Abstract: A thin film resistor and a method of fabricating the same are provided. The thin film resistor includes a substrate, a first end electrode, a second end electrode, a resistor layer, an electrostatic protection layer and an electrostatic electrode layer disposed on the resistor layer. The first end electrode and the second end electrode are disposed on two end portions of an upper surface of the substrate, respectively. The resistor layer and the electrostatic protection layer are disposed on the upper surface of the substrate. The electrostatic protection layer is disposed adjacent to one side of the resistor layer. The electrostatic protection layer includes two separated portions, and at least one of the two separated portions has a tip portion. Therefore, electrostatic charges can be prevented from flowing into the resistor layer, and moisture penetration into the resistor layer can be blocked.

Abstract: The technology described herein provides systems and methods for an Autonomous Vehicle Cloud Control System (AVCCS) with a World Model. The AVCCS comprises a cloud-based platform, a communication module, and/or an onboard unit (OBU). The AVCCS leverages generative models, predictive models, and reinforcement learning methods to generate and synthesize comprehensive information at real-time, short-term, and long-term scales for sensing, transportation behavior prediction and management, planning and decision-making, and/or vehicle control. The comprehensive information generated from the World Model comprises vehicle surrounding information, weather information, vehicle attribute data, traffic state information, road information, and incident information.

Abstract: The technology described herein provides systems and methods for an Automated Driving Cloud System (ADCS) for long-tail corner cases. The ADCS for long-tail corner cases comprises a cloud-based platform, a communication module, and/or an onboard unit (OBU). The ADCS leverages world models to provide automated driving functions including sensing, prediction, planning, decision making, and control at microscopic, mesoscopic, and/or macroscopic levels. The system is specifically designed to address long-tail corner cases, which include work zones, special events, reduced speed zones, incident detection, buffer spaces, and adverse weather conditions. Additionally, the ADCS is configured to provide safety and efficiency measures for vehicle operations and control at various special scales that require additional system coverage, including construction zones, special event zones, and special weather conditions. The ADCS enables adaptive and reliable automated driving in highly uncertain and dynamic environments.

Abstract: A resistor includes a substrate, a pair of inner electrodes, a thin-film resistive layer, a pair of backside electrodes, and a thick-film resistive layer. The substrate includes a first surface and a second surface opposite to the first surface. The pair of inner electrodes is disposed on two opposite ends of the first surface, respectively. The thin-film resistive layer is disposed on the first surface and contacts the pair of inner electrodes, wherein the thin-film resistive layer has a first resistance value and includes a trimming groove. The pair of backside electrodes is disposed on two opposite ends of the second surface, respectively. The thick-film resistive layer is disposed on the second surface and contacts the pair of backside electrodes, wherein the thick-film resistive layer has a second resistance value, and the second resistance value is greater than 100 times of the first resistance value.

Abstract: The technology described herein provides a cloud-based learning system (CLS) for end to end and/or sequential models for autonomous driving. The CLS provides high-performance computation capability that allocates computation power for sensing, prediction, planning and decision making, and control at a microscopic level, a mesoscopic level, and/or a macroscopic level. The CLS can acquire computation resources from a cloud system and from one or more of a roadside unit network, a network of vehicles comprising onboard units, a traffic control center/traffic control unit, or a traffic operations center. Additionally, the CLS is configured to optimize and generate detailed customized information and time-sensitive control instructions for vehicles by processing data through learning models to fulfill driving tasks and provide operations and maintenance services for vehicles.

Abstract: A method for performing block level exploration of integrated circuit (IC) design and associated electronic device and computer-readable medium are provided. The method may include running a synthesis control procedure on at least one processor within the electronic device, for performing automatic placement and routing of a target design of an IC.

Abstract: The present disclosure relates to compounds that are capable of inhibiting the ENPP1 gene and treating a variety of cardiac conditions. The disclosure further relates to methods of treating or preventing cardiac conditions, such as myocardial infarction and heart failure.

Abstract: An animal sorting device, including a housing, a sorting box, a placement box, and a screening device, where the sorting box is located in the housing, and a screening channel is formed in the sorting box and is provided with a material inlet and a material outlet; the placement box is located in the housing and communicates with the material outlet of the screening channel; and the screening device is disposed on the sorting box and includes an RFID module and a closing mechanism. According to the animal sorting device in the present disclosure, the animals are placed in the sorting box, the tags on the animals are identified by the screening device, and the animals fall into different placement boxes to implement sorting of the animals, thereby reducing the error rate of manual screening, and improving the efficiency of animal screening.

Abstract: The technology relates to a systematic intelligent system (SIS) configured to share data and allocate computing resources among automated driving systems (ADS), e.g., using unified data specifications and interfaces. The SIS comprises systematic intelligent units (SIU) that serve components of ADS, including vehicle intelligent units (VIU) and roadside intelligent units (RIU), and is configured to perform methods to serve an entire trip.

Abstract: A Vehicle Intelligent Unit (VIU) is configured to provide vehicle operations and control for Connected Automated Vehicles (CAV) and, more particularly, to connect with a Collaborative Automated Driving System (CADS) and manage and/or control information exchange between CAV and CADS and manage and/or control CAV lateral and longitudinal movements, including vehicle following, lane changing, and route guidance.

Abstract: The invention provides a vehicle AI computing system (VACS) that supports autonomous driving through an Onboard Unit (OBU) for vehicle-based computing and distributed computing based on vehicle-road-cloud. The vehicle-based computing can effectively complete various computational tasks by using onboard computing resources. The distributed computing allows the vehicle to work in collaboration with roadside units (RSUs) and/or the cloud to effectively complete various computational tasks. The VACS features an OBU with a sensing module, a communication module, and a data processing module that integrates data from vehicle sensors, RSUs, and the cloud. The OBU also includes a vehicle control module that helps control the vehicle based on the data of RSU and cloud. The VACS leverages high-performance computation resources to implement end-to-end driving tasks including sensing, prediction, planning and decision-making, and control.

Abstract: This invention presents a vehicle safety system (VSS) for autonomous vehicles (AVs) utilizing an onboard unit (OBU) capable of communicating with various entities, comprising roadside units (RSUs), other OBUs, and cloud platforms. The OBU incorporates a safety subsystem designed to implement proactive, active, and passive safety measures. The proactive safety measures comprise incident prediction and warnings. The active safety measures comprise emergency braking, and the passive safety measures comprise incident response and dynamic routing. This VSS can be further enhanced by incorporating sensing and processing capabilities of the RSU network or the cloud platform, enabling distributed and integrated safety functions and improved environmental awareness. The system aims to enhance AV safety by addressing incidents before, during, and after their occurrence through comprehensive safety measures at a system level, comprising a vehicle centric system, a vehicle-road system, or a vehicle cloud system.

Abstract: A high-power thin film resistor includes a substrate, a resistance layer, an internal electrode layer, a passivation layer and a thermal conductive layer. The resistance layer is disposed on the substrate, and the internal electrode layer has a middle internal electrode area and two terminal internal electrode areas. The resistance layer is divided into a middle resistance area and two terminal resistance areas by the middle internal electrode area and the two terminal internal electrode areas. The passivation layer covers portions of the resistance layer and the internal electrode layer. The thermal conductive layer is disposed on the passivation layer, wherein the thermal conductive layer has two thermal conductors and a gap between the two thermal conductors. The middle internal resistance area and the two terminal resistance areas form a series resistance, and the two terminal resistance areas have the same resistance value.

Abstract: This technology provides systems and methods for a vehicle computing system (VCS) for autonomous driving. This VCS furnishes End-to-End models that provide sensing, prediction, planning, decision-making, and control functions. The VCS executes vehicle control algorithms, trains general AI models, and makes inferences from those AI models. Specifically, a computing subsystem of the VCS performs computation methods that train a tensor-centered model and/or make inferences from a tensor-centered model. Additionally, the VCS gathers data from a roadside unit network, an onboard unit, a cloud platform, a traffic control center/traffic control unit, and a traffic operations center (TOC), thereby enhancing the safety and efficiency of autonomous driving.

Abstract: The invention provides systems and methods for a vehicle-based cloud computing system (VCCS) for autonomous driving. This VCCS builds world models based on a series of complex scenario data to optimize sensing, prediction, planning, decision making, and control for autonomous driving. The VCCS can execute vehicle control algorithms, train general AI models, and make inferences to optimize autonomous driving. Specifically, it dynamically adjusts driving strategies based on long tail scenarios including but not limited to weather, work zone information, and traffic status, ensuring safe and efficient vehicle operation. Additionally, the VCCS can gather supplementary data from (a) a roadside unit (RSU) network, (b) another OBU, (c) a cloud platform, (d) a traffic control center/traffic control unit (TCC/TCU), and (e) a traffic operations center (TOC), thereby further improving control and efficiency in complex driving environments.

Abstract: A high-power chip resistor includes a resistance layer, a first thermal conductive layer, an adhesion layer, internal electrodes, a first protection layer and a second thermal conductive layer. The first thermal conductive layer includes first thermal conductors and a first gap between the first thermal conductors. The adhesion layer is disposed between the resistance layer and the first thermal conductive layer to adhere them. The internal electrodes are disposed on two terminals of the resistance layer. The first protection layer covers the resistance layer and portions of upper surfaces of the internal electrodes. The second thermal conductive layer is disposed on the first protection layer and includes two second thermal conductors and a second gap between the second thermal conductors. The second thermal conductors contact other portions of upper surfaces located at two terminals of the internal electrodes and not covered by the first protection layer.

Abstract: A chip resistor includes a substrate, first to fourth electrodes, a resistance layer, a resin electrode layer, first and second insulating protective layers, and first and second external electrode layers. The first and second electrodes are respectively disposed on two opposite edge areas of a front surface of the substrate. The resistance layer extends from the first electrode to the second electrode. The first insulating protective layer completely covers the resistance layer. The resin electrode layer includes first to third portions respectively covering the first and second electrodes, and a portion of the first insulating protective layer. The second insulating protective layer completely covers the third portion and partially covers the first and second portions. The third and fourth electrodes are disposed on a back surface of the substrate. The first and second external electrode layers respectively connect the first and third electrodes, and the second and fourth electrodes.

Abstract: A chip resistor includes a substrate, first and second conductive structures in the substrate, first to third front electrodes and first to third back electrodes respectively on front and back surfaces of the substrate, first and second resistance layers respectively on the front and back surfaces, and first and second external electrode layers. The first to third front electrodes are opposite to the first to third back electrodes. The first back electrode and the first front electrode are connected to the first conductive structure. The first resistance layer is connected to the second front electrode and the second conductive structure. The second resistance layer is connected to the first and third back electrodes and the first and second conductive structures. The first and second external electrode layers respectively connect the first front electrode and the first back electrode, and the second front electrode and the second back electrode.

Abstract: Provided herein is technology relating to aspects of a Distributed Driving System (DDS) for managing Connected and Automated Vehicles (CAV) and particularly, but not exclusively, to systems, designs, and methods for a Device Allocation System (DAS) configured to allocate and distribute resources to devices of a Distributed Driving Systems (DDS).

Abstract: Provided herein is a technology for an Autonomous Vehicle Cloud System (AVCS). This AVCS provides sensing, data fusion, prediction, decision-making, and/or control instructions for specific vehicles at a microscopic level based on data from one or more of other vehicles, roadside unit (RSU), cloud-based platform, and traffic control center/traffic control unit (TCC/TCU). Specifically, the AVs can be effectively and efficiently operated and controlled by the AVCS. The AVCS provides individual vehicles with detailed time-sensitive control instructions for fulfilling driving tasks, including car following, lane changing, route guidance, and other related information. The AVCS is configured to predict individual vehicle behavior and provide planning and decision-making at a microscopic level. In addition, the AVCS is configured to provide one or more of virtual traffic light management, travel demand assignment, traffic state estimation, and platoon control.