LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models

Core Concepts

Utilizing Large Language Models for lane change prediction enhances accuracy and interpretability.

Abstract

The paper introduces LC-LLM, a model that leverages Large Language Models for lane change prediction. It reformulates the prediction task as a language modeling problem, improving accuracy and interpretability. Extensive experiments on the highD dataset show superior performance compared to baseline models. Lane change prediction is crucial for autonomous driving safety. LC-LLM leverages Large Language Models for accurate predictions. Experiments demonstrate improved accuracy and interpretability.

Stats

Compared with baseline models, the performance of intention prediction can be improved by 3.1%. The root-mean-squared error (RMSE) of lateral trajectory prediction can be reduced by 19.4%. The RMSE of longitudinal trajectory prediction can be reduced by 38.1%.

Quotes

"Our LC-LLM model not only can predict lane change intentions and trajectories but also provides explanations for its predictions, enhancing the interpretability." "Our study shows that LLMs can encode comprehensive interaction information for driving behavior understanding."

Key Insights Distilled From

LC-LLM

by Mingxing Pen... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18344.pdf

Deeper Inquiries

How can the interpretability of autonomous driving prediction models be further improved?

Interpretability in autonomous driving prediction models can be enhanced by incorporating more transparent and explainable AI techniques. One approach is to develop models that provide not only predictions but also explanations for those predictions, similar to the LC-LLM model discussed in the context. By integrating explanatory requirements into the model's outputs, users can better understand the reasoning behind the predictions. Additionally, visualizations and interactive tools can be utilized to present the model's decision-making process in a more intuitive manner. This can help users, such as vehicle operators or system developers, to trust the model's predictions and make informed decisions based on the model's outputs.

What are the potential limitations of using Large Language Models for lane change prediction?

While Large Language Models (LLMs) offer significant advantages in understanding complex driving scenarios and reasoning about interactions between vehicles, there are potential limitations to consider when using them for lane change prediction. One limitation is the computational complexity and resource-intensive nature of LLMs, which can make them challenging to deploy in real-time applications or resource-constrained environments. Additionally, LLMs may require large amounts of training data to fine-tune them effectively for specific tasks like lane change prediction, which can be a limitation in scenarios where labeled data is limited or costly to obtain. Furthermore, the black-box nature of LLMs can make it difficult to interpret how they arrive at their predictions, which may raise concerns about trust and transparency in critical applications like autonomous driving.

How can the findings of this study be applied to real-world autonomous driving systems for enhanced safety and efficiency?

The findings of this study can be applied to real-world autonomous driving systems to improve safety and efficiency in several ways. Firstly, the development of explainable lane change prediction models, like the LC-LLM proposed in the study, can enhance the interpretability of autonomous driving systems. By providing explanations for lane change predictions, operators and developers can better understand the model's decisions and take appropriate actions. Secondly, the integration of advanced language models and fine-tuning techniques can improve the accuracy of long-term predictions, enabling autonomous vehicles to anticipate lane change intentions and trajectories more effectively. This can lead to smoother traffic flow, reduced collision risks, and overall safer driving experiences. Lastly, the insights gained from this study can inform the design of future autonomous driving systems, guiding the development of more intelligent and reliable prediction models for enhanced safety and efficiency on the roads.