Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

A target-orientated navigation system and related method for a vehicle having a generic navigation system includes one or more processors and a memory. The memory includes one or more modules that cause the processor to receive perception data, discretize the perception data into a plurality of lattices, generate a collision probability array having a plurality of cells that correspond to the plurality of lattices, determine which cells of the collision probability array satisfy a safety criteria, receive an artificial potential field array having a plurality of cells that correspond to the plurality of cells of the collision probability array, generate, an objective score array having a plurality of cells corresponding to the cells of the collision probability array, and direct a vehicle control system of the vehicle to guide the vehicle to a location representative of a cell in the objective score array that has a highest value.

A system, a method and a device for planning a driving path for a vehicle are described. In one example aspect, the device is configured to: analyze sense data to obtain positioning data of vehicles; assign vehicle transportation tasks to an unmanned vehicle and a manned vehicle in the predetermined area in accordance with a predetermined transportation task, each vehicle transportation task including a transportation start point and a transportation end point; plan driving paths for the unmanned vehicle and the manned vehicle based on the assigned vehicle transportation tasks, the vehicle positioning data and map data; transmit the assigned transportation task and the planned driving path for the unmanned vehicle to the unmanned vehicle; and transmit the assigned transportation task and the planned driving path for the manned vehicle to a mobile device corresponding to the manned vehicle.

An online agricultural system manages and optimizes interactions of entities within the system to enable the execution of transaction and the transportation of crop products. The online agricultural system accesses historic and environmental data describing factors that may impact crop product transactions and/or transportation to determine market prices for crop products and crop product transportation. Responsive to receiving a request from an entity, the online agricultural system determines an optimal transaction for the entity, such as a price for selling a crop product, an available crop product for purchase, or a transportation opportunity to transport a crop product.

Provided is an autonomous moving body that controls driving wheels by allowing a trailing caster to be located on a front side with respect to a traveling direction when it is recognized that there is no step in a traveling direction and controls the driving wheels to change the orientation of a base body in which the driving wheels and the trailing caster are arranged and approach the step so that at least one of two driving wheels contacts the step before the trailing caster contacts the step when it is recognized that there is a step in the traveling direction.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A vehicular collision avoidance system comprising a system controller, pulsed laser transmitter, a number of independent ladar sensor units, a cabling infrastructure, internal memory, a scene processor, and a data communications port is presented herein. The described invention is capable of developing a 3-D scene, and object data for targets within the scene, from multiple ladar sensor units coupled to centralized LADAR-based Collision Avoidance System (CAS). Key LADAR elements are embedded within standard headlamp and taillight assemblies. Articulating LADAR sensors cover terrain coming into view around a curve, at the crest of a hill, or at the bottom of a dip. A central laser transmitter may be split into multiple optical outputs and guided through fibers to illuminate portions of the 360° field of view surrounding the vehicle. These fibers may also serve as amplifiers to increase the optical intensity provided by a single master laser.

A control device includes a direction identifying data generator that generates direction identifying data including at least a portion of acquired point group data indicating a position of a region including the ridge in front of an agricultural vehicle in a traveling direction, a direction identification part that identifies a direction of the ridge on the basis of the direction identifying data, and a travel control part that controls the agricultural vehicle such that the agricultural vehicle travels in the direction of the ridge identified by the direction identification part.

In various examples, a path perception ensemble is used to produce a more accurate and reliable understanding of a driving surface and/or a path there through. For example, an analysis of a plurality of path perception inputs provides testability and reliability for accurate and redundant lane mapping and/or path planning in real-time or near real-time. By incorporating a plurality of separate path perception computations, a means of metricizing path perception correctness, quality, and reliability is provided by analyzing whether and how much the individual path perception signals agree or disagree. By implementing this approach—where individual path perception inputs fail in almost independent ways—a system failure is less statistically likely. In addition, with diversity and redundancy in path perception, comfortable lane keeping on high curvature roads, under severe road conditions, and/or at complex intersections, as well as autonomous negotiation of turns at intersections, may be enabled.

A path planning method of mobile robots based on image processing is provided and includes: S1, preprocessing a map image: calculating a safety distance between a mobile robot and a surrounding obstacle during a movement of the mobile robot based on external geometric features of the mobile robot, forming a circular range on the map image with a expansion point as a center and the safety distance as an expansion radius to set a safety range, and marking the safety range; performing skeleton feature extraction on the map image after the marking to obtain a reference path map; S2, obtaining an initial path; and S3, optimizing the initial path. The path planning method improves the flexibility of the algorithm and has high robustness and operational efficiency, and the optimal path obtained can ensure the moving safety of the mobile robot.