How Data Labeling Services Empower Self-Driving Industry 2021? — Part2
In the last article, we introduce 2 giant self-driving companies, TESLA and BAIC, in this page, we’d like to present the third one Waymo.
In December 2016, Google’s driverless project became Waymo, a subsidiary of Alpha, Google’s parent company.
Since Waymo was founded, it has established a “zero-tolerance policy” — security. As the company’s mission mentioned, “let people and things move more conveniently and safely.”
Waymo, the research, and development company of automatic driving technology announced the details of its fifth-generation test car this year, which is based on the I-PACE, a pure electric vehicle owned by Jaguar. At present, this car is equipped with Waymo’s latest technology sensor, which can identify pedestrians and stop signs 500 meters away.
The fifth-generation system is equipped with multiple lidar sensors, the most prominent one is on the top of the vehicle. It has the ability to monitor the distance up to 500 meters and has a 360-degree angle monitoring range.
Waymo is clear about its own technical advantages. It covers all the design and development work of autopilot software and hardware, ranging from lidar, millimeter-wave radar, and sensor to system algorithm and high-precision map, so as to achieve seamless cooperation.
Specifically, Waymo’s system includes three kinds of independently developed lidars, one of which is to provide a short-range vision, one is to focus on high-definition medium range, the rest is to see long-range almost three football fields away. In addition, Waymo is equipped with a vision system composed of multiple high-definition cameras to deal with distant objects in the daytime and low light conditions; a radar system with a continuous 360-degree field of vision to detect the real-time speed of road participants in front, behind and on both sides; and many additional sensors such as an audio detection system that can hear the sirens of police cars and emergency vehicles.
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.
Common Data Labeling Types Include:
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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.
The labeler used to annotate point by point, which cost lots of time.
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. It is 100 times faster than before.
Thanks to an AI-assisted system, the corresponding parts can be automatic can be transcribed and the human only needs to check and modify the wrong parts.
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. Using AI-assisted tool can improve efficiency multiple times
Can we get rid of the human workforce?
The answer is no.
In fact, manually labeled data is less prone to errors regarding quality assurance and data exceptions.
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.
If the training data has a bias, the algorithm model cannot be well developed. In the auto industry, any minor bias in training data may cause a dreadful consequence. Facing the high human workforce budget, it is hard for car manufacturers to make a trade-off between cost and accuracy.
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, you 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.
If an in-house team costs a lot, can we go with outsourcing?
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 on the dashboard
Result preview: clients can get the results in real-time on the dashboard
Customer service: clients can communicate with data workers about the changes so that workers can respond quickly and make changes in workflow
In conclusion, in the Autocar 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.
ByteBridge is a human-powered and ML-powered data labeling tooling platform. We provide scalable, high-quality training data for ML/AI industry with flexible workflow.
Quality and Accuracy
- 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 our data acceptance and accuracy rate is over 98%
- The client can set labeling rules directly on the dashboard.
- The client can iterate data features, attributes, and task flow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation
For example, you can choose an Autopilot Annotation Template for your project:
Progress preview: client can monitor the labeling progress in real-time on the dashboard
- Result preview: client can get the results in real-time on the dashboard
- Real-time Outputs: client can get real-time output results through API. We support JSON, XML, CSV, etc., and we can provide customizable datatype to meet your needs.
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
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
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