Summary Drive Like a Human Rethinking Autonomous Driving with Large Language Models arxiv.org
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One Line
The use of a large language model can improve autonomous driving by imitating human driving patterns and adapting through continuous learning.
Slides
Slide Presentation (12 slides)
Key Points
- Large language models (LLMs) have the potential to understand the driving environment in a human-like manner and solve complex scenarios.
- Traditional optimization-based and modular autonomous driving systems have limitations in handling long-tail corner cases.
- An ideal autonomous driving system should drive like a human, accumulating experience and using common sense to solve problems.
- LLMs demonstrate impressive reasoning, interpretation, and problem-solving abilities in driving scenarios.
- The proposed memory module in LLMs allows for continuous learning and retrieval of past decision scenarios.
- LLMs have the potential to contribute to the development of human-like autonomous driving and artificial general intelligence.
Summaries
19 word summary
Using a large language model (LLM) can enhance autonomous driving by mimicking human driving behaviors and incorporating continuous learning.
69 word summary
This paper explores using a large language model (LLM) to understand the driving environment, arguing that traditional autonomous driving systems have limitations. The authors propose that an ideal system should drive like a human, using common sense and accumulating experience. The LLM demonstrates comprehension and problem-solving abilities, providing insights for human-like autonomous driving. The paper emphasizes the importance of memorization and proposes a separate memory module for continuous learning.
154 word summary
This paper explores the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize in complex scenarios. The authors argue that traditional optimization-based and modular autonomous driving (AD) systems face limitations when dealing with long-tail corner cases. They propose that an ideal AD system should drive like a human, accumulating experience and using common sense to solve problems. The authors demonstrate the feasibility of employing an LLM in driving scenarios by showcasing its comprehension and environment-interaction abilities. The LLM exhibits impressive reasoning and problem-solving abilities, providing valuable insights for the development of human-like autonomous driving. The paper also highlights the importance of the memorization ability in autonomous driving systems and proposes a separate memory module for continuous learning. Overall, the paper presents a compelling argument for using LLMs in autonomous driving systems, emphasizing reasoning, interpretation, and memorization abilities.
398 word summary
This paper explores the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize in complex scenarios. The authors argue that traditional optimization-based and modular autonomous driving (AD) systems face limitations when dealing with long-tail corner cases. To address this problem, they propose that an ideal AD system should drive like a human, accumulating experience through continuous driving and using common sense to solve problems.
The authors identify three key abilities necessary for an AD system: reasoning, interpretation, and memorization. They demonstrate the feasibility of employing an LLM in driving scenarios by building a closed-loop system to showcase its comprehension and environment-interaction abilities. The LLM exhibits impressive reasoning and problem-solving abilities, providing valuable insights for the development of human-like autonomous driving.
The paper presents two examples that highlight the interpretation and reasoning abilities of the LLM in driving scenarios. In one example, the LLM correctly identifies that a pickup truck carrying traffic cones is not a hazard because it is a common occurrence on the road. In another example, the LLM recognizes that scattered traffic cones around a pickup truck can be potentially dangerous and advises the driver to decelerate and maintain a safe distance.
The authors also discuss the importance of the memorization ability in autonomous driving systems. They propose a separate memory module that records decision scenarios that deviate from expert feedback. The LLM undergoes a self-reflection process to determine why its decision deviates from the expert's and adds the scenario to its memory. When encountering a similar case in the future, the LLM can retrieve the memory entry for reference and make an informed decision.
The paper concludes by highlighting the potential of LLMs in autonomous driving systems. The authors hope that their research will inspire further development of human-like autonomous driving and contribute to the advancement of artificial general intelligence.
Overall, this paper presents a compelling argument for using LLMs in autonomous driving systems. It emphasizes the importance of reasoning, interpretation, and memorization abilities in achieving human-like driving capabilities. The examples provided demonstrate the LLM's impressive comprehension and decision-making abilities in driving scenarios. The proposed memory module offers a practical solution for continuous learning in autonomous driving systems. The paper contributes valuable insights to the field of autonomous driving and inspires further research and development in this area.