Shenzhen Releases the First Commercial Application Standard for Self-driving Low-speed Driverless Vehicles
Meituan and Other Companies Participate in Standard-setting
On October 29, at the 2021 automatic driving low-speed uncrewed vehicle commercial application standard conference held in Shenzhen, the Shenzhen Intelligent Transportation Industry Association led the joint compilation of more than 57 units and 112 experts in the low-speed unmanned vehicle field. The group standard of “Safety Management Regulations for Commercial Operation of Low-speed Unmanned Vehicles in Cities” was officially released.
It is a first-of-its-kind standard prepared for the application of autonomous driving low-speed unmanned vehicle service scenarios. It is advanced and innovative. It will put the autonomous driving low-speed unmanned vehicle into the intelligent city system and play an important guiding role. It will also provide a practical reference for government functional management departments and users to introduce low-speed unmanned vehicles. At the same time, it will also regulate the market and serve the existing low-speed unmanned vehicles. Operation demonstration and high-quality new smart city construction will be escorted.
Shenzhen’s technological innovation and “dual-zone” advantages are very suitable for constructing autonomous driving demonstration cities and accelerating the deployment of vehicle-road coordination facilities. It focuses on introducing low-speed unmanned vehicles to enrich the urban service scenarios and thus realize fully automatic logistics, distribution, and environmental sanitation.
The Business Model will Determine the Direction of Driverless Cars
The more we pay attention to this field, the more diversified the future operating models of automobile companies will be. Nowadays, automobiles are the largest customers in the entire advertising industry. Suppose consumers no longer buy cars but choose Uber or Zipcar rental services, in this case, it will also impact the multi-billion-dollar advertising market size of the entire automotive industry and change the whole profit model of the automotive industry.
If the car itself is no longer “commoditized,” consumers are not concerned about what kind of car they buy but how to get to the destination. The technology platform and market of the transportation industry will become the most significantly profitable areas.
We believe that many problems with autonomous vehicles will be solved one by one with the continuous innovation of technology. After the emergence of autonomous driving technology, the business model will also become the focus of attention from the outside world.
For example, suppose the regulatory agency levies taxes on cars in a particular city based on mileage, there will be different incentives to increase the utilization rate of vehicles as much as possible and minimize travel costs. Suppose a car company decides to sell driverless cars only to commercial organizations and not to ordinary users, it will also cause manufacturers to allocate marketing and research, and development funds in different ways.
Data Annotation Service Behind Self-driving Industry
The more accurate annotation is, the better algorithm performance will be.
What we need to be clear is for AI companies and the entire industry, data annotation is an important part of the realization of artificial intelligence. The accuracy and efficiency of the labeled data affect the final result of the artificial intelligence algorithm model.
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.
If the general datasets used by the previous algorithm model were coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create their own unique one. 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:
- 2D Bounding Boxes
- Lane Marking
- Video tracking annotation
- Point Annotation
- 3D Object Recognition
- 3D Segmentation
- Sensor Fusion: Sensor Fusion Cuboids/Sensor Fusion Segmentation/Sensor Fusion Cuboids Tracking
- Semantic Segmentation
Outsource your data labeling tasks to ByteBridge, you can get the high-quality ML training datasets cheaper and faster!
- Free Trial Without Credit Card: you can get your sample result in a fast turnaround, check the output, and give feedback directly to our project manager.
- 100% Human Validated
- Transparent & Standard Pricing: clear pricing is available(labor cost included)
Why not have a try?