基于SC-IMM教师信号的端到端自动驾驶轨迹输出稳定化
Stabilizing Trajectory Outputs in End-to-End Autonomous Driving via SC-IMM Based Teacher Signals
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中文总结 AI 辅助
针对端到端自动驾驶轨迹输出不稳定问题，提出基于SC-IMM的离线教师信号生成方法，在CARLA Town12中驾驶评分提升28.0%，碰撞减少62.3%。
中文摘要 AI 辅助
端到端自动驾驶模型通常从传感器输入预测未来路径点，并通过下游控制器将其转换为车辆控制命令。然而，传统的基于路径点的模仿学习主要最小化坐标级误差，难以捕捉场景相关的路径-速度变化以及路径点输出的时间不稳定性。本文提出了一种基于场景条件交互多模型（SC-IMM）的离线教师信号生成与学习方法，用于轨迹输出稳定化以缓解该问题。所提方法将专家轨迹转换为路径-速度状态，并基于场景线索进行IMM更新，以生成路径-速度教师标签和模式后验概率。生成的信号在训练期间作为辅助监督添加到原始轨迹损失中，而推理结构和路径点控制器保持不变。在CARLA Town12中100条短路径的闭环评估中，与基线相比，所提方法将驾驶评分提高了28.0%，并将每公里碰撞次数减少了62.3%，同时改善了颠簸度和轨迹变化指标。这些结果表明，嵌入场景条件运动模型线索的离线教师信号可以引导轨迹输出驾驶模型实现更稳定的闭环行为。
英文摘要
End-to-End autonomous driving models commonly predict future waypoints from sensor inputs and convert them into vehicle control commands through a downstream controller. However, conventional waypoint-based imitation learning mainly minimizes coordinate-level errors, making it difficult to capture scene-dependent path-speed changes and temporal instability across waypoint outputs. In this paper, we propose an offline teacher-signal generation and learning method for trajectory-output stabilization based on a Scene-Conditioned Interacting Multiple Model (SC-IMM) to mitigate this issue. The proposed method converts expert trajectories into path-speed states and performs IMM updates conditioned on scene cues to generate path-speed teacher labels and mode posterior probabilities. The generated signals are added to the original trajectory loss as auxiliary supervision during training, while the inference structure and waypoint controller remain unchanged. In closed-loop evaluation on 100 short routes in CARLA Town12, the proposed method improved the driving score by 28.0% and reduced Collision/km by 62.3% compared with the baseline, while also improving jerk and trajectory-variation metrics. These results demonstrate that offline teacher signals embedding scene-conditioned motion-model cues can guide trajectory-output driving models toward more stable closed-loop behavior.
发表机构
- The CCS Graduate School of Mobility, Korea Advanced Institute of Science and Technology（韩国科学技术院CCS移动出行研究生院）
- Korea Advanced Institute of Science and Technology（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
