Large Language Models (LLMs) have enhanced autonomous driving, enabling natural language communication with navigation software and passengers. Current autonomous driving methods face limitations in understanding multi-modal data and interacting with the environment. Researchers have introduced LMDrive, a language-guided, end-to-end, closed-loop autonomous driving framework, along with a dataset and benchmark to improve autonomous systems’ efficiency and safety.
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Improving Autonomous Driving with LMDrive
Large Language Models (LLMs) have enhanced the field of autonomous driving by improving interpretability, reasoning capacity, and overall efficiency. Cognitive autonomous driving systems built on LLMs can communicate in natural language with navigation software or human passengers.
Challenges in Autonomous Driving
Despite advancements, autonomous driving systems still face challenges and may result in accidents in complex or unexpected situations. They often struggle to understand language information and engage with people due to reliance on limited-format inputs like sensor data and navigation waypoints.
Introducing LMDrive
To address these challenges, researchers have introduced LMDrive, a framework for language-guided, end-to-end, closed-loop autonomous driving. LMDrive analyzes and combines natural language commands with multi-modal sensor data, enabling smooth interaction between the autonomous car and navigation software.
Key Contributions of LMDrive
The team behind LMDrive has presented several valuable contributions, including the release of a dataset with over 64,000 instruction-following data clips and the introduction of the LangAuto Benchmark for assessing the system’s capacity in managing intricate commands and demanding driving situations.
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