The Importance of Data for Intelligent Driving Technology
The data output of intelligent driving sensors is generally divided into the following three types
The first is obstacle detection, obstacle tracking, and multi-sensor fusion. Smart Cloud-data crowdsourcing providers have been engaged in obstacle labeling for intelligent driving since 2015. In addition to the basic obstacle labeling capabilities of monocular and binocular cameras, fisheye cameras, and surround-view cameras, lidar point cloud data with different beams ranging from 4 lines to 128 lines can be annotated. In terms of multi-sensor detection(including lidar, camera fusion, and sensors such as millimeter-wave radar), multi-sensor annotation is feasible as well.
The second type is the environment perception outside the car and lane information. Some data-labeling platforms have also accumulated a wealth of labeling solutions, such as parking space recognition, road information, traffic signs, positioning elements, and drivable area semantic segmentation, as well as other types of data.
The third is the environment perception inside the car and the driver’s driving interaction. For the perception of the environment in the vehicle, certain crowd-sourcing providers have typical fatigue driving behavior detection ability, including the key point labeling and facial expression detection.
For intelligent driving technology, the role of data is even more critical
On one hand, the actual traffic scene is complex, and there are many safety threats. Autonomous driving attaches great importance to the efficiency and agility of data analysis. On the other hand, the quality of training data will directly affect the safety of autonomous driving. For example, the accuracy of data labeling of buildings, plants, traffic signs, and vehicles now determines the decision-making of automobile systems.
To understand the road, weather, and safety conditions, and react appropriately, autonomous vehicles need to use complex multi-dimensional data sets obtained from many sensors. Therefore, the team responsible for training the model faces the challenge of the supplier’s professionalism and the quality of the data labeling process. If they receive low-quality training data, they will inevitably waste a lot of time and resources used for internal audits — determine which parts of the data set need to be improved.
For example, to enable machine learning algorithms for multimodal self-driving cars, some manufacturers need to attach two distinct datasets with different dimensions. This operation is essential for developing self-driving car models, but it is challenging to perform manually. AI companies even hope that data companies can better understand algorithm technology and demand scenarios, participate in the research and development of algorithms, and give optimization suggestions on data collection.
Data Annotation Service in Self-driving Industry
The more accurate annotation is, the better algorithm performance will be. The mainstream algorithm model of autonomous driving is mainly based on supervised deep learning. It is an algorithm model that derives the functional relationship between known variables and dependent variables. A large amount of structured labeled data is required to train and tune the model.
On this basis, if you want to make self-driving cars more “intelligent”, and form a closed loop of the business model for self-driving applications that can be replicated in different vertical landing scenarios, the model needs to be supported by massive and high-quality real road data.
Common Data Labeling Types Include:
- 2D Bounding Boxes
- Lane Marking
- Video tracking annotation
- Point Annotation
- Semantic Segmentation
- 3D Object Recognition
- 3D Segmentation
- Sensor Fusion: Sensor Fusion Cuboids/Sensor Fusion Segmentation/Sensor Fusion Cuboids Tracking
A New Solution For the Self-Driving Data Annotation Project
ByteBridge, a human-powered and ML-powered data training platform provides high-quality services to collect and annotate different types of data such as text, image, audio, and video to accelerate the development of 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 machines and the human workforce
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%.
Communication Cost Saving
On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.
You can choose an Autopilot Annotation Template on the dashboard:
Control Your Own Project — 2D Images Labeling
In addition, researchers can create the data project by themselves, upload raw data, download processed results, check ongoing labeling progress simultaneously on a pay-per-task model with clear estimated time and take control over the project status.
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.
3D Point Cloud Annotation Types:
- Sensor Fusion Cuboids: 49 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 Time-aligned 2D images could be provided, point clouds outputs only.
Advantages of Our 3D Point Cloud Annotation Service:
· Support 2D/3D sensor fusion, support multiple cameras
· Support scalable data annotation
· AI-powered sensor fusion tool: labeling at 2X-5X speed
· Ease of use QC tool: real-time revision and synchronous feedback
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.
If you would like to have a look at the 3D point cloud live demo, please feel free to contact us: firstname.lastname@example.org
source: https://baijiahao.baidu.com/s id=1632049008796223978&wfr=spider&for=pc