首个关注驾驶舒适性的端到端自动驾驶系统，生成平滑连贯的行驶轨迹。
ComDrive: Comfort-Oriented End-to-End Autonomous Driving
- 基于条件去噪扩散模型生成多模态、时间一致的轨迹
- 相比UniAD提升17%舒适度，碰撞率降低25%
- 适合追求平稳驾驶体验的自动驾驶研究与应用
我们提出ComDrive：首个面向舒适性的端到端自动驾驶系统，可生成时间上一致且舒适的行驶轨迹。近期研究表明，基于模仿学习的规划器和学习型轨迹评分器能有效生成并筛选安全、贴近专家示范的轨迹。然而，这类方法常产生时间不一致且不舒适的轨迹。为解决该问题，ComDrive首先通过稀疏感知提取3D空间表征，作为条件输入；再利用基于条件去噪扩散概率模型（DDPM）的运动规划器生成时间一致的多模态候选轨迹；随后，双流自适应轨迹评分器从候选中选出最舒适的轨迹进行车辆控制。实验表明，ComDrive在舒适性与安全性上均达当前最优，相比UniAD提升17%驾驶舒适度，碰撞率较SparseDrive降低25%。更多结果见项目页面：https://jmwang0117.github.io/ComDrive/
原文摘要 · Abstract (English)
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/.
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