L3 will be launched in the first half of next year at the latest: ideal end-to-end autonomous driving and greatly improved performance

Recently, with the rise of generative AI technology, many new car-making forces are exploring new methods of visual language models and world models. End-to-end intelligent driving new technologies seem to have become a common research direction. Last month, Li Auto released the third-generation autonomous driving technology architecture of end-to-end + VLM visual language model + world model. This architecture has been pushed to thousands of people for internal testing. It personifies intelligent driving behavior, improves the information processing efficiency of AI, and enhances the ability to understand and respond to complex road conditions. Li Xiang once said in a public sharing that in the face of rare driving environments that are difficult for most algorithms to identify and process, VLM (Visual Language Model) can systematically improve the capabilities of autonomous driving. This method can be achieved theoretically A breakthrough.

The new generation of autonomous driving systems has greatly increased the upper limit of capabilities - allowing AI to deal with many situations that were difficult to solve in the past, and also lowered the threshold - reducing the need for the size of technology R&D teams, and is expected to allow more people to drive in the near future Get a vastly improved experience in the future.
This set of autonomous driving technology architecture is inspired by the fast and slow system theory of Nobel Prize winner Daniel Kahneman. Simulating human thinking and decision-making processes in the field of autonomous driving also requires "fast systems" and "slow systems" Collaborate. Among them:
・ The fast system (System 1) is good at handling simple tasks and is human intuition formed based on experience and habits; in autonomous driving, it is composed of an end-to-end large model, including perception and planning, which is enough to handle 95% of the problems when driving a vehicle. Routine scenario.
・ The slow system (System 2) is the logical reasoning, complex analysis and computing capabilities formed by humans through deeper understanding and learning; in the autonomous driving system, it is mainly the VLM model, which is used to solve complex or even unknown problems when driving a vehicle Traffic scenes account for about 5% of daily driving scenes.
Last week, at an event held at Li Auto’s Beijing R&D headquarters, Li Auto’s Vice President of Intelligent Driving Lang Xianpeng emphasized that Li Auto’s intelligent driving has now fully integrated into the end-to-end + large model solution, which allows vehicles to understand complex road conditions and traffic rule.
"Both end-to-end and traditional perception decision-making models require a large amount of data for training. One potential problem is that the system will not work well if it encounters unseen scenes," Lang Xianpeng said. "We are exploring the ability of vehicles to think and make decisions like humans."

Li Auto Beijing Headquarters.

Since the second half of last year, Ideal began to adjust its strategy and change its trajectory. In February this year, in the DriveVLM paper submitted by Tsinghua University's Cross-Information Research Institute and Li Auto, researchers applied the visual language model (VLM) that has recently emerged in the field of generative AI and demonstrated extraordinary capabilities in visual understanding and reasoning.

In the industry, this is the first work to propose an autonomous driving speed system. Its method fully combines the mainstream autonomous driving pipeline and a large model pipeline with logical thinking, and is the first to complete the large model work of end test deployment ( Based on NVIDIA Orin platform).

DriveVLM system

DriveVLM consists of a Chain-of-Though (CoT) process with three key modules:

1. Scenario Description: Use language to describe the driving environment and identify key objects.
2. Scene Analysis: Dive into the characteristics of key objects and their impact on the ego vehicle.
3. Hierarchical planning: Step-by-step plan development from meta-action and decision descriptions to waypoints.

These modules correspond to the perception, prediction and planning components in the traditional autonomous driving system process. The difference lies in their ability to handle object perception, intention-level prediction and task-level planning, which have been extremely challenging in the past.

Technical verification

Ideal verification technology is effective in long-tail scenarios:

• Disassemble real environment data
• Use generative models to supplement new perspectives
• Customize changes to weather, time, traffic flow and other conditions

Practical application

Li Auto’s end-to-end model and VLM model run in real time:

• End-to-end model: higher frame rate
• VLM model: larger number of parameters, lower frame rate

In complex cities In the scenario, VLM plays a role in situations where decision-making is impossible and delivers decision results and trajectories to the end-to-end model.

End-to-end approach

The end-to-end approach has become a technological watershed, marking the beginning of the real use of AI.

The new generation AI model

The new generation AI model can serve as the question maker:

• Select the data of users who meet the standard of private car drivers as "real questions"
• Combined with the world model to generate "simulation questions"

Computing power challenge

車両側での VLM などのモデルの展開は、次のようなコンピューティング能力の課題に直面しています:

• パラメータの数を最適に保つ
• 意思決定の待ち時間を改善するためのエンジニアリングの最適化

競争の見通し

Tesla FSD は間もなく国内スマートドライビング分野への参入 新たな競争ステージへの参入:

• 理想のクルマの目標: エンドツーエンド+VLM自動運転の量産化

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