Haomo DriveGPT is the "king of volumes" of large models! Focusing on "cost reduction, efficiency increase, veteran driver"

The 2023 China International Fair for Trade in Services, with the theme of "Openness Leads Development, Cooperation Wins a Future", concluded successfully on September 6. At this Service Trade Fair, a group of leading companies that have been engaged in artificial intelligence, autonomous driving, satellite remote sensing and other fields for many years demonstrated their latest scientific and technological achievements and demonstrated their steps towards the future

domestic He Xiang, a data intelligence scientist at the self-driving unicorn company Hao Mo Zhixing, gave a keynote speech on "Bei Mo DriveGPT Xuehu·Hairuo, accelerating the advent of the self-driving 3.0 era" and accepted interviews with the media after the meeting. The research and application exploration of autonomous driving technology in the model era has brought us a comprehensive interpretation

Image description: He Xiang (right), a data intelligence scientist at HaoMo Zhixing, is being interviewed by the media

The following is the record of the interview:

Host: Mr. He, can you introduce to us what kind of results and displays Haomo Zhixing will bring to us in this year’s trade in services?

He Xiang said that one of our most important achievements this year is the industry's first self-driving generative model DriveGPT released by Hao Mo Zhixing in April

Moderator: DriveGPT? Sounds like it has something to do with driving?

He Xiang: Yes, this is a large AI model used to solve problems related to the field of autonomous driving. We call it the autonomous driving generative pre-training large model DriveGPT

Moderator: Generative pre-training? How do we understand pre-training?

He Xiang said: The technical details of the large model are that it must first use massive driver driving behavior data to conduct pre-training in the cloud. Pre-training is to train the model first. After training, a prototype of the model is obtained, and then the driver's takeover data is introduced. The so-called takeover data means that every time when autonomous driving is turned on, if the autonomous driving decision is not good enough, the driver will take over, such as stepping on the brakes or holding the steering wheel. This takeover data amounts to corrections to our autonomous driving decisions. After obtaining this data, the model can be continuously corrected to make the model's driving effect better and better. This is a process of constant error correction and constant iteration to achieve better autonomous driving effects

Host: It can be said to be an upgrade to our traditional autonomous driving. The rewritten content is as follows: Host: It can be said that this is an upgrade to our traditional autonomous driving. He Xiang: Yes, it can be said to be a technological change. We can make a simple comparison. The development model of traditional autonomous driving technology is that when autonomous driving finds a problem, it will usually find data related to this problem from massive data. The cost is very high. of. Because it is not that easy to find the data you want in the massive data. After finding this data, the next thing to do is to give this pile of data to the annotation company, and manually annotate the problems in it. After the annotation is completed, use this data to train a small model. This model After training, put it in the car. At this point, this car has the ability to solve this problem. We call this model small data and small model, and it is "problem-driven".

Under the large model model of DriveGPT, the entire development model is different. With the support of DriveGPT, the current development model is to first use massive data, veteran driver data, and driving behavior to conduct pre-training to obtain a preliminary model, which has the ability to drive. When we discover a problem during autonomous driving, the driver will take over. This takeover is equivalent to correcting the driving decision. Based on this corrected data, the data is then sent back to correct the original pre-trained large model. After such a data closed loop is established, the effect of this model will continue to evolve and improve every day. We call this development model big data and big model, and it is "data-driven". This is a transformational improvement.

Moderator: We can observe that the current level of autonomous driving technology is approximately L2 level, and now most vehicles have reached L2.5 level

He Xiang: L2, we call it high Level assisted driving.

Moderator: With the support of the large model DriveGPT, what level can we achieve?

He Xiang: It should still be in the high-level assisted driving stage. Our large model mainly generates two business values.

The first business value is in the entire cloud. The traditional autonomous driving development model needs to be migrated to the cloud, which will bring very high costs and require a lot of data screening, especially manual participation and a lot of manual annotation. However, with large models, the entire data screening, annotation and data generation can be fully automated, which is very effective in reducing costs

For example, in the field of annotation, autonomous driving companies must have spent hundreds of millions of yuan on annotation every year. With DriveGPT, images or videos can be automatically annotated. If you do video annotation or 4D Clips Labeling can probably reduce costs by 98%. Even if only a single image is annotated, the cost can be reduced by 90%. The cost of cloud can be greatly reduced.

The second business value is on the car side, and the effect can be greatly improved. The model is trained based on massive data. Massive data means that our model has seen a lot of data. It has seen all kinds of scenarios. The more it is informed, the stronger its ability will be. This ability is called the generalization ability of the model or AI. With generalization capabilities, the effect of autonomous driving will be better.

In addition, the entire model is trained based on the driving behavior data of "old drivers". It is very high-quality data. Its overall driving effect or driving experience will be closer to that of "old drivers". Users will feel that the driving experience will be better during use.

Third point, our large model has a special ability to output the reasons for driving decisions. For example, when a driving decision is made such as "press the brakes" or "turn the steering wheel," our model can explain why that action was taken. If such an explanation can be provided, a good trust relationship can be established between intelligent driving vehicles and users, and users will be more assured when using autonomous driving products

Through continuous iteration based on large-scale models and data closed loops , the current advanced assisted driving still requires the driver to take over at any time. In the future, we hope to gradually realize true driverless driving through continuous iterative upgrades

Host: From this perspective, it not only reduces costs but also improves efficiency

He Xiang said: " There is no need for drivers to try and make mistakes again and again. Big data can help solve this problem. It can collect the takeover behaviors of all drivers and solve all problems at once. In this way, the driving effect will be improved very quickly."

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