Artificial intelligence is one of the information technology in the new generation, its application has gradually penetrated into all aspects of human life, promoting the development of transportation, medicine, and other fields. Let‘s talk about the popular AI application scenarios in transportation.
Using advanced science and technology to transportation, service control, and vehicle manufacturing, Intelligent Traffic System(ITS) strengthens the connection among vehicles, roads, and users, so as to form a qualified and comprehensive safety system with efficiency.
Intelligent transportation is considered as a model in the combination of Internet and equipment, which can fully meet people’s travel needs. Smart transportation is a promising industry with high values as it further promotes the development of other related industries
The development of AI needs the support of algorithms, computing power, and training data. The map product itself has a natural fit with AI.
On the one hand, the map is the mapping of the real world. Dataset is accumulating increasingly as online and offline human action tracks are reflected through this product. The data includes passengers’ travel records, PB level satellite map data, and global street view image data.
On the other hand, the function of the map itself can also be applied by a large number of AI. For example, a smart city can’t move forward without traffic and people flow track. Automatic driving also needs to combine a high-precision map with a radar sensor.
AI technology not only can monitor real-time traffic situations but also provide real-time weather conditions. Therefore, people can better plan the travel route.
Just as a triangle needs three sides to stabilize its shape, artificial intelligence will also need all three elements to perfect itself. In fact, getting high-quality labeled data is the toughest part of building a machine learning model.
ByteBridge is a human-powered and ML-powered labeling and collection and platform with robust tools and real-time workflow management.
- 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 machines and the human workforce
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%
In addition, we can provide personalized annotation tools and services according to customer requirements.
We comply with principles and rules in each region and we respect data the way your company does.
- The CEO of the company supervises data management as a DPO (Data Protection Officer)
- According to the guideline, if there is data leakage, we will inform the customer within 72 hours
- GDPR personal privacy and data protection regulations compliance
- Workers location, process, and authority restriction
- No original data leak as the data is compressed and preprocessed
- Support private cloud and privatization deployment
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
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: firstname.lastname@example.org
1 Data Annotation Service — How an Automated Data Labeling Platform Fuels Autonomous Vehicles Industry?
2 Labeling Service Case Study — Video Annotation — License Plate Recognition
3 Eight Common Data Annotation and Labeling Tools in Autonomous Vehicle Industry
4 The Best Data Labeling Company in 2021