How Data Labeling Services Empower Self-Driving Industry 2021? — Part4
If you are not as paranoid as Musk, automatic driving may not need to divide any technical routes, but only need to optimize the technology. But standing on the opposite side of lidar, Tesla may have missed the best time to develop fully autonomous driving.
Lidar is not to replace millimeter-wave radar and vision, but to match with other sensors as a heterogeneous sensor. Through these three different sensors, a heterogeneous fusion can be made to ensure the overall perception security and improve sensitivity and accuracy.
Different from the traditional mechanical rotary lidar, Suteng, a Chinese company mainly adopt MEMS technology, which has the advantages of small volume, easy integration, low energy consumption, and low cost.
The traditional mechanical lidar needs to achieve a high-precision point cloud. The more scanning wire harness, the better. As the number of wire harnesses requires the number of transmitting and receiving modules, the size can’t be too small. But MEMS lidar can be integrated into a small chip, which is more flexible and easy to install.
However, MEMS has some limitations in resolution and detection distance, but there are also corresponding technical solutions, and its biggest advantage is the cost.
But Tesla has “abandoned” the millimeter-wave radar a step further. On May 25, Tesla officially announced that from May this year, model 3 and model y made in North America will no longer be equipped with millimeter-wave radar. These models will support autopilot, FSD fully automatic driving, and some active safety functions through Tesla’s camera vision and deep neural network.
Musk once explained why he didn’t like lidar: “at radar wavelengths, the real world looks like a strange ghost world. Almost everything is translucent except metal. “ He insists that vision is more accurate.
However, in terms of Tesla visual route car accidents, the limitation is that it can’t recognize white and stationary obstacles, which means that visual route or millimeter-wave radar can only achieve the effect of “auxiliary driving”.
“The auxiliary driving is considered as the most perfect redundancy of drivers. Whenever there is a problem with the system, the driver has the responsibility.
“This design is very problematic for autonomous driving. Because vision is a passive sensor, it is greatly affected by ambient light. For places with amblyopia, night, and other situations, there would be missing visual inspection, which has a great impact on the industry and safety.
Previously, Google pointed out that vision recognition technology can detect with 99% accuracy, but the rate needs to be up o 99.99999%. Lidar is to solve the problem.
“In the scene of the parking lot, the situation is really very complex, for instance, pushing shopping carts and children, which can’t be detected by millimeter-wave radar. In this case, lidar has its advantages.”
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Since NIO launched the first flagship car et7 in January this year and the claim that it would carry the lidar products provided by INNOVUSION, the industry has begun the race of “mass production lidar models”.
In April, Xpeng launched the first P5 lidar model and said that it would be delivered in mass production within this year, there was less doubt about whether the lidar could be mass-produced.
In fact, not only technology goes faster than we thought, but costs are falling faster. It is said that the cost of vehicle-grade laser radar goes down from the initial tens of thousands of dollars to a few hundred dollars.
It is believed that by 2025–2026, the lidar-equipped models will really reach a million scale market, and the model price can be reduced by 40% — 50%.
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:
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 Launches World’s First Mobile 3D Point Cloud Data Labeling Service
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
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