Mobileye Details "Human-like" Algorithmic Driving Platform for Autonomous Cars at CES 2017

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【Summary】Mobileye, a company that specializes in optimized sensing platforms for driverless vehicles, has taken a completely different approach to such traditions by revealing various components of its product in great detail at CES 2017 (“Artificial Intelligence for Autonomous Driving: State of Technology, Breakthroughs, and Why Alliances are Valuable”).

Original   Michael Cheng  ·  Jan 05, 2017 1:15 PM PT
author: Michael Cheng   

Many developers in the self-driving car sector hide their technology from the public. Mobileye, a company that specializes in optimized sensing platforms for driverless vehicles, has taken a completely different approach to such traditions by revealing various components of its product in great detail at CES 2017 ("Artificial Intelligence for Autonomous Driving: State of Technology, Breakthroughs, and Why Alliances are Valuable").

At the start of the one-hour press conference, Prof. Amnon Shashua (CTO and Co-founder of the company) elaborated on the three pillars of autonomous driving:

·      Sensing: From forward-sensing capabilities to 360-degree awareness; environmental models of one's surroundings

·      Mapping: A combination of HD maps and localized maps at 10cm accuracy; main issue includes deployment of updates

·      Driving Policy: "Human-like" negotiation skills via algorithmic processes and machine learning (explained below)

According to the Mobileye CTO, currently, most self-driving systems are programmed to be defensive. This is great for maintaining safety, but ineffective for road engagement. For example, driverless cars that merge into traffic must be able to identify an opening on the road in a slightly aggressive (but safe) manner. Prof. Shashua highlighted the balance between "human negotiation" and "safety" is key to advancing driverless platforms. For autonomous driving, this level of negotiation must take place visually via "motion."

car_start.jpeg

This level of negotiation on public roads can be made possible through machine learning. The old "rule-based approach" to assessing road scenarios is too simple for self-driving platforms, resulting in "simplistic driving polices." Mobileye, through machine learning, aims to streamline the observation and collection of data to solve driving policy issues in real-time.

With this in mind, the speaker clarified that SAE L3 autonomous platforms are only for highways, requires HD maps and features non-instantaneous Take Over Request (TOR) times. When it comes to sensing components for L3 self-driving vehicles, the vessels are equipped with a "cocoon" radar, front-facing LIDAR unit and tri-focal or wide-angle cameras.

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