Abstract: A control device for a hybrid vehicle includes: a prediction unit that acquires position information of a parking point where parking time of the hybrid vehicle on a travel route is predicted to exceed a threshold value; and a target setting unit that sets a target charge rate of the battery and changes the target chare rate to a second charge rate that is lower than a first charge rate in a normal state when the hybrid vehicle satisfies an approach condition of approaching the parking point. A charge and discharge amount is controlled such that a charge and discharge amount of the battery corresponding to the second charge rate when the target setting unit sets the target charge rate to the second charge rate is larger to a discharge side than a charge and discharge amount of the battery corresponding to the second charge rate in the normal state.

Abstract: A single crystal ingot growth control device includes: an input unit that receives a diameter error that is a difference value between a measured diameter of the ingot and a target diameter; a calculation unit that performs integral calculation on the diameter error received by the input unit in real time and calculates a final pulling speed for each set time that is increased stepwise by reflecting the diameter error integral value, and an output unit that outputs the final pulling speed calculated by the calculation unit to a pulling controller during the set time.

Abstract: Described herein are technologies relating to controlling an autonomous vehicle based upon a computed lane boundary. The computed lane boundary defines a travel path for the autonomous vehicle when the autonomous vehicle turns across a lane of traffic at an intersection of two or more roadways. The autonomous vehicle navigates turn based upon the computed lane boundary, which allows the autonomous vehicle to enter the intersection without crossing into a lane of oncoming traffic.

Abstract: Provided is a method performed by a sensor control apparatus provided in a vehicle. The method includes: obtaining road driving information received through a wireless communication interface provided in the vehicle; selecting an object undetected area from a plurality of adjacent areas of the vehicle using the road driving information; obtaining sensing data from each of a plurality of sensors provided in the vehicle; selecting an object undetected area from the adjacent areas using the sensing data; determining an undetected area, which is selected as an object undetected area from the adjacent areas both when the road driving information is used and when the sensing data is used, as a power saving area; and outputting a control signal for controlling power saving target sensors, which are some of the sensors corresponding to the power saving area, to operate in a power saving mode.

Abstract: Automating reinforcement learning for autonomous vehicles may include assigning a probability with a scenario and varying that probability based at least in part on changes in performance by the autonomous vehicle associated with that scenario. The amount of time and computational bandwidth required to train a machine-learned component of an autonomous vehicle and the accuracy of the machine-learned component may be improved by determining a reward for performance of the autonomous vehicle in a scenario based at least in part on an severity metric. The impact severity metric may be determined based at least in part on a velocity, angle, and/or interaction area associated with the impact.

Abstract: A self-driving work vehicle including a traveling apparatus, a vehicle body supported by the traveling apparatus on ground, a variable traveling power supply apparatus configured to supply rotational drive power to the traveling apparatus, a travel operation interface configured to adjust a rate of the rotational drive power, a parking brake provided for a transmission shaft of the variable traveling power supply apparatus, a rotation detector configured to detect rotation of the traveling apparatus or the transmission shaft, and a controller. The controller is configured or programmed to control the parking brake to a braking state or a non-braking state by a parking brake control module, and manage the braking state or the non-braking state as a vehicle body state by a condition management module. The braking state and the non-braking state are operating states of the parking brake.

Abstract: A brake health monitoring system and method measure pressures in different components of a brake system of a vehicle system during movement of the vehicle system. Two or more of the pressures that are measured in the different components are compared with each other to select a health monitoring mode. One or more allowable pressures are selected based on the health monitoring mode that is selected. A state of health of the brake system is determined by comparing one or more of the pressures that is measured with the one or more allowable pressures that are selected.

Abstract: An environmental information acquiring unit for acquiring one or more pieces of environmental information, an important information specifying unit for specifying important environmental information among the pieces of environmental information acquired by the environmental information acquiring unit, a calculation unit for performing a calculation related to control of a vehicle on the basis of the pieces of environmental information acquired by the environmental information acquiring unit, and a trained model in machine learning, a contribution information specifying unit for specifying contribution environmental information among the pieces of environmental information used for the calculation by the calculation unit, and a reliability determination unit for determining a degree of reliability of a result of the calculation performed by the calculation unit by comparing the important environmental information specified by the important information specifying unit with the contribution environmental informa

Abstract: A vision-and-laser-fused 2.5D map building method includes: calculating inter-image frame transformation according to an RGB-D image sequence to establish a visual front-end odometer; taking a visual front-end initial estimation as an initial value of scanning matching, and performing laser front-end coarse-grained and fine-grained searches; performing loop closure detection, and performing back-end global optimization on a 2.5D map according to a detected closed loop; and performing incremental update on visual feature dimensions of the 2.5D map, and performing occupation probability update on grid dimensions. The 2.5D map is built using a method of fusing a laser grid and visual features, and compared with a pure laser map and a pure visual map, richness of dimensions is improved, and completeness of information expression is improved; the 2.5D map building method is not influenced by single sensor failure, and can still stably work in a scenario of sensor degradation.

Abstract: A vehicle controller including a driver assistance module configured to perform at least one of an automated and a semi-automated driver assistance function. The at least one of the automated and the semi-automated driver assistance function including a rated operational domain. The vehicle controller also including an image based operational domain detector including at least one channel configured to receive an image from a frame buffer and determine at least part of an operational domain of at least one camera originating the image based at least in part on the image.

Abstract: An autonomous movement system according to an embodiment is an autonomous movement system for autonomous movement in a facility, the autonomous movement system changing a movement speed for the autonomous movement depending on a cooperation mode, a connection mode and a caution mode, the cooperation mode being a mode of moving while acquiring position information inside a first range in cooperation with a facility camera, the first range being a range that the facility camera photographs to generate image data, the facility camera being fixed in the facility, the connection mode being a mode of moving inside a second range and outside the first range, the second range being a range in which connection with an access point is performed by wireless communication, the access point being fixed in the facility, the caution mode being a mode of moving outside the first range and outside the second range.

Abstract: An autonomous vehicle for passengers includes four steerable wheels where each steerable wheel is steered by a steering system having a set of two actuators. One actuator of each set of actuators is powered by a first power source while the other actuator of the set of powered by a second power source. Four controllers each control one actuator of each set of actuators and one actuator from another set of actuators.

Abstract: A vehicle travel control apparatus configured to control a traveling operation of a vehicle including an actuator for traveling, including a first sensor mounted on the vehicle to capture an image of a division line in a traveling direction of the vehicle or measure the division line, a second sensor mounted on the vehicle to detect a movement of a preceding vehicle, and an electronic control unit including a microprocessor and a memory connected to the microprocessor. The microprocessor is configured to perform controlling the actuator based on a position information of the division line obtained by the first sensor and a reliability with respect to the position information of the division line obtained by the first sensor, and the controlling including decreasing the reliability when a predetermined movement of the preceding vehicle to intersect the division line is detected by the second sensor.

Abstract: A method for diagnosing a functionality having an input or output signal with discrete values or discrete classes of values which are observable quantities of a diagnosis functionality. The signal to be diagnosed is the input or output signal, where the values or classes of values have a cardinality N, where N error counters are provided. The method includes providing a sum signal from the sum of the signals of the error counters. When a value or a class of values is erroneous, the method includes incrementing the signal of the error counter assigned to the erroneous value or the erroneous class. When the value or the class is correct, the method includes decrementing the signal of the error counter assigned to the value or the class and when the sum signal exceeds a pre-established limit value, the method includes outputting an error message of the functionality.

Abstract: Systems and methods for generating synthetic testing data for autonomous vehicles are provided. A computing system can obtain map data descriptive of an environment and object data descriptive of a plurality of objects within the environment. The computing system can generate context data including deep or latent features extracted from the map and object data by one or more machine-learned models. The computing system can process the context data with a machine-learned model to generate synthetic motion prediction for the plurality of objects. The synthetic motion predictions for the objects can include one or more synthesized states for the objects at future times. The computing system can provide, as an output, synthetic testing data that includes the plurality of synthetic motion predictions for the objects. The synthetic testing data can be used to test an autonomous vehicle control system in a simulation.

Abstract: Methods and systems for evaluating robustness of vehicle diagnostics and monitoring. One system includes a memory, a communication interface, and an electronic processor. The electronic processor is configured to access diagnostic event data of a vehicle system. The electronic processor is also configured to determine a RQ variable for the current operation cycle based on a set of maximum debounce levels included in the diagnostic event data and a failure threshold. The electronic processor is also configured to store, in the memory, the RQ variable as RQ data at an end of the current operation cycle. The electronic processor is also configured to transmit, via the communication interface, the RQ data for an external robustness evaluation. The electronic processor is also configured to receive, via the communication interface, an update to the failure threshold based on the external robustness evaluation.

Abstract: A captured image acquisition unit configured to acquire a captured image captured by an imaging device mounted on a work vehicle. A posture image acquisition unit configured to acquire a posture image representing at least one of a roll angle and a pitch angle of the work vehicle. A display image generation unit configured to generate a display image in which the posture image is disposed on the captured image so as to display the posture image at a position facing the operator's seat in a width direction of the display device. A display control unit configured to output a display signal for displaying a display image to the display device.

Abstract: The technology described herein provides Automated Driving System (ADS) methods and systems for coordinating and/or fusing intelligence and functions between Connected Automated Vehicles (CAV) and ADS infrastructure to provide target levels of automated driving. The technology provides systems and methods for function allocation comprising sensing allocation, prediction and decision-making allocation, and control allocation. The ADS operates across various intelligence levels, identified during vehicle operation. The function allocation system dynamically allocates essential functions based on the intelligence levels of both vehicles and infrastructures, ensuring that ADS achieves a system intelligence that surpasses that of individual components. This methodical function distribution enables ADS to manage both vehicles and infrastructures in a manner that enhances vehicular operations and control.

Abstract: The present disclosure relates to a vehicle seat moving apparatus configured to control movement of a vehicle seat along a first transfer rail and a second transfer rail formed on the bottom of a vehicle so as to be spaced apart from each other in parallel and a control method thereof.

Abstract: Systems and methods are disclosed for motion forecasting and planning for autonomous vehicles. For example, a plurality of future traffic scenarios are determined by modeling a joint distribution of actor trajectories for a plurality of actors, as opposed to an approach that models actors individually. As another example, a diversity objective is evaluated that rewards sampling of the future traffic scenarios that require distinct reactions from the autonomous vehicle. An estimated probability for the plurality of future traffic scenarios can be determined and used to generate a contingency plan for motion of the autonomous vehicle. The contingency plan can include at least one initial short-term trajectory intended for immediate action of the AV and a plurality of subsequent long-term trajectories associated with the plurality of future traffic scenarios.