The Intel® Automated Driving SDK Beta includes several task-specific modules to speed development of high-performance, power-efficient automated driving systems. Developers with approved access can customize their installations to include only the modules and tools they need.
Tools and Libraries
- Intel® System Studio Ultimate Edition
- Intel® C++ Compiler
- Intel® Math Kernel Library
- Intel® Data Analytics Acceleration Library
- Intel® Integrated Performance Primitives
- Intel® Threading Building Blocks
- Intel® VTune™ Amplifier
- Energy Analysis
- Intel® Inspector
- Intel® System Debugger
- GNU Project Debugger (GDB)*
- Intel® Autonomous Driving Library
- Intel® Deep Learning Deployment Toolkit
- Intel® FPGA SDK for OpenCL™ software technology
- Intel® Advisor
- Intel® Distribution for Python*
- Intel® MPI Library
- Intel® Trace and Analyzer Collector
For more information, see Optimization Tools Details.
|In-Vehicle Software Development||
Develop CPU-based in-vehicle applications that include sensor fusion, environment modeling, or trajectory planning. It contains core tools and libraries that help developers create, analyze, tune, and debug code, along with optimized system bring-up and validation.
|Data Center Software Development||
Build scalable, multinode data center applications for managing fleet data, building and validating reference algorithms, or facilitating machine learning. Includes tools and libraries to develop, analyze, tune, and debug code, plus data center software performance tools.
Likewise, you can use all the tools in the In-Vehicle Software Development module, except for the system debugger and energy profiler (Intel SoC Watch), which are not needed for data center development.
|FPGA Development Module||A simplified, scalable set of tools to develop and program FPGAs in the automated vehicle. Take advantage of an OpenCL application capable of performing optimizations on kernel code and producing the entire FPGA image in one step. A profiler shows performance opportunities in the kernel, along with a detailed optimization report. An emulator helps debug kernel functionality quickly and easily.
Intel® FPGA SDK for OpenCL™ Technology
|Deep Learning Deployment||Optimize deep learning models for deployment on autonomous vehicles built on flexible Intel hardware. Afterward, integrate your optimized models into your application.
Intel Deep Learning Deployment Toolkit
Deep Learning Model Optimizer
Ideal for autonomous driving application development, the following tools help developers build, debug, and tune code for targeted embedded systems. They include tools and libraries to efficiently optimize for performance and power efficiency, target and resolve defects, and improve machine learning performance.