How Data Labeling Services Empower Self-Driving Industry 2021? — Part1
Self-driving vehicles rely on artificial intelligence, visual computing, radar, monitoring devices, and GPS, enabling computers to operate vehicles automatically and safely without any human initiative. Most of the companies are still in the driving assistance stage.
As 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.
In this series, we’d like to introduce 3 giant self-driving companies.
Tesla’s AP function is automatic assistant driving, FSD function is fully automatic driving, it can be considered as the evolution of AP function hardware.
FSD (full self-driving computer), which can be considered as the evolution hardware of AP function, so that Tesla electric vehicle can realize the full automatic driving function.
Unlike other companies that develop autonomous driving technology, Tesla does not use expensive lidar but relies more on cameras, sensors, and artificial intelligence technology for recognition. Tesla has designed its own AI processor, which can recognize various objects or passers-by in the picture and guide the system to make correct driving actions.
Tesla’s autopilot system is undergoing large-scale system code rewriting, and AI Artificial neural network is absorbing more and more problems. The newly upgraded autopilot system also includes a more sophisticated labeling system to identify objects on the road.
The world’s first mass-produced vehicle model equipped with three laser radars, a complete automotive solution coming out
On April 15, 2021, the BAIC ARCFOX Alpha HI, equipped with Huawei’s automatic driving technology, conducted a public test ride in Shanghai, which is also the first public test ride of Huawei’s automatic driving technology in the world. Xu Zhijun, Huawei’s rotating chairman, said, “the R & D team told me that Huawei’s automatic driving can achieve 1000 kilometers without intervention in the urban area.
In addition, at the 2021 Shanghai auto show, the “Huawei inside” cooperation model provides the passengers with an autonomous driving experience in urban areas with dense vehicles.
According to the official introduction, the HI model is the world’s first mass-produced model equipped with three laser radars. It has Huawei’s highest level of automatic driving and covers all scenic spots in urban areas, high-speed roads, and parking lots.
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 video record 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).
Common applications include:
Aware of those challenges, ByteBridge moves a big step forward through its automated data collection and labeling platform. We provide high-quality training data for the machine learning industry.
- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
- All work results are completely screened and inspected by the machine and human workforce.
In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.
Configure Your Own Annotation Project
On ByteBridg’s dashboard, developers can set labeling rules directly, check the ongoing process simultaneously on a pay-per-task model with a clear estimated time and price.
For example, you can choose an Autopilot Annotation Template on the dashboard:
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
As the quality of the labeled dataset determines the success of the self-driving industry, cooperation with a reliable partner can help developers to overcome the data labeling challenges.
We can provide personalized annotation tools and services according to customer requirements.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: email@example.com
1The World’s First Driverless Law to Allow L4 Class Automatic Driving on the Road in Germany
2 Labeling Service Case Study — Video Annotation — License Plate Recognition
3 Eight Common Data Annotation and Labeling Tools in Autonomous Vehicle Industry
4 What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation? — Data Labeling Service In Self-driving Industry