Abstract: A vehicle driving assist apparatus includes a first unit to set a degree of acceleration suppression to suppress acceleration of a vehicle, a controller to suppress target acceleration of the own vehicle based on the degree of acceleration suppression, a storage to store road map information, an estimator to estimate a vehicle position, a detector to map-match the estimated vehicle position to the road map information and detect whether there is a crossing road ahead of the vehicle, a second setting unit to set, when the detector detects the crossing road, a traveling-prohibited region on an opposite side of the vehicle on the road map information, and a calculator to calculate a reaching distance from the vehicle to the region. The degree of acceleration suppression is set to a higher value as the reaching distance becomes shorter.

Abstract: Autonomous vehicles are requested to execute a route from an origin location to a destination location. The route specifies one or more waypoints between the origin location and the destination location, with the autonomous vehicle requested to transit from the origin location to the destination location via the waypoints. Some autonomous vehicles vary their route and do not necessarily visit each of the specified waypoints along the route. To improve adherence to the waypoints specified in the route, an arranger assigns a weight to each of the waypoints. The weight is used to score the vehicle's performance of the route based at least in part on whether the vehicle visited each of the waypoints and the weight associated with each of the waypoints. The score is used to control dispatch of additional service requests to the autonomous vehicle or other autonomous vehicles operated by the same operator or manufacturer.

Abstract: A system for controlling an autonomous vehicle when the autonomous vehicle is outside of its operational design domain. The system includes an environment detection system, a vehicle control system, and a first electronic processor. The first electronic processor is configured to detect that an autonomous vehicle is outside of its operational design domain and send a first electronic message. The first electronic message requests that a surrounding vehicle lead the autonomous vehicle until the autonomous vehicle returns to its operational design domain or reaches a predetermined location. The electronic processor is also configured to determine a leading vehicle and control the autonomous vehicle to follow the leading vehicle until the autonomous vehicle returns to its operational design domain or reaches the predetermined location.

Abstract: An example operation includes one or more of determining, by a transport at a first location, that the transport is not in use, determining, by the transport, a second location to transfer energy stored in the transport, maneuvering, by the transport, to the second location, discharging, by the transport, the stored energy to the second location, and maneuvering, by the transport, to the first location.

Abstract: A location and governance system and method for light electric vehicles that includes on-board sensors and receivers for providing readings used to compute absolute and relative vehicle position information, and combining the absolute and relative position information to compute a determined vehicle position, and a current surface type being traveled on by the vehicle. Governance commands for the vehicle can be generated based on the current surface type. Positioning system receivers, inertial measuring units, cameras and other sensor can be used. Vibration analysis, image processing, transition detection and other methods can be used to determine vehicle position and surface type, and spatial databases and other resources can be used. Determining a current surface type the vehicle is travelling on can include determining whether the vehicle is traveling on a sidewalk.

Abstract: A method for training an autonomous vehicle to reach a target location. The method includes detecting the state of an autonomous vehicle in a simulated environment, and using a neural network to navigate the vehicle from an initial location to a target destination. During the training phase, a second neural network may reward the first neural network for a desired action taken by the autonomous vehicle, and may penalize the first neural network for an undesired action taken by the autonomous vehicle. A corresponding system and computer program product are also disclosed and claimed herein.

Abstract: Generating a map associated with an environment may include collecting sensor data received from one or more vehicles and generating a set of links to align the sensor data. A mesh representation of the environment may be generated from the aligned sensor data. A system may determine a proposed link to add, a proposed link deletion, and/or a proposed link alteration, and receive a modification comprising instructions to add, delete, or modify a link. Responsive to receiving a modification, the system may re-align a window of sensor data associated with the modification. The modification and/or sensor data associated therewith may be collected as training data for a machine learning model, which may be trained to generate link modification proposals and/or determine sensor data that may be associated with a poor sensor data alignment.

Abstract: A method includes receiving, by machine-learning logic, observations indicative of a states associated with a first and second group of vehicles arranged within an engagement zone during a first interval of an engagement between the first and the second group of vehicles. The machine-learning logic determines actions based on the observations that, when taken simultaneously by the first group of vehicles during the first interval, are predicted by the machine-learning logic to result in removal of one or more vehicles of the second group of vehicles from the engagement zone during the engagement. The machine-learning logic is trained using a reinforcement learning technique and on simulated engagements between the first and second group of vehicles to determine sequences of actions that are predicted to result in one or more vehicles of the second group being removed from the engagement zone. The machine-learning logic communicates the plurality of actions to the first group of vehicles.

Abstract: A system for determining a travel direction that avoids objects when a vehicle travels from a current location to a target location is provided. The system determines a travel direction based on an attract-repel model. The system assigns a repel value to the object locations and an attract value. A repel represents a magnitude of a directional repulsive force, and the attract value represents the magnitude of a directional repulsive force. The system calculates an attract-repel field having an attract-repel magnitude and attract-repel direction for the current location based on the repel values and their directions and the attract value and its direction. The system then determines the travel direction for a vehicle to be the direction of the attract-repel field at the current location.

Abstract: A method, system and computer program product for determining a map position of an ego-vehicle are disclosed. The method includes acquiring map data comprising a road geometry, initializing at least one dynamic landmark by measuring a position and velocity, relative to the ego-vehicle, of a surrounding vehicle, and determining a first map position of the surrounding vehicle based on this measurement and the geographical position of the ego-vehicle. Further, the method includes predicting a second map position of the surrounding vehicle, and measuring a location, relative to the ego-vehicle, of the surrounding vehicle when it is estimated to be at the second map position, whereby the geographical position of the ego-vehicle can be computed and updated.

Abstract: A method of collision warning using broad antenna pattern ultra-wide band (UWB) radar includes emitting a first radar ping from a broad beam UWB antenna and receiving a first return signal identifying an object. A first hemisphere with a first radius is determined for the object. A second ping, second return and second hemisphere is defined for the object. At the intersection of the hemispheres, an object ring is defined. The radius of the object ring is compared with the radius of a collision cylinder (e.g., representing a safe distance around a system or device, such as a drone). The object may be identified as posing a collision threat when the radius of the object ring is smaller than the radius of the collision cylinder.

Abstract: A device includes an external sensor to scan an environment so as to periodically output scan data, a storage to store an environmental map, and a location estimation device to match the sensor data against the environmental map read from the storage so as to estimate a location and an attitude of the vehicle. The location estimation device determines predicted values of a current location and a current estimation of the vehicle in accordance with a history of estimated locations and estimated attitudes of the vehicle, and performs the matching by using the predicted values.

Abstract: Described herein are embodiments of a method and system that uses a vertical or upward facing imaging sensor to compute vehicle attitude, orientation, or heading and combines the computed vehicle attitude, orientation, or heading with range bearing measurements from an imaging sensor, LiDAR, sonar, etc., to features in the vicinity of the vehicle to compute accurate position and map estimates.

Abstract: Provided is a map generation unit that generates an attribute-attached occupancy grid map including an existence probability of an obstacle in a space around a moving body for each grid, and an attribute of the obstacle labelled in the occupancy grid map. A position estimation unit that estimates a position of the moving body by matching in a shape of a non-moving body and the attribute between the attribute-attached occupancy grid map and a pre-map that is the attribute-attached occupancy grid map prepared beforehand.

Abstract: A method for updating a high definition map according to one embodiment comprises: obtaining a two-dimensional image that captures a target area corresponding to at least a part of an area expressed by a three-dimensional high definition map, generating a three-dimensional local landmark map of the target area from a position of a landmark in the two-dimensional image, based on a position and an orientation of a photographing device which has captured the two-dimensional image and updating the high definition map with reference to the local landmark map corresponding to the target area of the three-dimensional high definition map.

Abstract: A submersible remotely operable vehicle used for inspection of liquid cooled electrical transformers can include a number of separate cameras and sensors for mapping and navigating the internal structure of the transformer with liquid coolant remaining in the transformer. The remotely operable vehicle can be wirelessly controlled to perform various inspection functions while the number of cameras provide video streams for processing to produce a three dimensional field of view based on an observation position of the remotely operable vehicle.

Abstract: The disclosure relates to a method for determining correction values for a number of sensors of a traveling motor vehicle. The method being based on backward calculation. The disclosure further relates to a method for determining a position of a motor vehicle, using the correction values. The disclosure also relates to an associated electronic control device and to an associated non-volatile computer-readable storage medium.

Abstract: A main server for controlling laser irradiation of a movement path of a robot, the main server including a communication unit configured to communicate with a camera module and a laser irradiation module; and a controller configured to: receive, via the communication unit, an image of a robot captured by the camera module, identify a location of the robot in the image captured by the camera module, generate a movement path of the robot based on sensing information, and transmit, via the communication unit, movement path information to the laser irradiation module for outputting the movement path to a vicinity of the robot with a laser for the robot to follow, in which the sensing information includes first information about an obstacle sensed by the camera module or second information about the obstacle sensed by the laser irradiation module.

Abstract: Embodiments of this application describe a motor vehicle self-driving method and a terminal device. The method may include obtaining, by a terminal device, vehicle external-environment data of a position of a motor vehicle and initial positioning precision of the motor vehicle. The method may also include determining, by the terminal device, a target driving parameter of the motor vehicle based on the vehicle external-environment data and the initial positioning precision. Furthermore, the method may include controlling, by the terminal device, the motor vehicle to drive based on the target driving parameter. In the embodiments of this application, the terminal device determines the target driving parameter of the motor vehicle based on the vehicle external-environment data and the initial positioning precision. In this way, the target driving parameter varies with the vehicle external-environment data, and further matches an external environment, thereby improving self-driving safety of the motor vehicle.

Abstract: The present disclosure relates to a method for driving on the basis of characteristics of a driving surface, and a robot for implementing the same, and a method for driving on the basis of characteristics of a driving surface, according to one embodiment of the present disclosure, comprises the steps in which: a sensing module of the robot senses an adjacent driving surface to generate characteristic information of the driving surface, and a control unit of the robot stores position and characteristic information of the driving surface in a map storage of the robot; the controller of the robot sets a function to be applied to the driving surface in response to the characteristic information of the driving surface, or generates a movement path selectively including the driving surface corresponding to start and end points of the robot; and the controller controls a moving unit and a functional unit of the robot according to the set function or the movement path.