Abstract: In a predictive path planning functionality for a transportation vehicle data describing a roadway are acquired by one or more sensors disposed on the transportation vehicle and analyzed. One or more predicted paths are predicted with at least one neural network. Data describing a reference path taken by the transportation vehicle are acquired. The one or more predicted paths are evaluated to detect one or more errors in the predicting operation wherein the one or more errors include an indication that the one or more predicted paths of the path planning system deviate from the reference path.

Abstract: A method and system for deploying a calibrated positioning map of an area. The method, executed in a processor of a server computing device, comprises deploying a crowd sourced magnetic fingerprint map for mobile device localization during traversal of the indoor area, accumulating received signal strength (RSS) measurements in forming a RSS fingerprint map using crowd sourced RSS measurements during the deploying, and switching, responsive to identifying desirability of a change in the mobile device localization within the indoor area, from the crowd sourced magnetic fingerprint map to the RSS fingerprint map as basis for localizing the mobile device.

Abstract: Provided are a mobile object control apparatus and a mobile object control method that provide an improved method of presenting a travel route during movement of the mobile object. A degree of relation with an individual in the vicinity of a mobile object is decided, and a travel mode that is associated with a process performed by a driving section that moves the mobile object and a process performed by an output section that outputs a representation for presenting a travel route of the mobile object is set on the basis of the degree of relation.

Abstract: A control device including: a determination unit that determines whether or not a malfunction has occurred in a monitoring target on the basis of a state of the monitoring target; an arithmetic unit that is capable of reconfiguring a function; a storage unit that stores software used for causing the arithmetic unit to reconfigure a function relating to an operation of the monitoring target; and a processing unit that reads the software from the storage unit and reflects the software on the arithmetic unit in a case in which it is determined by the determination unit that a malfunction has occurred in the monitoring target.

Abstract: Provided is an information processing device that includes a planning unit that sets a joining route on the basis of information about at least one of a planned route of a user mobile unit that uses a service or a planned route of a provider mobile unit that provides the service, the joining route being a zone for the user mobile unit and the provider mobile unit to receive and provide the service, and makes a movement plan including at least one of a route plan for the user mobile unit or a route plan for the provider mobile unit based on the joining route. The information processing device further includes a service provision unit that controls provision of the service so that the user mobile unit and the provider mobile unit receive and provide the service while moving on the joining route.

Abstract: A storage device (301) stores a failure correspondence table (405) that indicates a countermeasure for a time when a failure occurs in an apparatus mounted on a vehicle (100). A failure countermeasure update unit (404) updates the countermeasure indicated in the failure correspondence table (405) in accordance with a change in a traveling environment of the vehicle (100).

Abstract: A mobile terminal to be carried by a user of a vehicle acquires a captured image of the vehicle, acquires distance information on a distance to the vehicle based on the captured image, determines whether the distance to the vehicle is within a predetermined allowable distance based on the distance information, and transmits an operation signal corresponding to an operation content input by a user to the vehicle when the distance to the vehicle is determined to be within the allowable distance.

Abstract: Systems and methods for vehicle spatial path sampling are provided. The method includes obtaining an initial travel path for an autonomous vehicle from a first location to a second location and vehicle configuration data indicative of one or more physical constraints of the autonomous vehicle. The method includes determining one or more secondary travel paths for the autonomous vehicle from the first location to the second location based on the initial travel path and the vehicle configuration data. The method includes generating a spatial envelope based on the one or more secondary travel paths that indicates a plurality of lateral offsets from the initial travel path. And, the method includes generating a plurality of trajectories for the autonomous vehicle to travel from the first location to the second location such that each of the plurality of trajectories include one or more lateral offsets identified by the spatial envelope.

Abstract: The systems and methods described herein disclose detecting obstacles in a vehicular environment using host vehicle input and associated trust levels. As described here, measured vehicles, either manual or autonomous, that detect an obstacle in the environment will operate to respond to the obstacle. As such, those movements can be used to determine if an obstacle exists in the environment, even if the obstacle cannot be detected directly. The systems and methods can include a host vehicle receiving prediction data about an evasive behavior from one or more measured vehicles in a vehicular environment. A trust level can then be established for the measured vehicles. An obscured obstacle can be determined using the evasive behavior and the trust level which can then be mapped in the vehicular environment. A guidance input can then be created for the host vehicle using the obscured obstacle and the trust level.

Abstract: Systems and methods for vehicle motion control with interactive object annotation are provided. A method can include obtaining data indicative of a plurality of objects within a surrounding environment of the autonomous vehicle. For example, the plurality of objects can include at least at one problem object encountered by the autonomous vehicle while navigating a planned route. The method can include determining a group of objects of the plurality of objects. For example, the group of objects can include the problem object and one or more other objects in proximity to the problem object. The method can include determining a classification update to be applied to the group of objects. The method can include applying the classification update to the group of objects. The method can include providing data indicative of the classification update for the group of objects to the autonomous vehicle for use in motion planning.

Abstract: An air purifying system includes a robot cleaner configured to generate map information during vacuum cleaning and at least one air purifier that is operated based on the generated map information. Air purifying is performed using updated map information acquired by the robot cleaner during cleaning. The at least one air purifier includes two portable air purifiers mounted on a base air purifier. The two portable air purifiers are configured to be lifted from the base air purifier and moved to separate indoor rooms. The base air purifier includes a controller that determines, based on the generated map information and other information, which rooms the two portable air purifiers should be placed in so that air purifying may be efficient.

Abstract: A method preprocesses a set of non-scheduled lines within a multimodal transportation network of predetermined stations, by (a) for each non-scheduled line (l) of the set of non-scheduled lines, associating, to each station (plj) of a sequence of stations ({right arrow over (p)}(l)) defining the non-scheduled line (l), at least one time interval (I(l,j)) during which a trip on the non-scheduled line (l) can depart from the station (plj); (b) for each first station (plj) of a non-scheduled line (l) which is reachable from a second station (pti) of a scheduled line, adding to a set of feasible transfers between a scheduled line and a non-scheduled line, if there exists a trip (t) on the scheduled line such that a departure time at the first station (plj) after transferring is compatible with the at least one time interval (I(l,j)) associated to the first station (plj), the earliest transfer from the second station (pti) on the trip (t) to the first station (plj); and (c) outputting the set of feasible transfers

Abstract: A circle assembly for supporting implement of motor grader is provided. The circle assembly rotates relative to drawbar about rotation axis. The circle assembly includes plate and rim member coupled to plate. The plate defines front section and rear section and includes a ring gear portion defining a plurality of teeth formed integrally therein. A first skirt portion and a second skirt portion integrally and contiguously extend from ring gear portion at rear section. The first skirt portion and second skirt portion are angled relative to ring gear portion in direction of rotation axis. The rim member includes first arm member and second arm member attached to first skirt portion and second skirt portion, respectively, and a circumferential belt portion extending between the first arm member and the second arm member. The belt portion is spaced from and surrounds ring gear member at the front section of the plate.

Abstract: An autopilot recoupling system for a rotorcraft having an automatic flight control system with multiple layers of flight augmentation. The autopilot recoupling system includes an autopilot recoupling input operable to generate an autopilot recoupling signal. An autopilot recoupling signal processor is communicably coupled to the autopilot recoupling input. The autopilot recoupling signal processor is configured to receive the autopilot recoupling signal from the autopilot recoupling input and responsive thereto, determine a state of the automatic flight control system, activate a trim systems layer of the automatic flight control system if the trim systems layer is not active, engage an attitude retention systems layer of the automatic flight control system if the attitude retention systems layer is disengage and recouple an autopilot systems layer of the automatic flight control system.

Abstract: Provided are a pass route planning method and apparatus, a device and a readable storage medium. The method includes: obtaining current road environment information in front of a vehicle; determining a current passable area according to the current road environment information; setting pass candidate points in the current passable area; and determining a current optimal passable route according to the pass candidate points. Firstly, the passable area is determined, and then the pass candidate points are set in the passable area, and thus the current optimal passable route is calculated according to the pass candidate points in the passable area, which can effectively reduce a calculation amount of unreachable pass routes in the prior art in which candidate points are set in all areas directly in front of an unmanned vehicle, thereby reducing a calculation amount for planning a pass route and improving a planning speed of the pass route.

Abstract: The Automated Wayside Asset Monitoring system utilizes a camera-based optical imaging device and an image database to provide intelligence, surveillance and reconnaissance of environmental geographical information pertaining to railway transportation. Various components of the Automated Wayside Asset Monitoring system can provide features and functions that can facilitate the operation and improve the safety of transportation via a railway vehicle.

Abstract: Various embodiments include a system for determining a risk of an accident on a driving route comprising: a memory storing route risk parameters of the driving route, wherein at least one of the route risk parameters is assigned to route sections of the driving route, wherein each of the route risk parameters contains information relevant for road safety on the respective route section; and a processor in communication with the memory, programmed to determine a respective risk factor for each of the route risk parameters, wherein the respective risk factor indicates a measure of a risk of an accident on the respective assigned route section. The processor determines a total risk index of the driving route based on the respective risk factor of the respective risk parameter assigned to the respective route section, wherein the total risk index measures risk of an accident on the driving route.

Abstract: A vehicular control system includes a plurality of sensors disposed at a vehicle and sensing exterior of the vehicle. An electronic control unit (ECU) includes a processor that processes sensor data captured by the sensors. The ECU, responsive at least in part to processing of captured sensor data as the vehicle travels in a traffic lane of a multi-lane road, determines a rearward approaching vehicle rearward of the equipped vehicle that is in an adjacent traffic lane. The ECU determines a leading vehicle ahead of the equipped vehicle and traveling in the same traffic lane as the equipped vehicle. The ECU controls the equipped vehicle to accelerate the vehicle to at least match the speed of the determined rearward approaching vehicle and to maneuver into the adjacent traffic lane to pass the determined leading vehicle ahead of the determined rearward approaching vehicle.

Abstract: The machine learning device includes a predicting part configured to use a machine learning model to predict predetermined information, an updating part configured to update the machine learning model, and a part information acquiring part configured to detect replacement of a vehicle part and acquire identification information of the vehicle part after replacement. The updating part is configured to receive a new machine learning model trained using training data sets corresponding to the vehicle part after replacement from a server and apply the new machine learning model to the vehicle, if a vehicle part relating to input data of the machine learning model is replaced with a vehicle part of a different configuration.

Abstract: A control unit of a server device as an information processing apparatus is configured to: provide first information on a service for each of a plurality of moving objects, each of which is configured to provide a different service and autonomous travel; acquire second information on time and location of at least one service that a user wants to use; and determine at least one moving object that matches with the user based on the first information and the second information, and generate a travel plan of the at least one moving object.