Data Annotation Service — How a Data Labeling Service Fuels Autonomous Vehicles Industry?

Published in

Nerd For Tech

5 min readFeb 26, 2021

Hot Topic — Self-Driving Vehicle

“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 would have basic functionality for Level5 complete autonomy in 2020.

The self-driving vehicle topic is not just hot in Silicon Valley.

In China — the largest worldwide automobile market, companies are also getting on board to develop autonomous driving technology, including China’s internet search tycoon Baidu(also known as Google of China). Baidu has been developing autonomous driving technology through its “Apollo” project (also known as the open-source Apollo platform)which was launched in April 2017. Now the company announced that the world’s first production-ready platform for autonomous vehicles was ready.

More read: Mass-production Unmanned Vehicle Apollo Moon with the cost of $74,160

People can’t help asking — What makes the autonomous vehicle possible?

Behind the Self-Driving — Machine Learning and Data Annotation

Machine Learning

Machine learning algorithms, sensors, and graphics processing devices have been integrated into a smart driving neural network, or we can call it the smart “brain”. The “ smart brain” needs to learn image verification and classification, object detection and recognition, traffic rules, as well as weather conditions. Engineers develop the “smart brain” by feeding the machine learning models millions of labeled images to make the smart “brain” adept at analyzing dynamic situations and acting on their decisions.

A self-driving car should be able to sense its environment and navigate without human intervention. Autonomous vehicles depend on hardware and software while driving down the road. The hardware collects data and software processes it through machine learning algorithms that have been trained in real-world scenarios millions of times.

Data Annotation

The more accurate annotation is, the better algorithm performance will be. Any tiny error during a driving experience may lead to dreadful results. Nowadays, people are more and more concerned about the driving safety issue as several self-driving automobile accidents happened.

With the tremendous amount of training data and the high accuracy requirement, a high-quality data annotation service is crucial to guarantee autonomous vehicles are safe for the public.

Back to Tesla, this company uses cameras for visual detection, each car is equipped with 8 surround cameras. There are more than 750,000 Tesla cars around the world. If a Tesla user drives one hour a day on average, 180 million hours of data 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 in order to support the data process. In an interview, Elon Musk admits that annotation is a tedious job, and it requires skills and training, especially when it comes to 4D (3D plus time series).

Data Annotation Types in Self-driving Industry Include:

2D Bounding Boxes
Lane Marking
Semantic Segmentation
Video tracking annotation
Point Annotation
3D Object Recognition
3D Segmentation
Sensor Fusion: Sensor Fusion Cuboids/Sensor Fusion Segmentation/Sensor Fusion Cuboids Tracking

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Data Annotation Service in Self-driving Industry

Self-driving technology is going to transform the transportation industry, social and daily lives. It’s hard to know when that day will arrive. As life is priceless, we have to seek perfection from the beginning. It’s challenging for self-driving manufacturers to internally meet the burgeoning demand for high-quality data annotation.

AI-assisted Tool

3D annotation and video annotation are considered as the toughest services in data labeling. At present, object tracking algorithms based on machine learning have already assisted video annotation. The annotator annotates the objects on the first frame, and then the algorithm tracks the ones in the subsequent frames. The annotator only needs to adjust the annotation when the algorithm doesn’t function well.

Nowadays, some AI-assisted tools come to practice, standing out in 2 factors.

Cost reducing: With the help of AI-assisted capabilities, clients can save more money as the labor cost goes down.
Time reducing: Make the large-scale requirement of training data done in a short time.

Can we get rid of the human workforce?

The answer is no.

The human workforce cannot be totally replaced by some tools leading with an AI-based automation feature, especially dealing with exception, edge cases, complex data labeling scenarios, etc.

An In-house Team or Go With Outsourcing?

As mentioned, data accuracy is vital in the car industry, here comes another question: Should I build up an in-house team?

Before making the final decision, we have to keep 2 points in mind:

1. Complex process: including annotation tools and data pre-processing built-up, labeler performance training and following, data validation and quality check, etc.

2. High financial involvement: such as infrastructure labor cost, R&D, etc.

Compared to in-house infrastructure, outsourcing service needs effective communication and fast feedback. It is very important for manufacturers to choose the right one who can serve as “an extension department” of their company.

The Following Parts Should Also be Taken Into Account:

Progress preview: clients can monitor the labeling progress in real-time

Result preview: clients can get the results in real-time

Customer service: clients can communicate with project managers about the changes so that workers can respond quickly and make changes in workflow

In conclusion, in the self-driving industry, we rely much on the human workforce. Therefore, in terms of outsourcing partner choosing, we have to make sure of the flexible engagement in the labeling loop as we need labelers to respond quickly and make changes in workflow, based on the model testing and validation phase.

End

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