New breakthrough in 'human-vehicle interaction'! Purdue University releases Talk2Drive framework: a learnable/customizable 'command recognition' system

In the latest research from Purdue University’s Digital Twin Laboratory, scientists have adopted a revolutionary technology - using large language models (LLM) to enhance the intelligent command parsing capabilities of autonomous vehicles. This innovation brings new possibilities to the development of autonomous driving technology and is expected to improve vehicles' understanding and response speed to driving instructions.

The key to this technology is the Talk2Drive framework, which aims to use human natural language to control self-driving cars, creating a unique way of human-vehicle interaction.

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Paper link: https://www.php.cn/link/5f221386d076f4e7f6a97bb3b406c7b8

Project website: purduedigitaltwin.github .io/llm4ad

Through its innovative design, the Talk2Drive framework enables efficient and intuitive interaction between autonomous vehicles and human drivers. The running process of the framework includes key steps such as receiving commands, processing and reasoning, generating executable code, and executing the code and collecting feedback. Through these steps, the framework ensures that autonomous vehicles interact with drivers in a safe and reliable manner and take necessary decisions and actions when needed. The Talk2Drive framework is designed to improve the driving experience and provides important support for the development of autonomous driving technology.

First of all, through advanced speech recognition technology, the framework can accurately receive and convert human verbal commands into text instructions, thereby ensuring accurate understanding of human intentions.

Combined with real-time environmental data from the cloud, such as weather and traffic conditions, LLM will integrate this key contextual information when processing instructions to ensure that the developed driving strategy is both safe and adaptable to current environmental conditions.

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LLM uses contextual learning and thought chain prompts to reason about instructions. The generated code not only contains basic driving instructions, but also involves complex driving. behavior and parameters that need to be adjusted in the vehicle's low-level controller. The adjustment of these parameters, such as look-ahead distance and speed, is based on an in-depth understanding of current road conditions and driver needs.

In terms of security, the Talk2Drive framework ensures the safety of autonomous driving behavior by strictly checking the format and parameters of the generated code.

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In addition, the introduction of the memory module allows the system to record and learn the driver's preferences and feedback, providing the driver with a more personalized driving experience.

The uniqueness of the Talk2Drive framework is its highly personalized service.

By analyzing passengers' verbal instructions, such as "Please take me to my destination as soon as possible, I don't want to make my friends wait too long" or "I feel a little motion sick, please slow down", the framework can accurately understand and Meet the needs of passengers. Every human-vehicle interaction is recorded and used to optimize the system, allowing it to learn passenger preferences and provide a more customized driving experience in the future.

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In addition, the experimental results show that for drivers with different driving styles, using different LLM, the self-driving car using the Talk2Drive framework has better performance in actual practice. It performed well in road tests, being able to understand the driver's instructions of varying degrees of obscurity, effectively reducing the need for manual takeover, and being able to adapt to different driving styles and scenarios.

This achievement not only demonstrates the huge potential of large language models in the field of autonomous driving, but also opens up a new path for the future development of autonomous driving technology.

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For different types of drivers, using the Talk2Drive framework can significantly reduce the take-over rate during driving.

With the continuous advancement and optimization of technology, the Talk2Drive framework will be able to provide a safer, more comfortable and personalized driving experience for autonomous vehicles. This breakthrough research not only marks the integration and progress of autonomous driving and human-computer interaction, but also heralds the arrival of a people-oriented and more intelligent future transportation era.

Research Team

Purdue University Digital Twin Laboratory is committed to innovation and exploration at the intersection of large language models and autonomous driving.

We welcome researchers, engineers and industry colleagues from all over the world who are interested in this field to visit our project website to jointly promote the development of autonomous driving technology and explore the possibilities of future transportation.

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Reference:

https://www.php.cn/link/5f221386d076f4e7f6a97bb3b406c7b8

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