Nerd For Tech
Published in

Nerd For Tech

China’s Commercial Pilot Project of Autonomous Driving Landed

https://www.hncheshi.cn/qyxw/202106/62955.html

The country’s first commercial pilot project of autonomous driving travel services was opened in Beijing on November 25, 2021. When the charging service was released to the public on the first day, a Beijing resident used Baidu’s Apollo autonomous driving travel service platform “Carrot Run” to complete the first payment. It is the first order since Beijing started its pilot project of autonomous driving.

More application scenarios will be unlocked soon

Baidu and Pony.AI became the first companies to carry out commercial pilot services. At this stage, they would be located in the Beijing Economic and Technological Development Zone (60 square kilometers), with no more than 100 vehicles.

At present, the loading and unloading service requires passengers to walk to a fixed station. The distance between stations is 300 to 500 meters. Passengers can reach by foot. The fixed station is used to avoid illegal parking lots, green belts, and high accident sites, which takes passengers’ safety into consideration.

It is understood that Baidu and Pony.AI became the first batch of enterprises to carry out commercialization pilot services. At present, Baidu’s charging standard is a “starting price of 18 RMB and 4 RMB per additional kilometer”. Pony AI would be 4.9 RMB for a uniform price from November 26.

The pricing method of the automated driving commercialization pilot service refers to that of private cars and online car-hailing services. It is well understood. Like the taxi-hailing software, this stage requires users to try and experience the overall service. Only by obtaining more information can the data collection of autonomous driving be further improved.

The intelligent car blew the second-half whistle

Technology, cost, policy, etc., are the main factors restricting the large-scale commercial deployment of autonomous driving. At present, autonomous driving companies have entered the second half of their journey, and the entire industry has moved from technology verification and product development to the stage of commercialization.

Beijing began to license the commercialization of autonomous driving services to verify the feasibility of the business model. Therefore, the pilot project of autonomous driving has opened up a complete closed-loop from R&D to commercialization and will encourage and guide the exploration and innovation of business models for autonomous driving travel services.

According to the mainstream view of the industry, it is believed that by 2025, the cost of the entire life cycle of autonomous driving taxis can be equal to that of traditional taxis. After 2025, the price of autonomous taxis will be lower than traditional taxis. Perhaps it will begin to replace traditional taxis on a large scale.

Each self-driving car in the commercial pilot is equipped with a safety officer. From the start of the pilot to the large-scale operation of autonomous driving in the future, the key is to overcome the core technologies related to autonomous driving safety, gradually withdraw safety drivers from the car, and reduce or eliminate the cost of safety officers, to form a closed loop of commercial profitability.

The demand for data labeling continues to increase

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.

In the field of autonomous driving, data annotation scenes usually include changing lanes and overtaking, passing intersections, unprotected left and right turn without traffic light control, and some complex long-tail scenes such as vehicles running red lights, pedestrians crossing the road, and roadsides as well as illegally parked vehicles, etc.

The current artificial intelligence is also called data intelligence. At this stage of development, the more layers of the neural network, the larger amount of labeled data is needed.

For deep learning, data is meaningful only if it is well labeled.

End

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?

Source: https://xw.qq.com/cmsid/20211126A01GQ800?f=newdc

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
ByteBridge

ByteBridge

160 Followers

Data labeling outsourced service: get your ML training datasets cheaper and faster!— https://bytebridge.io/#/