Cheng-Lin Chen

Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving

This work designs a hybrid architecture that combines sparse anchors with an offset refinement module for efficient multi-modal trajectory prediction and adopts a two-stage training paradigm consisting of pretraining and reward-guided fine-tuning, demonstrating superior performance under real-time constraints.