NEW YORK, NY / ACCESSWIRE / August 26, 2020 / “I’m extremely confident that self-driving cars or essentially complete autonomy will happen, and I think it will happen very quickly,” Tesla CEO Elon Musk said in a virtual speech to the World Artificial Intelligence Conference in July, 2020. Musk mentioned Tesla will have basic functionality for level-five complete autonomy this year.
The self-driving vehicles is not just hot in Silicon Valley. In China, the largest automobile market worldwide, companies are also getting on board to develop autonomous driving technology, including China’s internet search tycoon Baidu, also referred to as the “Google of China.” Baidu has been developing the autonomous driving technology through its “Apollo” project (also known as open-source Apollo platform) launched in April 2017. Now the company announced the world’s first production-ready compute platform specifically for autonomous vehicles is ready for application.
Behind the self-driving: Machine learning and Data annotation
Before we talk about the feasibility about self-driving and autonomous technology, let’s make one question clear: how is self-driving possible?
In a nutshell, a self-driving car should be able to sense its environment and navigate without human intervention. Self-driving vehicles depend on hardware and software to drive down the road. The hardware collects data and software processes it through machine learning algorithms that have been trained in real-world scenarios. Simply put it, it is machine learning technology that plays a vital role in the self-driving industry. Machine learning algorithms, sensors and graphics processing devices have integrated into a smart driving neural network, or “brain”.
First and foremost, the smart “brain” needs to learn image verification and classification, object detection and recognition, as well as traffic rules, weather conditions. Engineers “teach” these situations by feeding the machine learning models millions of labeled images to make it adept at analyzing dynamic situations and acting on their decisions.
With the tremendous amount of raw data required for machine learning algorithms, and the need for accuracy, high quality data annotation is crucial to ensure that autonomous vehicles are safe to use for public.
Going back to Tesla, this company uses cameras for visual detection, each car equipped with 8 surround cameras. If a Tesla user drives one hour a day on average, considering more than 750,000 Tesla cars around the world, about 180 million hours of video can be generated per month.
Tesla Autopilot project has included 300 engineers plus more than 500 skilled data annotators. The company plans to enlarge the data annotation team to 1,000 people to support the data process. Elon Musk admits during an interview that data annotation is a tedious job, and it requires skills and training, especially when it comes to 4D (3D plus time series).
A new solution for data annotation market
It’s becoming challenging for the machine learning and AI companies to internally meet the burgeoning demand of high-quality data annotation.
ByteBridge.io has provided an innovative solution to empower the machine learning revolution through its automated and integrated data annotation platform. Built by developers for developers, ByteBridge.io has applied blockchain technology to the data processing platform where developers can create the project by themselves, highlight specific requirements, check the ongoing process simultaneously on a pay-per-task model with clear estimated time and price.
In order to reduce data training time and cost when dealing with complicated tasks, ByteBridge.io has also built up the consensus algorithm rules to optimize the labelling system and improve the accuracy of final data delivery.
Self-driving technology is going to transform the transportation industry, social and daily lives. It’s hard to know when that day will arrive. But one thing for sure is that with top data service companies, such as ByteBridge.io, to fuel the machine learning and autonomous industry, the intelligent future is edging closer into reality.