Recently, the hottest topic in the science and technology circle is "ChatGPT". However, ChatGPT is only an external manifestation, and what deserves more attention is the development of AI technology behind it and its future application.
Some people even describe the changes brought by ChatGPT optimistically: Before ChatGPT, AI was only a module of existing scene products at most. Then, after ChatGPT, AI will redefine the product framework of existing scenarios.
Whether it is as optimists say remains to be seen, but whether autonomous driving, as one of the important scenes of AI landing, will have further development in this wave has still aroused many people’s discussion.
Some people think that autonomous driving needs more graphics, images and data processing ability, requires higher image algorithm, and has little correlation with natural language processing ability. It is not possible to realize autonomous driving with ChatGPT’s ability at present.
Of course, some people think that the appearance of ChatGPT shows us a possibility, that is, trained AI will make high-level autonomous driving expected to appear in a few years.
Why does the progress of AI technology make people pay attention to whether autonomous driving is affected?
Observing the development history of autonomous driving, it is not difficult to find that every major breakthrough of autonomous driving is synchronized with the development of AI technology.
We know that,AI is actually imitating the brain neural network and learning some very humanized skills by analyzing a large amount of data.In 1980s, the first practical application of neural network happened in the field of automatic driving.
In 1987, researchers at Carnegie Mellon Artificial Intelligence Laboratory tried to make a truck that could drive automatically. They manually write codes for all driving behaviors and write as detailed instructions as possible for various situations encountered by trucks on the road, so as to make the vehicles run automatically. But unfortunately, this way can only make the car achieve a speed of several inches per second.
Manual code writing failed, and another doctoral student named Dean pomerleau chose another way: neural network.
He named his system ALVINN. After adopting this system, trucks use the images taken by the roof camera to track what drivers are doing, so as to observe how to learn to drive on the road. In 1991, ALVINN drove from Pittsburgh to Erie, Pennsylvania at a speed of nearly 60 miles per hour.
However, a more direct and broader impact occurred in 2012.
Jeff Hinton, a professor at the University of Toronto, and two of his students, Alex Krzyzewski and Ilya Satsky, won the first prize in the ImageNet image recognition competition, and published a paper introducing the algorithm AlexNet. This paper is not only the turning point of artificial intelligence, but also the turning point of global technology industry.
As the key technology of autonomous driving, target detection and image recognition are highly benefited from the breakthrough of computer vision algorithm. Therefore, with the recognition accuracy of Li Feifei team, director of Stanford Artificial Intelligence Laboratory, surpassing humans for the first time on ImageNet open data set in 2015, autonomous driving, as one of the most important landing scenes of AI, has also entered the fast lane of development.
So, will the appearance of ChatGPT become the Milestone of autonomous driving again?
Generally speaking, AI can be divided into three parts: voice, vision and natural language understanding.The last wave of AI was mainly based on the breakthrough of visual image recognition technology, and this time ChatGPT is a natural language processing technology based on GPT-3 model, which can effectively simulate human language understanding ability, thus helping people better understand and analyze natural language text data.
When we want to discuss what impact ChatGPT will have on autopilot, we think that we should first find out whether autopilot here refers to mass-produced low-level autopilot (assisted driving) or high-level L4 autopilot. Secondly, does ChatGPT refer to a language model or a more generalized generation model?
From the perspective of natural language understanding, ChatGPT has a more direct impact on human-computer interaction in the assisted driving part, but it may not have a great impact on L4-level automatic driving.
Cui Dongshu, secretary-general of the Federation, also wrote in his WeChat WeChat official account that the innovation of human-computer interaction and intelligent cockpit system is very strong, especially the human-computer interaction ability of domestic car companies is very strong. Only China enterprises can understand Chinese more deeply. With the empowerment of the bottom layer in the future, the domestic automobile industry will have more good human-computer interaction effects at the application level.
For example, by using ChatGPT, the vehicle can interact with the driver by voice or text, and provide the driver with real-time feedback on vehicle status and driving information.
Before this, although a large number of in-vehicle interactive systems have appeared, the pain point of the industry mainly focuses on the "understanding" part, and most of the in-vehicle voice interactive systems are not intelligent in "understanding", resulting in a single function and command word of the whole system. ChatGPT’s explosion made the market see the hope of solution.
However, Cui Dongshu, secretary-general of the Federation, also said that,Electrification is the core of new energy vehicles, and intelligence is just icing on the cake. In the future, the core competitiveness of car companies will still be to build electric vehicles, and at the same time make full use of intelligence such as ChatGPT to empower the development of the automobile industry.
Of course, whether it is the core or not, it is not enough to have a technological breakthrough if you want to get on ChatGPT. An AI industry person told Titanium Media that "there are still cost issues, including the use cost, cloud service cost and targeted training cost."
However, from a broader generative model, the generative model with big data and large parameters will help to achieve a higher level of autonomous driving.
He Xiang, a data intelligence scientist at Mimo Zhixing, said in an interview with Titanium Media App that the vehicle-side capabilities mainly include two categories: perception and cognition. The perception ability really relies mainly on image technology, while the cognitive ability relies more on similar generation technology of ChatGPT.
That is to say,ChatGPT’s revolutionary significance lies in: letting AI model enter the era of knowledge and reasoning. At present, the biggest shortcoming of autonomous driving lies in the lack of sufficient intelligence in decision-making planning.
ChatGPT uses a training method called "Human Feedback Reinforcement Learning (RLHF)", and He Xiang, a data intelligence scientist at Mimo Zhixing, explained to Titanium Media APP.GPT is a large-scale universal pre-training language model. GPT1, GPT 2 and GPT 3 mainly improve the parameter scale, while ChatGPT mainly introduces human feedback data for reinforcement learning.
The introduction of this method can ensure the minimum output of useless, distorted or biased information according to human feedback in training.
It happens that there is also a kind of automatic driving decision algorithm called imitation learning, which is to let the machine learn how human drivers do it in different scenarios.
Generally speaking, every takeover by a human driver is an artificial feedback to the autonomous driving strategy; This takeover data can be simply used as a negative sample, which is a record that the autopilot decision is corrected. At the same time, it can also be used as a positive sample to improve cognitive decision-making
"The big model of big data and big parameters can learn more potential knowledge, including different environments and different scenarios, which is equivalent to learning a lot of common sense of autonomous driving. This common sense is very important for autonomous driving decisions." He Xiang, a data intelligence scientist at the end of the year, told Titanium Media App.
That is to say,In the process of autonomous driving research and development, the idea of human feedback reinforcement learning can be used to train a model to verify and evaluate the output of the machine model, so that it can make continuous progress and finally reach the driving level of human beings.
Therefore, it can be said that the improvement of basic ability has brought about the expansion of imagination and applicable scenarios. However, at this stage, we still can’t accurately judge how much change the big model represented by ChatGPT will bring to autonomous driving. An industry person told Titanium Media App that the excellent generalization ability trained by the big model may make there no corner case in the world.
Corner case refers to a small probability event that may occur during driving, but the frequency is extremely low. Although it is rarely encountered at ordinary times, it is likely to lead to a fatal traffic accident when encountering a corner case that cannot make a decision for an autonomous driving system.
The emergence of ChatGPT has made the industry realize that it is possible to gain a higher level of autonomous driving technology by constantly accumulating kilometers and running like this.
In fact, before this, both foreign Tesla and domestic Tucki, Baidu and Mimo Zhixing were already exploring the route of "big model".
In 2020, Tesla announced that it would introduce a large model based on deep neural network into its autonomous driving, and now it has realized a large-scale public beta of pure visual FSD Beta; Tucki expressed the viewpoint of using large models to get through the whole scene of XNGP on the 1024 Science and Technology Day in 2022. Baidu Apollo believes that the Wenxin model will be the core driving force of the elevator’s automatic driving ability.
As early as 2021, Mimo Zhixing announced that it would improve its data processing ability with the help of a large model. On February 17th this year, Mimo Zhixing officially upgraded the large model of human driving self-monitoring cognition to "DriveGPT", which will continue to introduce large-scale real takeover data, and continuously improve the evaluation effect through intensive learning of human driving data feedback. At the same time, it also uses DriveGPT as a cloud evaluation model to evaluate the driving effect of small models at the vehicle end.
However,The development of high-level self-driving cars is a complex multidisciplinary field, involving a wide range of technical and regulatory challenges. The progress of artificial intelligence technology can bring some impetus, but this is not a short-term problem.
It is reported that GPT3.0 involves 170 billion parameters, with more than 300 GB of memory, and the training process costs more than 12 million US dollars. The above-mentioned industry insiders said that the autopilot algorithm is to run in the car. Can such a large model be deployed to the car? How much computing power does it need to support? In addition, autonomous driving can not be completed by repetitive and simple road data stacking, so how to ensure a large amount of data is also a key issue.
The article is reproduced from the titanium media. The opinions in the article are only for sharing and communication, and do not represent the position of WeChat official account. If copyright issues are involved, please let us know and we will deal with them in time.
About the laundry list technology company
Laundry Technology Co., Ltd. is the world’s leading spatio-temporal intelligent big data service company and the leading location data and industry application solution provider in China. Relying on self-developed patented technology, based on high-precision maps and multi-source spatio-temporal intelligent big data, we will establish city-level and industry-level holographic spatio-temporal digital twin systems, and actively serve smart transportation (autonomous driving, smart highway, vehicle-road coordination), natural resource asset management (carbon neutral and environmental protection remote sensing data service), LBS smart industry applications (mobile Internet local business location service, smart travel, smart logistics, new infrastructure, smart city, emergency rescue) and other fields. For more information, please search for "laundry list technology" and visit the company website.