A Transformer Optimized Planner for Autonomous Vehicle On-Ramping Merging Task

Motion planning is the core component of autonomous driving technology. Traditional rule- and optimization-based methods face limitations when handling long-tail scenarios and complex traffic dynamics. While learning-based approaches offer potential solutions, they often compromise on trajectory safety and interpretability. In this context, this article introduces a transformer optimized planner (TOP), which combines generative artificial intelligence (AI) with optimization methods for autonomous driving on-ramping merging task. Specifically, TOP treats motion planning tasks as an offline reinforcement learning (RL) problem and employs the powerful sequence modeling capabilities of transformers, typically used in natural language processing (NLP), to derive an initial planning trajectory that maximizes utility from a fixed, previously collected dataset. Additionally, TOP facilitates real-time planning by utilizing efficient tokenization and temporal abstraction to compress input sequences into high-level latent actions. On this basis, optimization methods are employed to refine and safely adjust the transformer-generated trajectory, ensuring it adheres to safety requirements and vehicle kinematic constraints. To evaluate the effectiveness of the proposed algorithm, we performed simulations and conducted experiments using the Apollo industrial control unit, comparing the performance of TOP with the transformer- and optimization-based planner in an on-ramp merging scenario. It is found that TOP guarantees that the motion planning decisions are made within tight latency constraints (such as 100 ms target) even in complex scenarios, demonstrating 33 times shorter online execution time compared to the transformer-based planner and was nearly three times faster than the optimization-based planner while ensuring safety and performance.