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 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 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: 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: 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 method including providing a stream of content to a processor, transforming kernels within the stream of content through affine modeling, transforming the affine modeled kernels, stream contracting kernel processes, and stream blocking the kernel processes.

Abstract: A location to insert stack clearing code into a method to be executed in an execution environment of a computer system is determined. The stack clearing code is inserted into the location of the method. The stack clearing code is executed during execution of the method to clear a stack. Other embodiments are also described and claimed.

Abstract: A virtual machine in a processing system manages type information for operands. In one embodiment, the virtual machine accomplishes the following results through execution of a single instruction: adding an operand tag to a tag stack, and updating a stack pointer for the tag stack to recognize the addition of the operand tag to the tag stack. The single instruction may be a shift instruction, for example. The tag stack may reside in a tag stack register, and each operand tag may indicate whether a corresponding operand on an operand stack is to be treated as a reference operand or a non-reference operand. Other embodiments are described and claimed.

Abstract: Methods and apparatuses provide for referencing thread local variables (TLVs) with techniques such as stack address mapping. A method may involve a head pointer that points to a set of thread local variables (TLVs) of a thread. A method according to one embodiment may include an operation for storing the head pointer in a global data structure in a user space of a processing system. The head pointer may subsequently be retrieved from the global data structure and used to access one or more TLVs associated with the thread. In one embodiment, the head pointer is retrieved without executing any kernel system calls. In an example embodiment, the head pointer is stored in a global array, and a stack address for the thread is used to derive an index into the array. Other embodiments are described and claimed.

Abstract: A heap organization for a multitasking virtual machine is described. The heap organization may comprise an execution engine to concurrently execute a plurality of tasks and a plurality of heaps coupled to the execution engine. In some embodiments, the plurality of heaps may comprise a system heap and a task heap separated from the system heap. The system heap may store system data accessible by the plurality of tasks. The task heap may store task data only accessible by one task of the plurality of tasks.

Abstract: A multitasking virtual machine is described. The multitasking virtual machine may comprise an execution engine to concurrently execute a plurality of tasks. The multitasking virtual machine may further comprise a heap organization coupled to the execution engine. The heap organization may comprise a system heap to store system data accessible by the plurality of tasks; and a plurality of task heaps. Each of the plurality of task heaps may be assigned to each of the plurality of tasks to store task data accessible by the assigned task. The multitasking virtual machine may further comprise a heap manager to manage the heap organization. The heap manager may comprise a heap size controller to control heap size of the system heap.

Abstract: A method and apparatus for reducing the effect of soft errors in a computer system is provided. Soft errors are detected by combining software redundant threading and instruction duplication. Upon detection of a soft error, errors are recovered through the use of software check pointing/rollback technology. Reliable regions are identified by vulnerability profiling and redundant multi-threading is applied to the identified reliable regions.

Abstract: Executing an instruction on an operand stack, including performing a stack-state aware translation of the instruction to threaded code to determine an operand stack state for the instruction, dispatching the instruction according to the operand stack state for the instruction, and executing the instruction.

Abstract: A virtual machine in a processing system manages type information for operands. In one embodiment, the virtual machine accomplishes the following results through execution of a single instruction: adding an operand tag to a tag stack, and updating a stack pointer for the tag stack to recognize the addition of the operand tag to the tag stack. The single instruction may be a shift instruction, for example. The tag stack may reside in a tag stack register, and each operand tag may indicate whether a corresponding operand on an operand stack is to be treated as a reference operand or a non-reference operand. Other embodiments are described and claimed.

Abstract: In one embodiment, the present invention includes a cache memory having a plurality of cache lines to store data, in which at least some of the cache lines are adapted to store data in a compressed state. The cache memory also may include a first tag corresponding to each of the cache lines to indicate whether data in the corresponding cache line is compressible.

Abstract: A location to insert stack clearing code into a method to be executed in an execution environment of a computer system is determined. The stack clearing code is inserted into the location of the method. The stack clearing code is executed during execution of the method to clear a stack. Other embodiments are also described and claimed.

Abstract: Methods for optimizing stream operator processing by creating a system of inequalities to describe a multi-dimensional polyhedron, solving the system by projecting the polyhedron into a space of one fewer dimensions, and mapping the solution into the stream program. Other program optimization methods based on affine partitioning are also described and claimed.