With the growing voice of “software-defined car”, from traditional car enterprises to new forces of car manufacturing, the competition with intelligent Internet + automatic driving as the core has been underway in an all-around way.
The core components of automobiles have gradually changed from engine, gearbox, and chassis to “automobile brain” composed of chips, software, and data. A new competition has been shown up. However, a series of new problems such as data security, laws, and regulations appear.
Tesla’s leading edge in the field of automatic driving mainly comes from its big data. The massive data and corner cases generated by millions of Tesla cars around the world every day are getting Tesla’s automatic driving function and performance continuously improved and enlarging the intelligent ability between Tesla and other car companies.
Indeed, every other day, Tesla’s intelligence ability will be stronger. When it is strong enough to a certain extent, there will be a qualitative difference.
The answer is Ecology.
Some people think that this year is the first year of high-level automatic driving as we can really see ensemble great progress in high-level automatic driving vehicles.
In China, before the Shanghai auto show,
Didi demonstrated the urban driverless function for 5 hours in a row;
Advanced automatic driving road test of Huawei and Jihu under complex working conditions;
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The first display of Weima’s L4;
Xpeng’s autopilot drives nearly 3000 kilometers from Guangzhou to Beijing, and the total number of take-over times is about 20, which means about 0.7–0.8 times per 100 kilometers.
All these reflect the overall progress of China’s intelligent vehicle automatic driving technology.
However, as for whether this year is the first year of high-level automatic driving, it is still hard to say.
The fact that driving 1000 km is OK doesn’t mean driving 1 million km is no problem, not to mention the whole life cycle. The difficulty exists in the safety and reliability of driverless function and it takes a long time to deal with corner cases. The real landing of high-level automatic driving still needs time and patience.
The first landing of unmanned driving will be in some special scenarios, such as unmanned trucks in ports and mines, cleaning and logistics vehicles in parks, and Robotaxi in specific areas. It will be a long time before the large-scale appearance of unmanned private cars.
It is also necessary to remind all auto companies to pay attention to the propaganda. At present, for instance, the Chinese government allows mass production and application of intelligent driving assistance technology limited to L3 level or below. Therefore, all auto companies should be cautious in propaganda and not over the line: don’t let users misunderstand that they are assured to give the right to the car.
Last year, at the CVPR 2019, Andrej Karpathy, the Senior Director of AI at Tesla responded to the question below:
how to estimate the volume of labeled data required to train and validate the self-driving cars for a particular scenario?”
378 hours of data.
The more accurate annotation is, the better algorithm performance will be.
ByteBridge is a human-powered and ML-powered data labeling tooling platform. We provide scalable, high-quality training data for ML/AI industry with flexible workflow.
Quality and Accuracy
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In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%.
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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.
More info: ByteBridge Launches World’s First Mobile 3D Point Cloud Data Labeling Service
3D Point Cloud Annotation Types:
- Sensor Fusion Cuboids: 12 categories include car, truck, heavy vehicle, two-wheeled vehicle, pedestrian, etc.
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- Sensor Fusion Cuboids Tracking
① Tracking the same object with the same ID, labeling the leaving state;
② Point clouds or time-aligned images could be provided, point clouds outputs only.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
As the quality of the labeled dataset determines the success of the self-driving industry, cooperation with a reliable partner can help developers to overcome the data labeling challenges.
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