Typical Application Scenarios of Autonomous Driving
Typical application scenarios where autonomous driving can be commercialized include independent taxi scenarios, warehousing, logistics, ports, docks, fixed-route area connections, sweepers, mining areas, etc. The following is a simple analysis of three representative landing scenes.
Application scenario 1: RoboTaxi (autonomous driving taxi)
Some time ago, Chinese companies such as Baidu, AutoNavi, Weride, AutoX, and Didi rushed to send good news in the field of RoboTaxi (self-driving taxi). It can be said that through the landing of RoboTaxi, human beings have opened a new page in the autonomous driving industry.
From point to point, then to the region, from targeting specific groups to more public groups, from free tests to charged trials, RoboTaxi is moving closer to ordinary travel services.
Although RoboTaxi has the advantage of being closer to the market, the process of “perception-decision” of autonomous driving systems is more difficult. It has higher requirements for scene-oriented training data and faces the pain points — lack of scene-oriented road data and poor data quality. Companies need high-quality scene-based AI data, which requires structured video, image, audio, text, LiDAR data.
Application Scenario 2: Unmanned Delivery
As a “star” during the epidemic prevention and control period, unmanned delivery robots have taken the challenge to the foreground and played an important role in contactless delivery. During the epidemic, consumer demand for intelligent, contactless services has increased largely, and the industry has begun to develop the potential of autonomous driving technology in the field of unmanned delivery.
Of course, unmanned delivery is nothing new. Before the epidemic, the main application scenarios were logistics parks, warehouses, communities, office buildings, etc. It can be seen that limited by technical difficulties, the goal of the current uncrewed delivery vehicle is still to solve the last-mile delivery problem. Freight and fixed traveling lines are still prominent features.
Application Scenario 3: Warehousing and Logistics
Logistics distribution is divided into long-distance trunk transportation and short-distance distribution.
Long-distance highway transportation overload, fatigue driving and traffic violations occur occasionally, however, the characteristics of expressways such as fast speed, no signal lights, and no obstacles are in favor of high efficient movement, which is very suitable for automatic driving.
Short-distance distribution often occurs in relatively closed scenarios such as warehouses, factories, and residential areas. Autonomous driving robots can accurately and efficiently undertake low-risk sorting, warehousing, outgoing, and delivery, saving the labor cost and reducing the error rate.
Taking the short-distance distribution warehousing logistics as an example, compared to Robotaxi, autonomous driving robots are more inclined to use computer vision technology to implement functions. On the premise of maintaining the data accuracy, the application’s requirement for AI data is relatively uniform. Scenario-based AI data training can help autonomous driving robots realize intelligent recognition and automatically classify the information to realize automatic sorting, etc.
The Industrialization Focus of Autonomous Driving Scenarios: AI Data
2D-3D fusion data:
For example, in order to develope multi-model machine learning algorithms for self-driving cars, some manufacturers need to fuse two distinct data sets with different dimensions. This operation is essential, 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. It has become the focus of data service providers to create competitive advatange as well.
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
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- 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%.
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From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.
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.
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· Support 2D/3D sensor fusion, support multiple cameras
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· AI-powered sensor fusion tool: labeling at 2X-5X speed
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A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
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