of operating modes. The method includes determining one or more response characteristics of the vehicle based at least in part on the operating mode of the vehicle, each response characteristic indicating how the vehicle responds to a potential collision. The method includes controlling the vehicle based at least in part on the one or more response characteristics.

An autonomous driving device includes a map recording a content having different type for each position while one or a plurality of contents and positions are associated with each other, an acquisition unit acquiring the content corresponding to a first position on the map, a specification storage unit storing a plurality of autonomous driving modes of the vehicle and the type of content necessary for the execution of the modes in association with each other, a selection unit selecting an executable autonomous driving mode based on the type of content acquired by the acquisition unit and the type of content stored in the specification storage unit, and a control unit controlling the vehicle at the first position in the selected autonomous driving mode, the selection unit determines one autonomous driving mode based on an order of priority set in advance when there is a plurality of executable autonomous driving modes.