SpeedFormer: Learning Speed Profiles with Upper and Lower Boundary Constraints Based on Transformer

This paper presents a new method for generating speed profiles for autonomous vehicles using a Transformer-based network that predicts the coefficients of quintic polynomials. To train and validate the network, we curate a dataset of 500K simulated urban driving scenarios, where the ground truths are obtained by running offline model predictive control (MPC) optimization. We also present tailored loss functions to emulate MPC behavior and constrain upper-and-lower boundary conditions to provide feasible speed profiles. Extensive experimental results demonstrate the efficacy of the proposed method in providing high-quality speed profiles for a large number of path candidates and long planning horizons. The proposed method is capable of generating efficient speed profiles for 1024 path candidates within 30ms.