C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving
Summary: arXiv:2603.29908v1 Announce Type: new
Introduction
In recent years, the advancement of autonomous driving technology has been significantly influenced by the integration of large language models (LLMs) for commonsense reasoning. However, despite their potential, LLM outputs have been found to be inherently unreliable, which poses considerable risks in safety-critical applications such as autonomous driving. To address these challenges, a new framework known as C-TRAIL has been proposed.
Overview of C-TRAIL
C-TRAIL, an acronym for Commonsense Trajectory planning, is designed to enhance trajectory planning in autonomous vehicles by coupling LLM-derived commonsense information with a robust trust mechanism. This innovative approach aims to ensure that the decision-making process in trajectory planning is not only informed by commonsense reasoning but also reliable and safe.
Operational Mechanism
The framework operates through a closed-loop cycle comprising three main modules: Recall, Plan, and Update.
- Recall Module: This module queries an LLM to obtain semantic relations relevant to the trajectory planning task. Furthermore, it quantifies the reliability of these relations using a dual-trust mechanism, which ensures that only the most trustworthy information is utilized in the planning process.
- Plan Module: In this phase, the trust-weighted commonsense information is incorporated into a Monte Carlo Tree Search (MCTS) algorithm through a Dirichlet trust policy. This allows C-TRAIL to make informed decisions while considering the reliability of the data it uses.
- Update Module: The final module adaptively refines trust scores and policy parameters based on environmental feedback. This continuous learning aspect ensures that the system improves over time and can adapt to new situations effectively.
Experimental Results
To validate the effectiveness of C-TRAIL, extensive experiments were conducted on four simulated scenarios within the Highway-env environment, as well as two real-world datasets: highD and rounD. The results from these experiments demonstrated significant improvements in trajectory planning performance.
- Average Decrease in Average Displacement Error (ADE): 40.2%
- Average Decrease in Final Displacement Error (FDE): 51.7%
- Improvement in Success Rate (SR): 16.9 percentage points
Conclusion
C-TRAIL represents a significant advancement in the field of trajectory planning for autonomous driving. By integrating commonsense reasoning with a trust mechanism, it addresses the reliability issues associated with LLM outputs, thus paving the way for safer autonomous driving solutions. The source code for C-TRAIL is available for public access at https://github.com/ZhihongCui/CTRAIL.