Abstract: A distributed Artificial Intelligence (AI) system for an Autonomous Vehicle (AV) is disclosed. The system is hosted on a vehicle system and comprises an AI Allocation Subsystem and an Operating and Management Subsystem, which provide customized allocation of computing power, data, models, and parameters for vehicle-specific operation. A distillation framework is applied to refine trip parameters and AI driving models for optimized decision-making, efficient resource distribution, and real-time or dynamic updates. In some versions of the system, cloud-based large AI models perform knowledge extraction from extensive sensor data and generate soft labels and inference backups, while compact in-vehicle AI models receive compressed and quantized knowledge for real-time decision-making.

Abstract: A control circuit generates a set signal based on an output voltage and a reference voltage signal, a total output current and a reference current signal, and each sub-control circuit receives the set signal; a next sub-control circuit receives an enable signal generated by its previous sub-control circuit, and generates a corresponding switch control signal and an enable signal acting on its next sub-control circuit to control the corresponding switch circuit to turn on or turn off based on the received enable signal and the set signal. The present disclosure can control sequential conduction of the plurality of switch circuits through the signal transmission among the multiple sub-control circuits, and can implement overcurrent protection when the total output current is overcurrent, and implement the control of the switch circuit when the total output current is not overcurrent based on the comparison of the output voltage and the reference voltage signal.

Abstract: Provided herein is technology relating to a function allocation system (FAS) that deploys artificial intelligence models for a connected automated highway (CAH) system and a connected automated vehicle (CAV) system to distribute driving intelligence between the CAV system and the CAH system. The FAS comprises a communication module, a data module, and a computing module. The computing module is configured to analyse scenes using sensing data, determine automated driving function requirements, deploy function allocation methods, and analyse CAH system and CAV system functions. The function allocation methods provide analysis, guidance, and optimization capabilities for sensing, decision-making, and control functions. The FAS allocates automated driving functions to the CAV system and the CAH system based on their respective intelligence levels.

Abstract: The technology relates to an autonomous vehicle intelligent control (AVIC) system with distributed AI computing, which is a component of a Connected Automated Vehicle Highway (CAVH) system. This AVIC system provides training and application for distributed AI computing by providing automated vehicles (AVs) and/or connected automated vehicles (CAVs) with vehicle-specific control instructions comprising instructions for vehicle longitudinal and lateral position, speed, and steering and control. Through the system, CAVs are effectively and efficiently controlled by the AVIC system. In addition, the AVIC system provides vehicle-specific control instructions to CAVs and controls them through a hierarchy of traffic control centers/traffic control units (TCCs/TCUs) or the combination of TCCs/TCUs and onboard units (OBUs) comprising vehicle control modules.

Abstract: Disclosed is a signal transmission method, a transmission device, and a system. A master device and at least one slave device, which is pre-configured with a sequence for instruction execution, are connected through a single bus. After the master device sends the instruction set through the single bus, the at least one slave device sequentially acquires its corresponding instruction from the instruction set according to the sequence of the instruction set and executes corresponding operation. The instruction set has one or more special instruction signals, each having a characteristic different from that of a normal instruction signal and serving as a basis for determining a sequence of the at least one slave device. Thus, rapid instruction transmission can be achieved through the single bus. Within one cyclic period, each slave device can obtain its accurate position in the sequence of the instruction set, with high robustness.

Abstract: The present application relates to an object display method and apparatus, a device, and a storage medium. The method includes: displaying a first page, where a first object and a first topic word are displayed on the first page, the first object belongs to a first object set obtained by aggregation based on the first topic word, and an object in the first object set has a description feature corresponding to the first topic word (S101); and displaying a second page in response to a preset trigger operation for the first topic word, where a second object is displayed on the second page, the second object belongs to the first object set, and the second object includes at least one type of object from a live streaming object, a video object, an image-text object and a life service object (S102).

Abstract: The present disclosure relates to a method and apparatus for information processing, an electronic device and a storage medium. The method comprises: obtaining a page loading instruction comprising identification information of a page to be loaded, wherein the page to be loaded comprises a target configurable area and a target non-configurable area; rendering the page to be loaded using first target resource information and second target resource information; wherein the first target resource information corresponds to the target configurable area; the first target resource information is variable resource information and determined according to current promotion activity contents; and the second target resource information corresponds to the target non-configurable area; and the second target resource information is fixed resource information, which does not vary with changes of promotion activity contents.

Abstract: Embodiments of the present application relate to a data processing method, a network interface controller, a compute device, and a computer cluster, and relate to the field of communication technologies. An example method includes: grouping a plurality of compute nodes of a computer cluster into a first group and a second group, where each of the first group and the second group includes at least two compute nodes; and establishing an all-to-all connectivity between processes on intra-group compute nodes of the first group and a correspondence between processes that belong to a same application and that are on inter-group compute nodes of the first group and the second group.

Abstract: A phase-number control circuit for a multiphase controller, a multiphase controller, and a multiphase switching power supply are provided. The multiphase controller comprises n PWM pins and n comparators corresponding to the n PWM pins. A k-th comparator receives a k-th load current sampling signal and a k-th threshold signal, and outputs a k-th comparison signal, wherein the k-th load current sampling signal is a load current sampling signal of an output rail of the k-th PWM pin, and the k-th threshold signal determines whether to turn on a switching circuit. The multiphase controller controls whether the switching circuit performs power operation according to the k-th comparison signal, and controls a multiphase switching power supply with an output rail count less than or equal to p, wherein 2 ? p ? n. Area and power consumption are reduced, and phase-switching response speed is increased.

Abstract: A website performance optimization method includes creating performance baseline data, performing performance optimization for each website core scenario related to performance data, and outputting a performance test report and runtime information data, determining a performance change condition of the website based on the performance baseline data, the performance test report and the runtime information data, and determining whether to end a performance optimization cycle based on the performance change condition.

Abstract: This disclosure presents a distributed automated driving system for autonomous vehicles operating in long-tail scenarios. These long-tail scenarios comprise adverse weather conditions, adverse road conditions, disaster, and/or a failure of a vehicle component or function. During operation of an autonomous vehicle, some of its automated driving capabilities for sensing, prediction, planning, decision-making, or control may be downgraded due to long-tail events. The intelligent roadside toolbox (IRT) functions as an edge server or a cloud and can supplement the vehicle's sensing, prediction, planning and decision-making, and control functions by providing customized, on demand, and dynamic computing resources and functions to the vehicle. In addition, the IRT computing functions provide computation support for sensing, prediction, planning, decision-making, and/or control functions of the vehicle.

Abstract: This invention presents a function allocation system for an autonomous vehicle (AV). During the operations of the AV, some or all of its automated driving capabilities or functions could be downgraded due to long-tail events or malfunctioning. The roadside intelligent infrastructure, or the cloud platform, could supplement some or all of AV's automated driving functions, including sensing, prediction and decision-making, and/or control functions. The function allocation system dynamically allocates these functions between AV and intelligent infrastructure, achieving a higher system intelligence level S than the downgraded vehicle intelligence level V. In addition, a function allocation system could dynamically allocate sensing, prediction and decision-making, and/or control functions between AV and a cloud platform. This invention also presents a function integration system or a fusion system for an AV.

Abstract: A method for pulse heating a power battery of an electric vehicle and an electric vehicle. A powertrain includes a first power circuit and a second power circuit. The method is used to control the second power circuit or a motor power circuit to generate a pulse alternating current on a direct current bus, and the pulse alternating current is used to heat the power battery. A phase value of a pulse alternating current output by at least one of the first power circuit and the second power circuit is adjusted, to reduce a phase difference between a pulse alternating current generated by the first power circuit and a pulse alternating current generated by the second power circuit, thereby improving heating efficiency of the power battery.

Abstract: A charging circuit of an on-board charger, where a second end of a first power conversion circuit of the charging circuit is coupled to a first end of a second power conversion circuit, a high-voltage output end of the second power conversion circuit charges a power battery pack of an electric vehicle, and a first low-voltage output end of the second power conversion circuit supplies power to a low-voltage system of the electric vehicle. The first power conversion circuit is configured to, when the electric vehicle is in a charging mode, convert an alternating current input from a first end of the first power conversion circuit into a direct current and transmit the direct current to the first end of the second power conversion circuit.

Abstract: A method and aerial vehicle perform illumination invariant image mapping for terrain relative navigation (TRN). Two images of a terrain segment are obtained, each with known sun vectors and different lighting conditions. The first image is matched and registered to the second image. A target third image is obtained under a lighting condition that exceeds a threshold required for matching. A synthetic composite image is synthesized based on the images and sun vectors. The synthetic image is registered to the first and second images. The synthetic image is matched to the target third image. Based on the matching, a TRN solution is derived and used to navigate the aerial vehicle.

Abstract: Disclosed is an operational amplifier circuit and a switching circuit. The operational amplifier circuit comprises a main operational amplifier, which performs operational amplification based on a reference signal and a feedback signal of an output voltage of the switching circuit to obtain an operational amplification signal for controlling a switching state of the switching circuit, and generates an adjustment signal according to the feedback signal, the reference signal, and a preset first voltage. When a load of the switching circuit is reduced, an output terminal of the main operational amplifier receives the adjustment signal and obtains an adjusted operational amplification signal, and the adjustment signal is used for suppressing a change of the operational amplification signal.

Abstract: The present technology relates to systems and methods that allocate, arrange, and distribute certain types of functions and intelligence for autonomous driving systems (ADS) to facilitate vehicle operations and controls; to improve the safety of an autonomous vehicle (AV); and to ensure the efficiency, intelligence, reliability, and resilience of ADS. The present technology also provides methods to define ADS system intelligence and its levels, which are based on two dimensions: the vehicle intelligence and the cloud intelligence. The cloud intelligence supports and complements the vehicle intelligence when a vehicle is impaired or handicapped for certain functions (sensing, prediction, decision, and/or control) during operations in abnormal conditions. Distribution of driving intelligence between the AV and the cloud facilitates safe operations of the AV for long-tail corner cases.

Abstract: This application discloses a resistance detection method and a resistance detection circuit. A test voltage and a reference voltage are obtained, and one of them is set as a value-to-be-compared and the other one is set as a reference value with a fixed value. The test voltage is a voltage value obtained by applying a test current to a unit-under-test, and the reference voltage is a voltage value generated by a reference voltage supply circuit. The value-to-be-compared includes a first boundary value and a second boundary value. The first boundary value and the second boundary value are compared with the reference value respectively to obtain a first comparison result and a second comparison result. A resistance range of a resistance of the unit-under-test is calculated based on the first comparison result and the second comparison result.

Abstract: An Enterprise-Oriented Cloud Control System is disclosed for providing time-sensitive, vehicle-specific guidance and control instructions to autonomous vehicles (AVs) in long-tail corner cases. The system operates through an enterprise-oriented cloud intelligent unit (E-CIU) that integrates service, management, communication, sensing, prediction, planning, control, and support subsystems. Automated driving scenarios are classified by a traffic fence based on demand and supply characteristics. The E-CIU receives multi-source event and non-event data from vehicles, roads, clouds, and centers, offers various “as-a-service” functions, and employs event models to convert macroscopic event data into microscopic operational data. These models generate cooperative sensing, prediction, planning, and control solutions and produce customized, real-time instructions for longitudinal/lateral motion and operational decisions.

Abstract: This technology provides several methods for operating an Autonomous Vehicle Intelligent Control System that integrates a sensing module for collecting driving environment data and an onboard unit (OBU) for vehicle control. The OBU features a vehicle control module and communication modules for guidance-level interaction with Traffic Control Centers/Traffic Control Units (TCC/TCU). The system receives targeted guidance instructions and information, such as vehicle maneuvering, safety maintenance, traffic control, and special conditions, to enhance driving tasks. The system also incorporates weather or special information in vehicle operation or control. The OBU receives vehicle-specific targeted weather information and/or special information from the TCC/TCU. The special information comprises activity, accidents, and hazards/obstacles information.