Abstract: The invention relates to a method and a system for determining process data of a work process carried out by an implement based on the determination of a moved mass by a weighing system of the implement and the detection of a parameter concerning a state of the implement and/or a work step, wherein with reference to these and further data a prediction value concerning the remaining part of the current work process is determined and on the basis of which information on the assistance is indicated to the operator of the implement, and to an implement comprising such a system.

Abstract: In order to control at least partially automated vehicles in a road danger zone, in particular road junctions in road traffic, in such a way that the vehicles can pass through the road danger zone in flowing traffic without stop/start interruptions, such as those caused e.g. by signalling equipment, traffic lights, the following proposes the following steps: a) each vehicle of the vehicles, on approaching the road danger zone, surrender the power to control the dynamic driving tasks of the vehicle in order to pass through said zone, b) when the vehicles have surrendered vehicle control power, a central control entity generates a digital road danger zone twin, and, as a result of the vehicles having surrendered vehicle control power, vehicle movements of the vehicle are automatically and dynamically controlled in a vehicle-coordinated and collision-free manner in order to pass through the road danger zone.

Abstract: The present disclosure provides a neural network-based method for calibration and localization of an indoor inspection robot. The method includes the following steps: presetting positions for N label signal sources capable of transmitting radio frequency (RF) signals; computing an actual path of the robot according to numbers of signal labels received at different moments; computing positional information moved by the robot at a tth moment, and computing a predicted path at the tth moment according to the positional information; establishing an odometry error model with the neural network and training the odometry error model; and inputting the predicted path at the tth moment to a well-trained odometry error model to obtain an optimized predicted path. The present disclosure maximizes the localization accuracy for the indoor robot by minimizing the error of the odometer with the odometry calibration method.

Abstract: Implementations process, using machine learning (ML) layer(s) of ML model(s), actor(s) from a past episode of locomotion of a vehicle and stream(s) in an environment of the vehicle during the past episode to forecast associated trajectories, for the vehicle and for each of the actor(s), with respect to a respective associated stream of the stream(s). Further, implementations process, using a stream connection function, the associated trajectories to forecast a plurality of associated trajectories, for the vehicle and each of the actor(s), with respect to each of the stream(s). Moreover, implementations iterate between using the ML layer(s) and the stream connection function to update the associated trajectories for the vehicle and each of the actor(s). Implementations subsequently use the ML layer(s) in controlling an AV.

Abstract: A tire wear prediction system (100) is provided with a wear prediction unit (110) for predicting a wear state of a tire mounted at a predetermined wheel position of a vehicle based on the traveling state of the vehicle, a change history acquisition unit (120) for acquiring a change history including the rotation of the wheel position on which the tire is mounted or a content of replacement with another tire, and a wear state correction unit (140) for correcting a reference of the wear state of the tire predicted by the wear prediction unit (110) based on the change history. The wear prediction unit (110) predicts the wear state of the tire based on the reference of the wear state corrected by the wear state correction unit (140).

Abstract: Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

Abstract: Connected vehicles and methods described herein provide for message sender identification. Connected vehicles and methods disclosed herein can include generating a message based hyper-graph based on the positions of connected elements of a road traffic network as communicated to the connected vehicle by the connected elements. Connected vehicles and methods disclosed herein can include generating, a perception based hyper-graph based on the perceived positions of at least a subset of the elements of the road traffic network as determined by a sensor of the vehicle. Connected vehicles and methods disclosed herein can include matching a node of the message-based hyper-graph to a corresponding node of the perception-based hyper-graph to determine the sender of a received message.

Abstract: Methods and systems are provided for an automated door system for a vehicle. In one example, the method for a vehicle may include operating an automated door system to enable hands-free actuation of a set of door vehicles while the height of the vehicle is not changing and when the vehicle height is changing, suspending operation of the automated door system until the floor has stopped changing height.

Abstract: A vehicle control apparatus selects, as a first group, objects in a predetermined area from objects included in object information, selects, as a second group, an upper limit number of the objects from the first group in descending order of a priority value when the number of the objects of the first group is greater than the upper limit number, and executes a collision avoidance control when an index value satisfies a predetermined condition. The priority value represents a probability that the own vehicle collides with the object. The apparatus reduces the priority value of a particular object of the first group when the moving speed of the own vehicle is equal to or lower than a moving speed threshold. The particular object is an object which is deemed to have a moving speed lower than a moving speed of a four-wheel vehicle.

Abstract: A hybrid vehicle control method controls a hybrid vehicle having an electric power source, a vehicle electrical equipment and drive motor to which electric power is supplied from the electric power source. The vehicle electrical equipment and the drive motor are electrically connected to the electric power source via at least a shared harness. When a temperature of the harness is equal to or greater than a predetermined temperature, upper limit values of electric power supplied from the electric power source to the vehicle electrical equipment and the drive motor are both reduced, and a degree of reduction in the upper limit value for the vehicle equipment is greater than a degree of reduction in the upper limit value for the drive motor.

Abstract: An automatically moving floor treatment appliance has an appliance housing, a drive, a computing element and a plurality of fall sensors. The computing element compares a detection result of a fall sensor with a known reference result, and when the detection result does not correspond with the reference result, determines a malfunctioning of the fall sensor. The computing element determines distances detected chronologically successively by the same fall sensor during a movement of the appliance with one another, and when the distances are identical, determines a malfunctioning of the fall sensor, and/or compares a detection result of the leading fall sensor with a detection result of a trailing fall sensor and when the trailing fall sensor detects a slope without the leading fall sensor having detected the slope before, determines a malfunctioning of the leading fall sensor and the trailing fall sensor takes over the from the leading fall sensor.

Abstract: Disclosed are devices, systems and methods for using a rotating camera for vehicular operation. One example of a method for improving driving includes determining, by a processor in the vehicle, that a trigger has activated, orienting, based on the determining, a single rotating camera towards a direction of interest, and activating a recording functionality of the single rotating camera, where the vehicle comprises the single rotating camera and one or more fixed cameras, and where the single rotating camera provides a redundant functionality for, and consumes less power than, the one or more fixed cameras.

Abstract: The disclosure is generally directed to systems and methods for battery life cycle prediction for a preowned electrified vehicle including receiving state of charge (SOC) and mileage data associated with the preowned electrified vehicle, providing one or more driving maneuvers to be performed by a driver, providing one or more instructions to the driver to operate power-driven accessories of the preowned electrified vehicle, collecting data representing battery usage by the driver by monitoring the driving maneuvers and the operation of power-driven accessories as performed by the driver, and responsive to the collected data representing battery usage and the SOC and mileage, providing a battery life prediction for the preowned electrified vehicle.

Abstract: A vehicle is provided including an electronic power steering system, an electronic throttle control system, and a stability control system.

Abstract: A control method of an electric vehicle using one or a plurality of motors as a traveling drive source, includes: a motor torque command value calculating step that calculates a motor torque command value; an angular velocity detecting step that detects an angular velocity correlating with a rotation speed of a drive shaft which transmits a drive force of the motor to a drive wheel; a disturbance torque estimating step that estimates a disturbance torque acting on the motor based on the motor torque command value and the angular velocity; a limiting torque setting step that sets a limiting torque corresponding to the disturbance torque in a manner that a slipping rate of the drive wheel does not exceed a first predetermined value; and a motor torque limiting step that limits the motor torque command value using the limiting torque.

Abstract: A method of sensing a three-dimensional (3D) space using at least one sensor is proposed. The method can include acquiring spatial information over time for the sensed 3D space, applying a neural network based object classification model to the acquired spatial information over time to identify at least one object in the sensed 3D space. The method can also include tracking the sensed 3D space including the identified at least one object, and using information related to the tracked 3D space.

Abstract: A method for calibrating a driving risk identification model includes: S1. establishing a vehicle platform by installing an information acquisition device on a test vehicle; S2. acquiring synchronized test data related to the test vehicle and driving scenarios; S3. extracting moments when the drivers start to press the accelerator pedal, start to release the accelerator pedal, start to press the brake pedal in the driving scenarios, and start to release the brake pedal, so as to define risk level values respectively corresponding to the moments; S4. obtaining a risk identification curve of the drivers in different driving scenarios, wherein the risk identification curve represents the drivers' judgment of the risk level over time; S5. using the risk identification curve to calibrate the driving risk identification model.

Abstract: A method for processing sensor data. The method including: providing surroundings data of a vehicle; assigning the surroundings data to corresponding locating data; and transferring the locally assigned surroundings data to a processing device, the transfer of the surroundings data between a sensor unit and a control unit and/or the transfer of the locally assigned surroundings data between the control unit and the processing device being carried out according to a defined data limiting criterion.

Abstract: Systems and methods for vehicle microphone activation and/or identification of incoming speech-to-text data by the location of the speaking occupant. In some embodiments, the system may comprise a plurality of microphones, each of which may be linked with a particular occupant/seat in the vehicle. The system may be configured to link incoming STT data with a particular microphone/occupant. This may be done by an explicit trigger from an occupant or by actuation of a button or other actuation means by a vehicle occupant. The STT data may then be processed using the location data.

Abstract: An information management apparatus acquires location information of one or more vehicles having electric power of only a first battery as a power source and a remaining level of the electric power of the first battery, acquires information of a surplus power accumulated in a second battery that is installed in each of a plurality of facilities and is capable of supplying electric power to devices installed in the facility and location information of the facility, and provides the location information of the facility to the vehicle in a case in which the remaining level of the electric power of the first battery is equal to or lower than a first threshold, the facility being in which the second battery of which the surplus power is equal to or higher than a second threshold is disposed and being present within a predetermined distance from a vehicle.