How Data Annotation Services Fuel Self-Driving Industry 2021? — Part2
2021 is the Year of the Autonomous Lidar.
The new forces of the mainstream car building adopt lidar scheme on the intelligent electric vehicles. In traditional car factories, such as Audi, BMW, Nissan, Toyota, and so on, lidar will be applied, but no specific timetable has been released.
Contrary to the concentration of a large number of car companies on lidar, Tesla CEO mask has said more than once publicly that lidar is a stupid choice.
It is told that there is no dispute about whether lidar is advanced or not. In the academic field, the principle is lidar is advanced if it can provide more and more accurate data.
Compared with visual recognition, lidar provides a stronger perception. In the academic research field, everyone is in favor of lidar deployment. At present, many mapping in high-precision is also completed by lidar. It is more reasonable to choose lidar on the automatic driving as the project relies on a high-precision map.
Tesla is the only representative of pure vision in the mainstream car companies in the world.
Why does Tesla move against the trend？
The most impressive reason is that Tesla believes that as people rely on two eyes to judge the surrounding environment, in order to achieve similar driving behavior, we need to rely on vision recognition as well. Radar is a machine and it is easy to generate mechanical sensitivity.
Behind the explanation by Tesla, the real reason is that visual recognition is Tesla’s comfort zone. Tesla first adopted a visual scheme, quickly deployed the autopilot function to mass production vehicles, which brought Tesla a huge amount of data, and significantly exceeded its competitors in algorithm improvement, and developed special chips for this purpose. Data, algorithm, chip, Tesla has formed a multi-layer protective umbrella. In the visual field, with the protection of these advantages, no one can shake Tesla’s dominant position.
Another reason that Tesla didn’t choose lidar at first, because it was expensive and unstable at that time.
Back in 2015, a lidar cost $125200 with a life span of only three months. Such cost and technical status couldn’t meet the mass production in any case. In consequence, Tesla abandoned lidar when it first developed autopilot.
2. How AI Will Power the Next Wave of Healthcare Innovation?
3. Machine Learning by Using Regression Model
4. Top Data Science Platforms in 2021 Other than Kaggle
The debate between vision and laser is not very large, only because Tesla has formed a multi-layer wall in the visual field and the sales lead. Hence, it becomes a controversial topic in public.
Now, the cheapest laser radar costs less than $500 with stronger perception.
On May 20, 2021, the US media INSIDEEV released news that someone photographed the Tesla Model y test run a car on the streets of California, with a special license plate for the automobile manufacturer, equipped with laser radar.
MFG on the license plate represents the license plate issued by the California government to the automaker.
This means Tesla has begun to consider the laser lidar technology route. According to one radar expert, The lidar manufacturer should be Luminar, and the product type Hydra is specially used for R & D and testing.
From the current choice of most car companies, we could affirm that lidar will occupy the mainstream position in the future. However, the current technical condition is not mature enough that the mainstream car companies are waiting for better solutions.
There are clear answers to the battle between vision and laser. However, there are still no clear answers either algorithm or calculation force is more important for automatic driving.
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.
For technology-type car companies, from 0 to 1 has been completed, but they still need to reduce costs and increase efficiency and move towards larger-scale production.
The goal has been settled, that is, to improve the capacity of large-scale mass production and commercial profitability, and to “feed” technology with massive data.
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:
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 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.
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.
3D Point Cloud Annotation Service
ByteBridge self-developed 3D Point Cloud labeling, quality inspection tool, and pre-labeling functions can complete high-quality and high-precision 3D point cloud annotation for 2D-3D fusion or 3D images provided by different manufacturers and equipment, and provide one-station management service of labeling, QA, and QC.
More info: ByteBridge Launches World’s First Mobile 3D Point Cloud Data Labeling Service
3D Point Cloud Annotation Types:
- Sensor Fusion Cuboids: 12 categories include car, truck, heavy vehicle, two-wheeled vehicle, pedestrian, etc.
- Sensor Fusion Segmentation: obstacles classification, different types of lanes differentiation
- Sensor Fusion Cuboids Tracking
① Tracking the same object with the same ID, labeling the leaving state;
② Point clouds or time-aligned images could be provided, point clouds outputs only.
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
1 The World’s First Driverless Law to Allow L4 Class Automatic Driving on the Road in Germany
2 Is Lidar the Future of the Self-Driving Industry2021?
3 Didi Autopilot Has Completed a New Round of Financing of over US $300 million
4 High-Quality Training Data for Autonomous Cars in 2021
5 The World’s First Mass-Produced Automotive Vehicle Coming out