Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling

Understand

Safe and feasible trajectory planning is critical for real-world autonomous driving systems.

• However, existing learning-based planners rely heavily on expert demonstrations, which not only lack explicit safety awareness but also risk inheriting undesirable behaviors such as speeding from suboptimal human driving data.
• Inspired by the success of large language models, we propose Plan-R1, a two-stage trajectory planning framework that decouples principle alignment from behavior learning.
• In the first stage, a general trajectory predictor is pre-trained on expert data to capture diverse, human-like driving behaviors.