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As one of the most successful acquisitions in Intel’s history, Mobileye is a growth engine for INTC. Revenue has more than doubled since the acquisition and fully funds Mobileye’s development work on the autonomous future. Unique in the industry, Mobileye’s business model encompasses the entire automated driving value chain, including the front-facing camera that powers most of today’s advanced driver-assistance systems (ADAS), conditional autonomy – also known as level 2+ – and the self-driving system (SDS) for robotaxis and consumer autonomous vehicles (AVs). Mobileye is leading in every one of these categories with the industry’s most advanced vision sensing technology, crowd-sourced mapping capability and the Responsibility-Sensitive Safety (RSS) driving policy. Read on to learn more about our progress.


Recent development of hybrid-electric vehicles (HEV) and electric vehicles (EV) has accelerated innovation and improved efficiency in electric motor controls, power conversion, and battery management systems. However, the algorithms driving these systems require continuous upgrades and design changes to optimize performance.

ASIC development cycles are too long to meet these rapidly evolving market demands, and today’s microcontrollers (MCUs) are unable to keep up with escalating performance requirements. Intel® FPGAs deliver hardware failsafe logic for insulated gate bipolar transistor (IGBT) bridge protection, efficient motor control with our model-based DSP Builder for Intel FPGAs design flow, and hardware acceleration with faster control loops to improve energy efficiency, reduce noise, and improve the reliability of electrical motors.

You can use FPGAs or CPLDs anywhere you need DSP to improve system performance, such as: on-board charger, traction inverter, DC/DC converter, motor control system and battery management system.

To accelerate your time to market and increase productivity, Intel offers a variety of intellectual property (IP) and tools. Intel Motor Control IP includes Pulse-Width Modulation (PWM), Analog-to-Digital (ADC) and digital encoder interfaces, and integrated customizable field-oriented control (FOC) reference designs.  

Car Outline
Racing Driving

Automobile data analytics isn’t just about self-driving cars, data science and machine learning technologies can help keep auto organizations competitive by improving everything from research to design manufacturing to marketing processes.

Data science, machine learning, and ultimately AI, can improve efficiencies in every stage of automotive production, enabling organizations to cut costs, better serve customers, and perhaps most importantly develop new, innovative products.

Dataiku for the Automotive Industry

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. Hundreds of companies use Dataiku daily to build, deploy, and monitor predictive data flows. For automotive organizations, Dataiku brings:

Secure processing for data and creating machine learning models, with or without coding. User permissions are only useful when they’re enforceable, and Dataiku supports organization best practices surrounding secure data usage and storage. Dataiku supports analysis by non-technical users, cutting out inefficiencies and potential compliance concerns in traditional systems that require data teams to facilitate all access to data insights.


Productionalizable models that drive value. Unless machine learning models can be leveraged on a regular basis with real data, their insights are a curiosity at best. Dataiku provides a seamless environment for the entire data pipeline, from data cleaning to production.

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