Systems and apparatuses for using machine learning to generate a safety output are provided. In some examples, data may be received from a plurality of sources, may be analyzed and one or more machine learning datasets may be generated based on the analyzed data. In some arrangements, data may be received from one or more vehicles. The vehicles may be autonomous, semi-autonomous, or non-autonomous, and/or configured to operate in one or more of those modes. The data may be evaluated based on the one or more machine learning datasets to determine a safety output associated with the data. The safety output may then be used to classify the data and/or to generate one or more instructions for operation of an autonomous vehicle. The instruction(s) may be transmitted to the autonomous vehicle and may modify operation of the vehicle (e.g., to improve safety associated with the vehicle).

A processing device acquires map data that has road information, acquires detection results detected by one or more detectors that detects surroundings of a first mobile object, and cross-checks a position of a second mobile object included in the detection results with the road information of the map data to determine whether or not the position of the second mobile object is included in a region indicating a road of the road information.

The present invention discloses an obstacle navigation travelling device of a power line inspection robot. The present obstacle navigation travelling device of a power line inspection robot includes: a travelling device underframe; a first arm assembly mounted at one end of the travelling device underframe; a first linear driving device for driving the first arm assembly to move linearly on the one end of the travelling device underframe; a second arm assembly mounted at one end of the travelling device underframe far from the first arm assembly; a second linear driving device for driving the second arm assembly to move linearly on the one end of the travelling device underframe far from the first arm assembly; and a third arm assembly. The present invention has the beneficial effects of a large turning range and excellent balancing.

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.

Disclosed are a mover robot system and a controlling method for the same, in which a manipulation of a control screen where a remote control is performed is restricted if a mover robot is located in an area other than a driving area, and a locked screen requesting an input of a preset use code is displayed on a terminal, whereby a display of the locked screen is maintained or released in accordance with the input code.

A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

An agricultural machine includes a vehicle body including a front wheel and a rear wheel, a hood at a front portion of the vehicle body, a protector provided to the vehicle body to protect an operator seat, and an obstacle detector to detect an obstacle, the obstacle detector being located in front of the protector outwardly in a width direction of the hood.

The present disclosure generally relates to an information system for a working machine, specifically adapted for projecting an image onto an area externally of the working machine based on construction data relating to a surrounding of the working machine. The present disclosure also relates to a corresponding method and computer program for such a working machine.

Methods and systems for communicating between autonomous vehicles are described herein. Such communication may be performed for signaling, collision avoidance, path coordination, and/or autonomous control. A computing device may receive data for the same road segment from autonomous vehicles, including (i) an indication of a location within the road segment, and (ii) an indication of a condition of the road segment. The computing device may generate, from the data for the same road segment, an overall indication of the condition of the road segment, which may include a recommendation to vehicles approaching the road segment. Additionally, the computing device may receive a request from a computing device within a vehicle approaching the road segment to display vehicle data. The overall indication for the road segment may then be displayed on a user interface of the computing device.

An auto clean machine, comprising: a light source configured to emit light to illuminate at least one light region outside and in front of the auto clean machine; a first image sensing area, configured to sense a first brightness distribution of the light region; a second image sensing area below the first image sensing area, configured to sense a second brightness distribution of the light region; and a processor, configured to control movement of the auto clean machine according the first brightness distribution and the second brightness distribution. The processor generates a wall detection result based on the first brightness distribution of the light region, generates a cliff detection result based on the second brightness distribution of the light region, and controls the movement of the auto clean machine according to the wall detection result and the cliff detection result.