Intel Corporation

Uisee Technologies (beijing) Ltd

Jinzhan Peng

Xin Zhou

Gansha Wu

Guei-Yuan Lueh

Dan Zhang

Yan Jiang

Xiaocheng Zhou

Sai Luo

Zhiwei Ying

Peng Guo

Xiaohua Shi

Ali-Reza Adl-Tabatabai

Anwar Ghuloum

Shih-wei Liao

Zhaohui Du

Ken Lueh

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.

Speed planning method and apparatus and calculating apparatus for automatic driving of vehicle

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.

Steering control method and system of self-driving vehicle

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 (S); 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 (S); 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 (S). The present disclosure effectively solves the problem that a model trained by means of machine learning cannot be locally adjusted and easily modifies the decision of a certain partition without affecting the decision results of other partitions at all. The intuitive nature of a partition decision table can effectively help to find and solve problems in the machine learning process. The partition decision table can speed up the decision process.

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.

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.

Computation transformations for streaming applications on multiprocessors

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.

Inserting stack clearing code in conservative garbage collection

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.

System and method for managing a register-based stack of operand tags

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.

Method and apparatus for referencing thread local variables with stack address mapping

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.

Code interpretation using stack state information

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.

Compressing data in a cache memory

A system of permitting stack allocation in a program with open-world features is described. The system includes an escape analysis module to (1) determine which objects of the program can be stack-allocated under a closed-world assumption and (2) analyze, after stack allocation, which stack allocation is invalidated due to the occurrence of an open-world feature. A stack allocation module is provided to stack-allocate these objects based on the determination of the escape analysis module. A stack allocation recovery module is provided to recover those invalidated stack allocations back to their original allocation in heap based on the analysis of the escape analysis module. A method of permitting stack allocation in a program with open-world features is also described.

Growing community of inventors

Beijing, China

Jinzhan Peng