Abstract: A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

Abstract: A system includes a vehicle control module, a vehicle gateway module, and a wired vehicle communications network communicatively coupling the vehicle gateway module to the vehicle control module. The vehicle control module is programmed to receive sensor data from at least one sensor, execute a machine-learning program trained to determine whether the sensor data satisfies at least one criterion, and transmit the sensor data satisfying the at least one criterion to the vehicle gateway module. The vehicle gateway module is programmed to transmit the machine-learning program to the vehicle control module; upon receiving the sensor data from the vehicle control module, store the sensor data; and upon establishing a connection with a remote server, transmit the sensor data to the remote server.

Abstract: A computer-implemented method for using machine learning to determine control parameters for a control system, in particular of a motor vehicle, in particular for controlling a driving operation of the motor vehicle. The method includes: providing a set of travel trajectories; deriving reward functions from the travel trajectories, using an inverse reinforcement learning method; deriving driver type-specific clusters based on the reward functions; determining control parameters for a particular driver type-specific cluster.

Abstract: A method for adaptive mission execution by an unmanned aerial vehicle includes receiving a set of pre-calculated mission parameters corresponding to an initial UAV mission; collecting UAV operation data during flight of the unmanned aerial vehicle; calculating a set of modified mission parameters from the set of pre-calculated mission parameters and the UAV operation data, the set of modified mission parameters corresponding to a modified UAV mission; and executing the modified UAV mission on the unmanned aerial vehicle.

Abstract: When a vehicle control interface receives information indicating “Standstill” from a VP, the vehicle control interface sets a value 2 in a signal indicating an actual moving direction (a signal indicating a moving direction of a vehicle). When the number of wheels rotating in a forward rotation direction is larger than two, the vehicle control interface sets a value 0 in the signal indicating the actual moving direction. When the number of wheels rotating in a reverse rotation direction is larger than two, the vehicle control interface sets a value 1 in the signal indicating the actual moving direction. When the number of wheels rotating in the forward rotation direction is equal to the number of wheels rotating in the reverse rotation direction, the vehicle control interface sets a value 3 in the signal indicating the actual moving direction. The vehicle control interface provides the signal indicating the actual moving direction to an ADK.

Abstract: A method for determining a slip angle ?r of a rear wheel of a single-track motor vehicle for the purpose of traction control of the rear wheel of the single-track motor vehicle by means of a closed loop control is provided. The slip angle ?r of the rear wheel is determined as a feedback value of the closed loop using at least one of three model-based steps. A slip angle ?r1, ?r2 or ?r3 is determined by one of the three steps representing the slip angle ?r or the slip angle ?r is determined from at least two of the slip angles ?r1, ?r2 and ?r3.

Abstract: An intelligent driving method comprising: obtaining feature parameters of a vehicle at a current moment and a road attribute of a driving scenario of the vehicle in a preset future time period; comparing the feature parameters at the current moment with feature parameters of a standard scenario in a scenario feature library; comparing the road attribute of the driving scenario of the vehicle in the preset future time period with a road attribute of the standard scenario in the scenario feature library; determining a total similarity of each scenario class to a driving scenario of the vehicle at the current moment based on comparing results; determining, as the driving scenario at the current moment, a first scenario class with a highest total similarity in N scenario classes; and controlling, based on the determining result, the vehicle to perform intelligent driving.

Abstract: Various technologies described herein pertain to a vertically stacked lidar assembly of an autonomous vehicle. The vertically stacked lidar assembly includes a first lidar sensor system configured to spin about an axis and a second lidar sensor system configured to spin about the axis. The first lidar sensor system is vertically stacked above the second lidar sensor system in the vertically stacked lidar assembly. Moreover, the first lidar sensor system and the second lidar sensor system are coaxially aligned. Redundancy is provided by the vertically stacked lidar assembly including the first lidar sensor system and the second lidar sensor system.

Abstract: A vehicle includes a maintenance-necessity detector and an automatic driving controller. The maintenance-necessity detector is configured to determine whether maintenance of the vehicle is necessary. The automatic driving controller is configured to cause the vehicle to move to a maintenance facility at which the maintenance of the vehicle is to be performed, on the basis of automatic driving that is independent of driving to be performed by an occupant of the vehicle, in a case where the maintenance is determined by the maintenance-necessity detector as being necessary.

Abstract: A vehicle control system includes a memory, a first processor mounted in a vehicle, and a second processor different from an in-vehicle device. The first processor and the second processor are configured to execute acquisition processing, operation processing, reward calculation processing, and update processing. The first processor is configured to execute at least the acquisition processing and the operation processing, and the second processor is configured to execute the update processing.

Abstract: Examples relate to systems, method and systems, methods and computer programs for efficiently determining an order of driving destinations and for training a machine-learning model using distance-based input data. A system for determining an order of a plurality of driving destinations is configured to obtain information on a distance between the plurality of driving destinations, the distance being defined for a plurality of routes between the plurality of driving destinations, with the routes being defined separately in both directions between each combination of driving destinations of the plurality of driving destinations within the information on the distance. The system is configured to provide the information on the distance between the plurality of driving destinations as input to a machine-learning model, the machine-learning model being trained to output information on an order of the plurality of routes based on the information on the distance provided at the input of the machine-learning model.

Abstract: A system and method for controlling an autonomous vehicle to affect a view seen by an occupant of the autonomous vehicle is described. In one embodiment, a method for controlling an autonomous vehicle to affect a view seen by an occupant of the autonomous vehicle includes determining a navigation route, determining content associated with the navigation route, monitoring current conditions of the autonomous vehicle and the occupant, determining, based on the current conditions, whether to change a position of the vehicle to affect the view seen by the occupant, and when the current conditions permit, moving the autonomous vehicle to affect the view seen by the occupant.

Abstract: A method and an electronic device for generating a trajectory of a Self-Driving Car (SDC) are provided. The method comprises: determining a presence of at least one third-party object around the SDC; generating a plurality of predicted trajectories for the third-party object, where at least one of the plurality of trajectories includes a maneuver executable, by the third-party object, at a future third-party object location; calculating, for the at least one of the plurality of trajectories including the a respective braking profile associated with the third-party object; in response to the respective braking profile being above a pre-determined threshold, eliminating an associated one of the at least one of the plurality of trajectories from future processing; determining an SDC trajectory based on remaining ones of the plurality of predicted trajectories for the third-party.

Abstract: A mobile robotic device has a motion sensor assembly configured to provide data for deriving a navigation solution for the mobile robotic device. The mobile robotic device temperature is determined for at least two different epochs so that an accumulated heading error of the navigation solution can be estimated based on the determined temperature at the at least two different epochs. A calibration procedure is then performed for at least one sensor of the motion sensor assembly when the estimated accumulated heading error is outside a desired range.

Abstract: A method for controlling a vehicle that has a cleaning device. The method includes: receiving, from a terminal, reservation information for a cleaning operation performed by the cleaning device located in the vehicle; transmitting, based on the received reservation information, one or more first operation commands that control the vehicle to move to a location associated with the received reservation information and enables the cleaning device located in the vehicle to perform the cleaning operation; receiving, from the vehicle and based on completion of the cleaning operation performed by the cleaning device located in the vehicle, a completion message; and handling control of the vehicle and the cleaning device located in the vehicle based on the received completion message.

Abstract: One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

Abstract: Provided is an electronic device including: a sensing device selected from a group including a radar and a lidar and installed in a vehicle to have a sensing zone directed to outside of the vehicle, the sensing device configured to obtain sensing data about an object; an image obtainer installed in the vehicle to have a field of view directed to the outside of the vehicle, the sensing device configured to obtain image data; and a controller including at least one processor configured to process the sensing data obtained by the sensing device and the image data obtained by the image obtainer, wherein the controller generates a first virtual driving path and a second virtual driving path based on the sensing data and the image data, and when a first boundary of the first virtual driving path and a second boundary of the second virtual driving path are located at different positions, provides a virtual driving path having a boundary closest to the vehicle between the first virtual driving path and the second vir

Abstract: One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

Abstract: A travel control device is configured to make an autonomous driving vehicle travel in such a way that the autonomous driving vehicle arrives at a specified position specified by a user who intends to board the autonomous driving vehicle, when the autonomous driving vehicle has reached a predetermined range from the specified position, transmit an information sending request notifying the user terminal that the autonomous driving vehicle has reached a vicinity of the specified position and requesting sending of position identifying information for identifying a position at which the user intends to board the autonomous driving vehicle to the user terminal via a communication circuit, and when receiving the position identifying information via the communication circuit, change a position of the autonomous driving vehicle, based on the position identifying information in such a way that the autonomous driving vehicle comes close to the user.

Abstract: An aircraft includes at least one sensor, an altitude actuator, a memory device, and an electronic controller. The at least one sensor is configured to detect altitude of the aircraft, current position of the aircraft and speed of the aircraft. The altitude actuator is configured to change the altitude of the aircraft. The memory device is configured to store predetermined terrain data of an area. The electronic controller is configured to estimate a future position of the aircraft based on a detected current position of the aircraft and a detected speed of the aircraft. The electronic controller is further configured to control the altitude actuator based on the future position, a detected altitude of the aircraft and the predetermined terrain data.