Abstract: The present disclosure relates to a self-propelled robotic work tool (1), e.g. an automatic robotic lawn mower, and a corresponding method. The robotic tool comprises an inertia measurement unit (IMU 15) which generally obtains (25) measured IMU parameters regarding the robotic working tool's movement. A prediction algorithm (17) predicts (27,36) required motor currents for driving the robotic work tool's wheels (5) based on the measured IMU parameters. The predicted motor current is compared (29,37) to the actual current used and the difference constitutes an error (19), which is used in a collision detection unit (21). If the collision detection unit (21) senses that the actually used motor current is much higher than the predicted current, a collision may be indicated (31).

Abstract: A vehicular vision system includes a camera and a radar sensor disposed at the equipped vehicle. The system includes an electronic control unit (ECU) and, as the equipped vehicle travels along a road, the ECU, via processing of image data and processing of sensor data, determines other vehicles present on the road ahead of the equipped vehicle. The ECU, responsive to determining the presence of a plurality of other vehicles present on the road, fuses the image data captured by the camera and the sensor data captured by the radar sensor. The ECU, based on the fused data, determines a threat level for each vehicle of the plurality of other vehicles and, when the threat level for one or more vehicles of the plurality of other vehicles exceeds a threshold value, generates a braking command. The ECU transmits the braking command to a braking system of the equipped vehicle.

Abstract: Determining an instantaneous vehicle characteristic (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined instantaneous vehicle characteristic of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined instantaneous vehicle characteristic of the additional vehicle. In many implementations, the instantaneous vehicle characteristics of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Abstract: There is provided a driver-support system for use with a human-operated material-transport vehicle, and methods for using the same. The system has at least one sensor, a human-vehicle interface, and a transceiver for communicating with a fleet-management system. The system also has a processor that is configured to provide a mapping application and a localization application based on information received from the sensor. The mapping application and localization application may be provided in a single localization-and-mapping (“SLAM”) application, which may obtain input from the sensor, for example, when the sensor is an optical sensor such as a LiDAR or video camera.

Abstract: A driving device and a driving method for an electric assisted bicycle are provided. The driving device includes a motor, a rotation speed sensor, a torque sensor and a controller. The rotation speed sensor senses a rotation speed value of a crank or a chainring of the electric assisted bicycle. The torque sensor obtains an average torque value applied by the crank to the chainring of the electric assisted bicycle. The controller controls an output of the motor in response to the rotation speed value and the average torque value.

Abstract: A remote operation device includes: a vibration detector to detect a plurality of vibration components in a plurality of directions different from each other, the vibration components being included in a vibration caused on an attachment; a transmission device; and a transmission control section that controls an operation of the transmission device. A vibration determination condition is set in advance, the vibration determination condition including a condition that an amplitude of a maximum vibration component largest in amplitude among the plurality of vibration components detected by the vibration detector is equal to or larger than a preset amplitude threshold. The transmission control section controls the operation of the transmission device to allow the vibration information to be transmitted to an operator only when the vibration determination condition is met.

Abstract: There is provided a smart detection system including multiple sensors and a central server. The central server confirms a model of every sensor and a position thereof in an operation area. The central server confirms an event position and predicts a user action according to event signals sent by the multiple sensors.

Abstract: A traveling trajectory estimation system obtains traveling record information indicating a past traveling record of a vehicle, and characteristic object position information indicating the installation position of a characteristic object, and performs a vehicle position estimation process to estimate an object vehicle position at an object time, based on the traveling record information and the characteristic object position information. In the vehicle position estimation process, not only a time previous to the object time, but also a time subsequent to the object time, is used as a reference time. The traveling trajectory estimation system sets each of a plurality of successive times as the object time, and performs the vehicle position estimation process, to estimate the object vehicle positions at the respective times, and determines a collection of the estimated object vehicle positions as a traveling trajectory of the vehicle.

Abstract: Methods, computer-readable media, software, and apparatuses may determine whether a human driver, or an autonomous driving system, should be in control of a vehicle in response to a detection of an unexpected event. A decision may be made to pass control from the autonomous driving system to the human driver, if it is determined that the human driver can handle the unexpected event more safely than the autonomous vehicle.

Abstract: An automation method of an artificial intelligence (AI)-based diagnostic technology for equipment application includes receiving one or more pieces of data among vibration data, noise data, and controller area network (CAN) data, a data input processing operation of trimming the input data, an operation of extracting features from the trimmed data, setting a setting value of a hyper-parameter with respect to the one or more pieces of data thereamong, and generating a total of N models to include both of machine learning (ML) and deep learning (DL) as N individual models and generating ensemble prediction model structures for the N individual models. As a parameter updating is being proceeded due to the hyper-parameter so as to minimize values of cost functions of the N individual models, a reward for model accuracy performance is optimized and the ensemble prediction model structures of the N individual models change.

Abstract: An autonomous speed control function for autonomously controlling a traveling speed of the vehicle and an autonomous steering control function for autonomously controlling steering of the vehicle include executing first autonomous control where, when an external situation of the vehicle is recognizable from a captured image by a camera on the vehicle, the vehicle is autonomously controlled using information recognized from the captured image; executing second autonomous control in which information indicating the external situation of the vehicle is acquired from stored map information provided in the vehicle and the vehicle is autonomously controlled using the acquired information; specifying an unrecognizable area in which the external situation of the vehicle is unrecognizable from the captured image; and when the unrecognizable area is present on a travel route of the vehicle, switching from the first autonomous control to the second autonomous control before the vehicle enters the unrecognizable area.

Abstract: A sensor system includes a pump, a valve supplied from the pump, a sensor fixed relative to the pump, a nozzle supplied from the valve and aimed at the sensor, and a computer communicatively coupled to the pump, the valve, and the sensor. The computer is programmed to instruct the pump to operate; while the pump is operating, instruct the valve to close; receive data from the sensor while the pump is operating and the valve is instructed to be closed; and set a flag upon determining, based on the data received from the sensor, that the nozzle is outputting fluid onto the sensor.

Abstract: A system comprises a computer having a processor and a memory, the memory storing instructions executable by the processor to access sensor data of a first sensor of a vehicle while an adaptive cruise control feature of the vehicle is active, detect, based on the sensor data of the first sensor, a stationary object located along a path of travel of the vehicle, wherein the stationary object is located outside of a range of a second sensor of the vehicle, determine a presence of an intersection within a threshold distance of the stationary object that is along the path of travel of the vehicle, and responsive to a determination that the stationary object is a stopped vehicle of the intersection, adjust, by the adaptive cruise control feature, the speed of the vehicle.

Abstract: A method for operating an electric motor for an electric braking device, in particular for an electric parking brake of a vehicle, in particular a motor vehicle, in which the braking device can be put into a first operational state by operation of the electric motor in a first direction of rotation and into a second operational state by operation of the electric motor in a second direction of rotation different from the first direction of rotation, includes determining a quantity which characterizes an internal resistance of the electric motor, comparing the first quantity with a predeterminable first threshold value, controlling the electric motor for a predeterminable first control duration according to the direction of rotation which corresponds to a current operational state of the braking device when the result of the comparison is that the first quantity exceeds the first threshold value.

Abstract: Among other things, sensor signals are received using a vehicle comprising an autonomous driving capability. A risk associated with operating the vehicle is identified based on the sensor signals. The autonomous driving capability is modified in response to the risk. The operation of the vehicle is updated based on the modifying of the autonomous capability.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

Abstract: An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a path tracking controller configured to detect lane boundaries and a path curvature for the traffic lane from the image, determine a lateral offset of the vehicle from a reference path for the traffic lane and a heading offset for the vehicle from the path curvature, determine a yaw rate maintaining the vehicle within the traffic lane using a kinematics control, determine a steering angle maintaining the vehicle within the traffic lane using a dynamics control and the yaw rate determined by the kinematics control, and activate a steering control based on the determined steering angle.

Abstract: An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a vehicle behavior prediction controller configured to determine lane boundaries and road curvature for a segment of a traffic lane occupied by the vehicle from the captured image and prior captured images; determine lateral distances of the vehicle from the lane boundaries and a rate of departure of the vehicle from the occupied traffic lane that is accurate for the determined road curvature; determine a time to line crossing for the vehicle from the lateral distances and the rate of departure; and activate a lane departure warning indicator based on the determined time to line crossing.

Abstract: A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation.

Abstract: The subject matter described in this specification is directed to a computer system and techniques for determining a location of an autonomous vehicle. The computer system is configured to determine the location using localization data from multiple data sources. When the localization data from the multiple data sources is unavailable or inaccurate, the computer system is configured to determine the location using predefined features of the environment.