Abstract: The present disclosure provides a technical solution related to tagging user behavior data. The processing device and method may determine and tag user behavior data according to an application to which the user behavior data belong based on analysis on an inheritance relationship between tasks associated with the user behavior data, so as to facilitate selectively deleting the user behavior data later.

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model; partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result; and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result.

Abstract: The present disclosure provides for a steering control method and a steering control system for self-driving of a vehicle. The method comprises the steps of: obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation; detecting whether an effective torque is applied to a steering wheel by a driver; and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system.

Abstract: The present disclosure provides a technical solution related to tagging user behavior data. The processing device and method may determine and tag user behavior data according to an application to which the user behavior data belong based on analysis on an inheritance relationship between tasks associated with the user behavior data, so as to facilitate selectively deleting the user behavior data later.

Abstract: An objective of the present application is to provide a road characterization method, comprising: acquiring a target road, wherein the target road comprises one or more road segments; determining one or more atomic lanes corresponding to the road segments according to the road segment attribute information of the road segments; and determining lane association information related to the atomic lanes corresponding to the target road; wherein the one or more atomic lanes and the lane association information are used for characterizing the target road. The method can functionally completely describe objective connections of lanes within a target area, including lane connectivity and lane change feasibility.

Abstract: An objective of the present application is to provide a road characterization method, comprising: acquiring a target road, wherein the target road comprises one or more road segments; determining one or more atomic lanes corresponding to the road segments according to the road segment attribute information of the road segments; and determining lane association information related to the atomic lanes corresponding to the target road; wherein the one or more atomic lanes and the lane association information are used for characterizing the target road. The method can functionally completely describe objective connections of lanes within a target area, including lane connectivity and lane change feasibility.

Abstract: The present disclosure provides for a steering control method and a steering control system for self-driving of a vehicle. The method comprises the steps of: obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation; detecting whether an effective torque is applied to a steering wheel by a driver; and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system.

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model; partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result; and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result.

Abstract: A processing device includes a processing core, coupled to a memory, to execute a task including a code segment identified as being monitored and a kernel recorder, coupled to the processing core via a core interface. The kernel recorder includes a first filter circuit to responsive to determining that the task being executed enters the code segment, set the kernel recorder to a first mode under which the kernel recorder is to record, in a first record, a plurality of memory addresses accessed by the code segment, and responsive to determining that the execution of the task exits the code segment, set the kernel recorder to a second mode under which the kernel recorder is to detect a write operation to a memory address recorded in the first record and record the memory address in a second record.

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model (S110); partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result (S120); and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result (S130).

Abstract: A steering control method and a steering control system for self-driving of a vehicle are disclosed. The method comprises obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation; detecting whether an effective torque is applied to a steering wheel by a driver; and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system.

Abstract: Disclosed are a steering control method and a steering control system (100, 200, 300) for self-driving of a vehicle. The method comprises the steps of: obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation (S410); detecting whether an effective torque is applied to a steering wheel by a driver (S420); and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system (S430).

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model (S110); partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result (S120); and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result (S130).

Abstract: A processing device includes a processing core, coupled to a memory, to execute a task including a code segment identified as being monitored and a kernel recorder, coupled to the processing core via a core interface. The kernel recorder includes a first filter circuit to responsive to determining that the task being executed enters the code segment, set the kernel recorder to a first mode under which the kernel recorder is to record, in a first record, a plurality of memory addresses accessed by the code segment, and responsive to determining that the execution of the task exits the code segment, set the kernel recorder to a second mode under which the kernel recorder is to detect a write operation to a memory address recorded in the first record and record the memory address in a second record.

Abstract: A computer system may comprise a computer platform and input-output devices. The computer platform may include a plurality of heterogeneous processors comprising a central processing unit (CPU) and a graphics processing unit (GPU) and a shared virtual memory supported by a physical private memory space of at least one heterogeneous processor or a physical shared memory shared by the heterogeneous processor. The CPU (producer) may create shared multi-version data and store such shared multi-version data in the physical private memory space or the physical shared memory. The GPU (consumer) may acquire or access the shared multi-version data.

Abstract: Embodiments of the invention provide a programming model for CPU-GPU platforms. In particular, embodiments of the invention provide a uniform programming model for both integrated and discrete devices. The model also works uniformly for multiple GPU cards and hybrid GPU systems (discrete and integrated). This allows software vendors to write a single application stack and target it to all the different platforms. Additionally, embodiments of the invention provide a shared memory model between the CPU and GPU. Instead of sharing the entire virtual address space, only a part of the virtual address space needs to be shared. This allows efficient implementation in both discrete and integrated settings.

Abstract: Embodiments of the invention provide a programming model for CPU-GPU platforms. In particular, embodiments of the invention provide a uniform programming model for both integrated and discrete devices. The model also works uniformly for multiple GPU cards and hybrid GPU systems (discrete and integrated). This allows software vendors to write a single application stack and target it to all the different platforms. Additionally, embodiments of the invention provide a shared memory model between the CPU and GPU. Instead of sharing the entire virtual address space, only a part of the virtual address space needs to be shared. This allows efficient implementation in both discrete and integrated settings.

Abstract: Various embodiments are generally directed an apparatus and method for configuring an execution environment in a user space for device driver operations and redirecting a device driver operation for execution in the execution environment in the user space including copying instructions of the device driver operation from the kernel space to a user process in the user space. In addition, the redirected device driver operation may be executed in the execution environment in the user space.

Abstract: An audio accelerator includes a decoder to decode first and second sets of data blocks, a processor to process the first and second sets of decoded data blocks, a storage area to store the first and second sets of processed data blocks, and a controller to generate interrupt signals for controlling operation of the decoder. The controller may control a rate at which data blocks are to be decoded by the decoder to reduce a time gap between outputting adjacent ones of the data blocks from the first and second sets in the storage area.

Abstract: Systems and methods may provide for using audio output device driver logic to maintain one or more states of an audio accelerator in a memory store, detect a suspend event, and deactivate the audio accelerator in response to the suspend event. In addition, firmware logic of the audio accelerator may be used to detect a resume event with respect to the audio output accelerator, and retrieve one or more states of the audio accelerator directly from the memory store in response to the resume. Thus, the retrieval of the one or more states can bypass the driver logic.