Abstract: A method and an apparatus of processing traffic data, a device, a medium and a product are provided. The method of processing traffic data includes: acquiring initial traffic data for a vehicle, wherein the initial traffic data includes current position information of the vehicle, position information of a plurality of traffic objects and feature information; determining a relative positional relationship between the current position information of the vehicle and the position information of the plurality of traffic objects; and processing the initial traffic data based on at least one of the feature information and the relative positional relationship to acquire at least one traffic data set, so as to navigate the vehicle based on the at least one traffic data set.

Abstract: A processing apparatus is provided, to, for each candidate point-of-interest of at least one candidate point-of-interest identified for association with a road in a network of roads for determining a name for the road, generate first data indicative of a relationship relating to a distance between the candidate point-of-interest and a road segment of the road, generate second data indicative of a relationship between the road segment and a projection of the candidate point-of-interest in a direction of the road segment, and, if the first and second relationships satisfy a first and second conditions for association respectively, generate data indicative of the candidate point-of-interest being an associated point-of-interest, process data corresponding to the associated point-of-interest to extract name data indicative of a road name associated with the associated point-of-interest, and generate, based on the name data, data indicative of the name for the road.

Abstract: A system comprises a ship having one or more maneuvering units for controlling the speed and direction of the ship. The ship comprises a ship control unit configured to send signals to the one or more maneuvering units to maneuver the ship and a transmitter-receiver connected to the control unit and configured to transmit and receive information. The system comprises a remote-control center remote from the ship. The remote-control center comprises a transmitter-receiver 16 configured to receive situation information of the ship. The remote-control center comprises a remote-control unit configured to determine pilot-control information for maneuvering the ship based on the situation information and configured to transmit the pilot-control information to the ship via the transmitter-receiver of the remote-control center. The transmitter-receiver of the ship is configured to receive the pilot-control information.

Abstract: A hybrid vehicle, a control method therefor, and a storage medium which are capable of suppressing changes in an engine sound and vibration even when acceleration is performed at the time of increasing a required driving force are provided. In a hybrid vehicle capable of performing automated driving control for automatically controlling at least acceleration and deceleration of a vehicle, a rotational speed of the engine is gradually increased by a predetermined increase step while maintaining or decelerating a vehicle speed of the hybrid vehicle until an increase timing of a required driving force in a case where it is predicted that the required driving force is required to be increased while the automated driving control is performed, and at least the power generated by the generator is supplied to the motor at the increase timing of the required driving force.

Abstract: An automatic driving system includes an electronic control device configured to: detect a driving operation input amount during an automatic driving control for a vehicle; determine whether the driver is able to start manual driving during the automatic driving control for the vehicle; output a signal for performing switching from automatic driving to the manual driving based on a result of a comparison between the driving operation input amount and a driving switching threshold that is a threshold for the switching from the automatic driving to the manual driving; set the driving switching threshold to a first driving switching threshold when it is determined that the driver is able to start the manual driving; and set the driving switching threshold to a second driving switching threshold exceeding the first driving switching threshold when it is determined that the driver is not able to start the manual driving.

Abstract: A system is disclosed for generating a feature line, such as for lane marking data of a map database. The system, for example, determines connectivity data for the feature line based on at least one first point of the feature line and at least one second line, which is within a maximum distance from the at least one first point. Further, orientation data associated with the at least one first point and at least one second point associated with the at least one second line is generated, in response to determining the connectivity data for the feature line. Further, distance data associated with distance between the at least one first point and the at least one second point is determined to generate a connecting line for the feature line based on the at least one first point, the at least one second point, the orientation data and the distance data.

Abstract: A method for determining routes for test drives of autonomous vehicles, wherein a plurality of test features are to be encountered or avoided on the routes. The method includes determining a geographical area in which a test drive is to take place, determining a starting point, determining a destination point, determining the plurality of test features, which are relevant for the test drive, creating a logical expression which places the plurality of test features in a logical relationship to one another, determining, at least one route which are located within the geographical area, which have the starting point and the destination point and which satisfy the logical expression, using a map material for the area and positions or areas where the plurality of test features are located, and displaying a representation of the determined routes for a user for the purpose of selecting a selected route.

Abstract: Systems, methods, and non-transitory computer-readable media can determine sensor data captured by at least one sensor of a vehicle while navigating a road segment. A plurality of features describing the road segment can be extracted from the sensor data. A map representation of the road segment can be determined based at least in part on the sensor data and the plurality of features extracted from the sensor data, the map representation being determined as the vehicle navigates the road segment. While the map representation of the road segment is being determined, at least one scenario associated with the road segment can be determined based at least in part on the map representation and the plurality of features extracted from the sensor data.

Abstract: The present disclosure provides methods, devices and systems for route deviation quantification and vehicular route learning based thereon. In some examples, there is provided a method for route deviation quantification of a suggested route. The method comprises: obtaining a ground truth route based on a filtered trajectory, the filtered trajectory including an inferred location of origin and an inferred location of destination; obtaining a suggested route generated based on the inferred location of origin and the inferred location of destination; quantifying a deviation of the suggested route from the ground truth route by calculating an off course ratio based on a combined length of road segments in the suggested route that are matched to corresponding road segments in the ground truth route and a combined length of road segments in the ground truth route.

Abstract: The present disclosure relates to an apparatus and method for vehicle guidance. In an embodiment, the present disclosure relates to a method of vehicle guidance including determining a current lane-level position of a vehicle, comparing the determined current lane-level position of the vehicle to an anticipated lane-level position of the vehicle, the anticipated lane-level position of the vehicle corresponding to a lane-level position of the vehicle within a predetermined route of the vehicle, receiving, based upon the comparison, an input regarding an intended route of the vehicle, and generating, via processing circuitry, guidance information based on the received input regarding the intended route of the vehicle, wherein the receiving the input regarding the intended route of the vehicle includes determining alternate routes between a current position of the vehicle and a destination of the vehicle based on the determined current lane-level position of the vehicle.

Abstract: An intersection information distribution apparatus includes a plurality of image capturers, one or more first wireless communicators, a position estimator, and a controller. The plurality of image capturers capture images in and around an intersection. The one or more first wireless communicators perform directional communication with a second wireless communicator mounted on a vehicle. The position estimator estimates a position of the vehicle in and around the intersection from a communication state of the directional communication. The controller selects a piece of data from among pieces of data of the images captured by the plurality of image capturers on the basis of the estimated position of the vehicle. The one or more first wireless communicators transmit the selected piece of data to the second wireless communicator of the vehicle.

Abstract: A system for vehicle navigation may include a processor including a circuitry and a memory. The memory may include instructions that when executed by the circuitry cause the processor to receive navigational information associated with the vehicle including an indicator of a location of the vehicle, and determine target navigational map segments to retrieve from a map database. The map database may include stored navigational map segments each corresponding to a real-world area. The determination of the target navigational map segments may be based on the indicator of vehicle location and on map segment connectivity information associated with the stored navigational map segments. The instructions may also cause the processor to initiate downloading of the target navigational map segments from the map database, and cause the vehicle to navigate along a target trajectory included in one or more of the target navigational map segments downloaded from the map database.

Abstract: An autonomous driving system includes an electronic control unit configured to generate, based on a target route, a target path in a predetermined coordinate system and a speed plan specifying a passage time at a control point on the target path, and rebuild, based on an actual speed of a vehicle, the speed plan when an operation intervention is performed during autonomous driving performed by autonomous driving control for causing the vehicle to travel along the target path according to the speed plan, the operation intervention changing a braking force acting on the vehicle, and perform the autonomous driving control by using an actuator mounted on the vehicle.

Abstract: A map information correction method for correcting map information includes information of a lane boundary line, the method further including: detecting a position with respect to an own vehicle of a lane boundary line set in place on a road surface around the own vehicle; estimating an own position on a map of the own vehicle; and correcting, depending on the estimated own position and the detected position of the lane boundary line, a position of the lane boundary line included in the map information by, in a first region comparatively close to the own vehicle, a larger rotational correction amount than in a second region comparatively far from the own vehicle and, in the second region, a larger translational correction amount than in the first region.

Abstract: The present disclosure provides a method and an Internet of Things system for determining a government traffic route in a smart city. The method may include: receiving a query request for a preset route initiated by a user, transmitting the query request to the government traffic route determination management platform, generating a query instruction, issuing the query instruction to the government sensor network platform, sending the query instruction to corresponding object platform, obtaining the traffic data information according to the query instruction, uploading the traffic data information to the government traffic route determination management platform, determining the time when each preset route reaches the target location, and feeding back the time when each preset route reaches the target location to the citizen user platform through the government service platform.

Abstract: A method for controlling at least one industrial truck comprising determining a driving job using a superordinate control unit and sending the driving job from the superordinate control unit to a transceiver of the at least one industrial truck. The driving job is transmitted from the transceiver to a vehicle controller. A position of the at least one industrial truck within a previously known route to be driven is determined via the superordinate control unit. An upcoming driving situation is identified using the superordinate control unit and is based on the position of the at least one industrial truck and the driving job. A protective field is generated with a collision protection apparatus, wherein the protective field is monitored by the at least one industrial truck based on the upcoming driving situation even before the at least one industrial truck reaches the driving situation.

Abstract: A method for landmark-based localization of a vehicle involves forming a plurality of position hypotheses for a vehicle position based on a forming of associations between sensor landmark objects detected by sensor and map landmark objects stored in a digital map. A most likely vehicle position is ascertained as the localization result on the basis of a probabilistic filtering of the position hypotheses, and a guaranteed position area is ascertained, in which a predefined error upper limit is not exceeded. This is performed several times with different set-ups of the probabilistic filtering. The localization result with the smallest guaranteed position area is selected as vehicle position if the guaranteed position areas overlap fully in pairs.

Abstract: The present technology pertains to visualization of maps used for autonomous vehicle applications. These maps include many layers of dense information. Each layer can be stored in a vector format and small portions, called tiles, of a map can be requested by a users web browser. The web browser can filter for desired layers and render all desired map layers at once to provide visualization of map and layers of interest.

Abstract: Aspects of the disclosure relate to generating map data. For instance, data generated by a perception system of a vehicle may be received. This data corresponds to a plurality of observations including observed positions of a passenger of the vehicle as the passenger approached the vehicle at a first location. The data may be used to determine an observed distance traveled by a passenger to reach a vehicle. A road edge distance between an observed position of an observation of the plurality of observations and a nearest road edge to the observed position may be determined. An inconvenience value for the first location may be determined using the observed distance and the road edge distance. The map data is then generated using the inconvenience value.

Abstract: A global path planning method and device for an unmanned vehicle are disclosed. The method comprises: establishing an object model through a reinforcement learning method, wherein the object model includes: a state of the unmanned vehicle, an environmental state described by a map picture, and an evaluation index of a path planning result; building a deep reinforcement learning neural network based on the object model established, to obtain a stable neural network model; inputting the map picture of the environment state and the state of the unmanned vehicle into the deep reinforcement learning neural network after trained, and generating a motion path of the unmanned vehicle. According to the present disclosure, the environment information in the scene is marked through the map picture, and the map features are extracted through the deep neural network, thereby simplifying the modeling process of the map scene.