We propose ComDrive: the first comfort-oriented end-to-end autonomous driving system to generate temporally consistent and comfortable trajectories. Recent studies have demonstrated that imitation learning-based planners and learning-based trajectory scorers can effectively generate and select safety trajectories that closely mimic expert demonstrations. However, such trajectory planners and scorers face the challenge of generating temporally inconsistent and uncomfortable trajectories. To address these issues, ComDrive first extracts 3D spatial representations through sparse perception, which then serves as conditional inputs. These inputs are used by a Conditional Denoising Diffusion Probabilistic Model (DDPM)-based motion planner to generate temporally consistent multi-modal trajectories. A dual-stream adaptive trajectory scorer subsequently selects the most comfortable trajectory from these candidates to control the vehicle. Experiments demonstrate that ComDrive achieves state-of-the-art performance in both comfort and safety, outperforming UniAD by 17% in driving comfort and reducing collision rates by 25% compared to SparseDrive. More results are available on our project page: https://jmwang0117.github.io/ComDrive/.

翻译：我们提出ComDrive：首个面向舒适性的端到端自动驾驶系统，能够生成时间一致且舒适的轨迹。近期研究表明，基于模仿学习的规划器和基于学习的轨迹评分器能够有效生成并选择紧密贴合专家示范的安全轨迹。然而，此类轨迹规划器和评分器面临生成时间不一致及不舒适轨迹的挑战。为解决这些问题，ComDrive首先通过稀疏感知提取三维空间表征，并将其作为条件输入。这些输入被基于条件去噪扩散概率模型（DDPM）的运动规划器用于生成时间一致的多模态轨迹。随后，双流自适应轨迹评分器从候选轨迹中选择最舒适的轨迹来控制车辆。实验表明，ComDrive在舒适性与安全性方面均达到最先进性能：其驾驶舒适性较UniAD提升17%，碰撞率较SparseDrive降低25%。更多结果详见项目页面：https://jmwang0117.github.io/ComDrive/。