Abstract: An embodiment pallet stacking apparatus includes a lidar installed in an unmanned forklift and configured to radiate laser light and convert range data reflected from a first cup-feet pallet to 3D point cloud data, a bird's eye view (BEV) conversion unit configured to convert the 3D point cloud data to a 2D BEV image, a cup recognition unit configured to perform channel normalization with input data of the 2D BEV image and recognize a cup position through calculation using convolutional neural network, a forklift controller configured to control operation of the unmanned forklift according to a control signal, and a control unit configured to identify a difference between a cup position of the first cup-feet pallet and a cup position of a stationary second cup-feet pallet and apply a control signal for adjustment to a matching position to the forklift controller.

Abstract: Disclosed are various embodiments for implementing passenger profiles for autonomous vehicles. A passenger of the autonomous vehicle is identified. A passenger profile corresponding to the passenger and comprising a passenger preference is identified. The passenger preference is identified. A configuration setting of the autonomous vehicle corresponding to autonomous operation of the autonomous vehicle is then adjusted based at least in part on the passenger preference.

Abstract: A drive system of an electric vehicle includes: an internal combustion engine; a generating motor provided so as to be able to generate electricity by receiving motive force from the internal combustion engine; and a travel motor provided so as to be able to be driven by electric power generated by the generating motor, and the drive system is configured so as to be able to switch a series hybrid mode and an engine direct connected mode, the series hybrid mode being a mode in which, while the internal combustion engine and the drive wheel are linked via a first clutch so as to be unlinkable/linkable, the travel motor and the drive wheel are linked via a second clutch different from the first clutch so as to be unlinkable/linkable, and the travel motor is used as a drive power source such that the electric vehicle travels by transmitting motive force from the travel motor to the drive wheel, the engine direct connected mode being a mode in which at least one of the internal combustion engine and the generator

Abstract: Route provisioning techniques for an autonomous driving feature of a vehicle include receiving a user-generated route generated by a user using a second map database that differs from a first map database of the vehicle that stores a road graph specifying nodes and links representative of a map of roads proximate to the vehicle and specifies an array of geo-points between a current geo-location of the vehicle and a final geo-location for the vehicle, determining, using the road graph maintained by the first map database, a set of valid routes from the current geo-location to the final geo-location ordered based on relative likelihood, determining a best route from the set of valid routes based on a weighted comparison between each route of the set of valid routes and the user-generated route, and utilizing the selected route for control of the autonomous driving feature.

Abstract: Systems and methods for mapping trip trajectories include identifying one or more vehicle trips (e.g., aircraft flights). One or more trajectory configurations associated with at least a portion of each vehicle trip can be identified, and location data associated with the one or more vehicle trips can be requested. Trip trajectory data based at least in part from the location data associated with the one or more vehicle trips and the one or more trajectory configurations associated with at least a portion of each vehicle trip can be generated. The trip trajectory data can be provided for display on a map of a geographic area including one or more locations defined by the location data associated with the one or more vehicle trips.

Abstract: A server device 2 stores a distribution map DB 21 including autonomous driving regulatory information Ir for regulating autonomous driving of a vehicle in a predetermined section, and sends map data D1 including the autonomous driving regulatory information Ir to a driving assistance device 1. Then, the driving assistance device 1 receives the map data D1 including the autonomous driving regulatory information Ir and controls the autonomous driving of the vehicle based on the received autonomous driving regulatory information Ir.

Abstract: A system and method includes a wireless receiver and/or receiver-transceiver integrated into one of a powertrain electrical system, ECU, MCU, and vehicle speed output sensors of the high-performance vehicle, a speed command control device integrated into the wireless receiver and/or receiver-transceiver to automatically turn ON at a predetermined vehicle speed, and a transmitter and/or transmitter-transceiver operable by law enforcement to transmit RF messaging packets to the wireless receiver and/or receiver-transceiver when the speed command control device is activated by the transmitter and/or transmitter-transceiver to reduce the speed of the high-performance vehicle. The method may implement Vehicle Speed Ship Mode (VSSM) with the same, similar, or different RF receiver and/or receiver-transceiver by turning ON the speed bit indefinitely by use of custom factory software and or factory tool.

Abstract: Provided is a device and a method for route planning. The route planning device (100) may include a data interface (128) coupled to a road and traffic data source (160); a user interface (170) configured to display a map and receive a route planning request from a user, the route planning request including a line of interest on the map; a processor (110) coupled to the data interface (128) and the user interface (170). The processor (110) may be configured to identify the line of interest in response to the route planning request; acquire, via the data interface (128), road and traffic information associated with the line of interest from the road and traffic data source (160); and calculate, based on the acquired road and traffic information, a navigation route that matches or corresponds to the line of interest and meets or satisfies predefined road and traffic constraints.

Abstract: A method for operating a higher-level automated vehicle (HAV), in particular a highly automated vehicle, is provided, including: S1 for providing a digital map, which may be a highly accurate digital map, in a driver assistance system of the HAV; S2 for determining an instantaneous vehicle position and localizing the vehicle position in the digital map; S3 for providing an expected setpoint traffic density at the vehicle position; S4 for ascertaining an instantaneous actual traffic density in the surroundings of the HAV; S5 for comparing the actual traffic density to the setpoint traffic density and ascertaining a difference value as the result of the comparison; S6 for checking the vehicle position of the HAV for plausibility at least partially based on the difference value and/or S7 for updating the digital map at least partially based on the difference value. Also described are a corresponding driver assistance system and a computer program.

Abstract: A first method of predicting the range of an electric vehicle comprises, determining a range value during a current vehicle operating cycle using a first range model, wherein the first range model is dependent on an energy consumption rate value recorded during a previous vehicle operating cycle. A second method of predicting the range of an electric vehicle comprises, monitoring a trailer detecting means of the vehicle; and determining a first range value if the trailer detecting means detects that a trailer is attached to the vehicle.

Abstract: An autonomous driving vehicle provides a driverless transportation service for a user. An alarming phenomenon is a natural phenomenon that potentially causes a disaster. The autonomous driving vehicle recognizes, based on driving environment information, the alarming phenomenon at a current location of the autonomous driving vehicle or on a planned travel route from the current location to a destination. When recognizing the alarming phenomenon, the autonomous driving vehicle determines whether to continue or halt vehicle travel control in accordance with a current travel plan. When determining to halt the vehicle travel control in accordance with the current travel plan, the autonomous driving vehicle sets an emergency plan depending on a type of the alarming phenomenon and controls the autonomous driving vehicle in accordance with the emergency plan.

Abstract: A computer-implemented method for providing landmark-assisted navigation guidance by selectively utilizing database information includes receiving navigation requests from one or more mobile computing devices, each of the requests being associated with a respective starting point, destination, and travel mode. For each navigation request, a corresponding route to be depicted in a corresponding digital map is determined. For each navigation request associated with a first travel mode, corresponding points of interest (POIs) are determined from among a plurality of POIs stored in a database. The corresponding POIs are determined based at least on whether the corresponding POIs are associated with any landmark category. For each navigation request associated with a second travel mode, corresponding POIs are determined irrespective of whether the corresponding POIs are associated with any landmark categories.

Abstract: A vehicle controller determines whether the position of a detected travel lane relative to an edge of a road being traveled by a vehicle may differ from an actual position; estimates a first required time for an action, which is required before the vehicle reaches a predetermined location, for the case that the position of the detected lane relative to the edge of the road is correct; estimates a second required time for the action for the case that the position of the detected lane differs from the actual position by a predetermined number of lanes; and sets start timing of the action, based on the second required time, so that the action will finish before the vehicle reaches the predetermined location, when the position of the detected lane may differ from the actual position and the second required time is longer than the first required time.

Abstract: An autonomous driving vehicle provides a driverless transportation service for a user. An alarming phenomenon is a natural phenomenon that potentially causes a disaster. The autonomous driving vehicle recognizes, based on driving environment information, the alarming phenomenon at a current location of the autonomous driving vehicle or on a planned travel route from the current location to a destination. When recognizing the alarming phenomenon, the autonomous driving vehicle determines whether to continue or halt vehicle travel control in accordance with a current travel plan. When determining to halt the vehicle travel control in accordance with the current travel plan, the autonomous driving vehicle sets an emergency plan depending on a type of the alarming phenomenon and controls the autonomous driving vehicle in accordance with the emergency plan.

Abstract: A computer-implemented method for securing unmanned aerial system (UAS) operations includes receiving a UAS flight plan for a UAS and a UAS operation, the UAS flight plan including a flight profile and flight path for the UAS; determining a mission type for the UAS operation requires use of dummy aircraft information; and assigning a dummy UAS identification for the UAS. Generating dummy airframe information, including dummy airframe characteristics and performance data, for the UAS, includes generating dummy airframe information that corresponds to airframe information for an actual civil aircraft that could follow the received UAS flight plan. The method further includes causing the UAS to broadcast the dummy UAS identification and the dummy airframe information with an automatic dependent surveillance-broadcast signal during at least a portion of the UAS operation.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Various technologies described herein pertain to controlling an autonomous vehicle to provide indicators to distinguish the autonomous vehicle from other autonomous vehicles in a fleet. The autonomous vehicle includes a vehicle propulsion system, a braking system, a notification system, and a computing system. The notification system outputs an indicator that is perceivable external to the autonomous vehicle. The computing system receives data specifying an identity of a passenger to be picked up by the autonomous vehicle. Moreover, the computing system controls at least one of the vehicle propulsion system or the braking system to stop the autonomous vehicle for passenger pickup. Further, the computing system controls the notification system to output the indicator; a characteristic of the indicator outputted by the notification system is controlled based on the identity of the passenger to be picked up and whether the autonomous vehicle is stopped for passenger pickup.

Abstract: An approach is provided for providing a map matcher tolerant to wrong map features. The approach involves, for instance, finding a segment of a probe trajectory containing a plurality of low-speed probe points. The approach also involves forming a line between a first and a last probe point of the segment. The approach further involves calculating a distance from each probe point of the segment to the line. The approach further involves splitting the segment based on comparing the distance to a threshold at a turning point of the segment. The approach further involves creating a new probe trajectory based on a plurality of high-speed probe points in the probe trajectory and the turning point. The approach further involves estimating a heading of the turning point of the segment based on the new trajectory, and then performing a feasibility check between two consecutive probe points of the new trajectory.

Abstract: Embodiments relate to transportation systems. More particularly, embodiments relate to methods and systems of vehicle data collection by a user having a mobile device. In a particular embodiment, vehicle data (also termed herein “driving data” or “data”) is collected, analyzed and transformed, and combinations of collected data and transformed data are used in different ways, including, but not limited to, predicting, detecting, and reconstructing vehicle accidents.

Abstract: An occlusion detection system for an autonomous vehicle is described herein, where a signal conversion system receives a three-dimensional sensor signal from a sensor system and projects the three-dimensional sensor signal into a two-dimensional range image having a plurality of pixel values that include distance information to objects captured in the range image. A localization system detects a first object in the range image, such as a traffic light, having first distance information and a second object in the range image, such as a foreground object, having second distance information. An occlusion polygon is defined around the second object and the range image is provided to an object perception system that excludes information within the occlusion polygon to determine a configuration of the first object. A directive is output by the object perception system to control the autonomous vehicle based upon occlusion detection.