DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning

GuideFlow has been introduced as a novel planning framework for end-to-end autonomous driving, addressing the limitations of existing Imitative and Generative E2E Planners. By employing Constrained Flow Matching, GuideFlow effectively mitigates mode collapse and incorporates explicit safety and physical constraints into the trajectory generation process.

The introduction of Percept-WAM marks a significant advancement in autonomous driving technology, focusing on enhancing spatial perception through a unified vision-language model that integrates 2D and 3D scene understanding. This model addresses the limitations of existing systems, which often struggle with accuracy and stability in complex driving scenarios.

A novel framework named ResAD has been introduced to enhance end-to-end autonomous driving systems by addressing the challenges posed by spatio-temporal imbalances in trajectory data. This approach focuses on predicting the residual deviation from a deterministic inertial reference rather than directly forecasting future trajectories, aiming to improve model robustness and immediate safety.

The paper introduces DiffRefiner, a two-stage trajectory prediction framework designed for end-to-end autonomous driving. The first stage generates coarse trajectory predictions using a transformer-based Proposal Decoder, while the second stage refines these predictions through a Diffusion Refiner, enhancing the flexibility and accuracy of trajectory planning. This approach addresses limitations in current generative methods that rely on predefined anchors or random noise.