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From safe roads to enjoyable commutes, automated driving is poised to change lives and society for the better.
As the car moves to the center of the autonomous world, developers will be tasked with creating innovative and seamless solutions to swiftly respond to and grow with market demands. This requires some serious resources in both the vehicle and data center. Intel already has an ecosystem built with you in mind. With these tools, you'll be able to create—and recreate—the experience of driving.
Automotive basics
 | Automated Driving Levels
Highly Automated Driving (HAD) — Supports the driver with Advanced Driver Assistance Systems (ADAS). These systems entail navigation, safety, security monitoring, image recognition and processing, and sensor data—the data processing hub.
Fully Automated Driving (FAD)— Puts the driver in the passenger seat with onboard intelligence.
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Data Center
Data generated by a single automated car equals the data generated by nearly 3,000 people. The data center will be crucial for storing, sharing, and protecting the enormous volume of data generated from deep learning algorithms and keeping the automated vehicle on the road.
5G
More than just raw speed, 5G connectivity delivers ultra-low latency at gigabit speeds and high bandwidth. This allows for intelligent and agile networks to give priority to the safety-critical devices required by the automated vehicle.
Human Machine Interface
A software-defined cockpit is the consolidation of cluster displays with in-vehicle infotainment (IVI) systems. This approach seamlessly merges IoT-connected experiences, both inside and outside the vehicle, into a centralized communications, command, and control console that automatically conforms to the needs of individual drivers. The software-defined cockpit is becoming the one-stop shop for:
- Media management
- Ubiquitous availability and connectivity
- Security systems and cloud connections
- Human-machine interface (HMI) designs that build trust between driver and vehicle
In-vehicle computing
As the automated vehicle evolves, it will rely more on sensors, data, and processing power. A vehicle generates nearly one gigabyte of sensor data per second. Intel's portfolio of power-efficient processors and field programmable gate arrays (FGPAs) are designed to deliver high computer performance per watt.
Solutions for Automated Driving
Creating an accident free experience
The complexity of a car learning and navigating through its environment takes more than raw processor power. For a car to sense, learn, and make proper decisions, it needs deep-learning algorithms and ways to observe its surroundings.
Building a safe and autonomous vehicle requires
- A deep-learning foundation for Advanced Driver Assistance Systems (ADAS) algorithms.
- Ability to process enormous amounts of environmental data.
- Ability to sense surroundings.
- Access for developing in common computer vision routines
- Providing inference data gathered from cameras
- Methods for extracting features from video and tracking them
Intel® Deep Learning SDK
For algorithm and data issues with the help of these frameworks:
- Intel® Distribution of Caffe* — A popular community application for image recognition. (GitHub*)
- Intel® Software Optimization for Theano* — A Python* library used for model writing and training. (GitHub)
Get the SDK
The Intel® Computer Vision SDK for vision-specific problems.
Sign Up for the Preview
Taming the Mountains of Data
An automated vehicle generates, consumes, and processes an enormous amount of data. You will need tools for acceleration and optimization of data processing.
Intel® Data Analytics Acceleration Library(Intel® DAAL) — Develop performance improvements for machine learning, especially for time-series data of automotive controller area networks (CAN).
Intel® MPI Library — This library provides distributed computing (multinode) analytics and is used for simulation performance improvement in the automated driving data center.
These tools are available in Intel® Parallel Studio XE.
Embedding Code Quickly and Easily
The devices in the automated vehicle, in both the cockpit and trunk, require a simple and scalable way to be programmed. By using a field-programmable gate array (FPGA), you can:
- Take advantage of an OpenCL™ application capable of performing optimizations on the kernel code and producing the entire FPGA image in one step
- Use a profiler that shows performance opportunities in the kernel, along with a detailed optimization report
- Run an emulator to debug the kernel functionality quickly and easily
These features are available in Intel® FPGA SDK for OpenCL™ technology.
Eliminate Bottlenecks and Inefficiencies
Many devices create the ecosystem that brings the automated vehicle to life. With the complexity of many components working together comes the reality of data bottlenecks, especially when it comes to connecting to the remote data center.
Acceleration and Profiling Tools
Intel® Trace Analyzer and Collector — A graphical tool that allows you to visualize data and profile the load balance in your data center.
Intel® Distribution for Python* — Used for code performance improvements in the automated data center.
These tools are available in Intel® Parallel Studio XE.
Intel® Math Kernel Library (Intel® MKL) — Accelerates math processing and neural network routines to increase vehicle computing performance.
Intel® VTune™ Amplifier — Offers additional profiling tools, such as monitoring the performance of all the processors in the automated car, as well as powerful tools to tune OpenCL™ applications and GPUs.
These tools are available in Intel® System Studio 2017.
Connect the Vehicle to the Driver
The automated car needs to interact with its driver in the cockpit and make sense of the data it gathers from other sensors. This data needs to be processed in a timely and efficient manner.
Intel® Integrated Performance Primitives (Intel® IPP) — A suite of high-quality, production-ready, low-level building blocks. These are ready made solutions for the automated vehicle's image processing, signal processing, and data processing needs.
These tools are available in Intel® System Studio 2017.
The Future and Beyond
We're taking our vision of the automated vehicle to another level with robust and fast developer tools.
Intel® GO™ Automotive Software Development Kit (SDK) Technical Preview
This kit will consolidate existing tools in one place, as well as expand the toolbox with the latest in automated driving technology. Included in the first release are:
- A new Intel® Computer Vision SDK
- An integrated Intel® Deep Learning SDK
- A sensor data labeling tool
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