MIRA-PRM: Mission-Informed Reusable Roadmap Planning for Mobile Gas-Sensing Inspection
本文提出MIRA-PRM算法,通过结合几何、任务流和检测条件采样等方法解决移动气体检测中可靠到达预定采样位置的问题。
本文提出MIRA-PRM算法,通过结合几何、任务流和检测条件采样等方法解决移动气体检测中可靠到达预定采样位置的问题。
为解决极化图像融合中DoLP可靠性问题,提出LG-PF框架,通过置信度引导选择性残差传递,并在多尺度上进行融合,以稳定局部光度和结构转换。
本文针对Solana跨链交易追踪问题,提出了一种基于候选集选择性决策的方法SolTracer,有效提高了跨链交易关联的准确性。
论文提出了一种基于隐式Q学习引导的蚁群优化方法IQACO,用于解决敏捷卫星海上移动目标观测调度问题,通过自适应调整信息素因子等参数来优化任务选择与调度。
This study addresses the challenges of unreliable historical states and motion evolution in long-horizon planning for end-to-end autonomous driving by proposing StableDrive. The method leverages Mamba operators to construct a selective momentum memory that enhances the robustness of historical representations, while introducing a motion-stage training scaffold to guide the model in perceiving dynamic evolution, thereby enabling efficient single-model deployment without ensembling. Experiments demonstrate that StableDrive achieves state-of-the-art performance on benchmarks such as nuScenes, reducing collision rates by 23.3% and attaining the highest EPDMS score on NAVSIM v2. These results indicate significant improvements in both safety and temporal consistency for long-horizon planning, validating the effectiveness of integrating structured memory mechanisms with stage-aware training in complex driving scenarios.
本文提出MIRA-PRM算法,通过结合几何、任务流和检测条件采样等方法解决移动气体检测中可靠到达预定采样位置的问题。
为解决极化图像融合中DoLP可靠性问题,提出LG-PF框架,通过置信度引导选择性残差传递,并在多尺度上进行融合,以稳定局部光度和结构转换。
本文针对Solana跨链交易追踪问题,提出了一种基于候选集选择性决策的方法SolTracer,有效提高了跨链交易关联的准确性。
论文提出了一种基于隐式Q学习引导的蚁群优化方法IQACO,用于解决敏捷卫星海上移动目标观测调度问题,通过自适应调整信息素因子等参数来优化任务选择与调度。
This study addresses the challenges of unreliable historical states and motion evolution in long-horizon planning for end-to-end autonomous driving by proposing StableDrive. The method leverages Mamba operators to construct a selective momentum memory that enhances the robustness of historical representations, while introducing a motion-stage training scaffold to guide the model in perceiving dynamic evolution, thereby enabling efficient single-model deployment without ensembling. Experiments demonstrate that StableDrive achieves state-of-the-art performance on benchmarks such as nuScenes, reducing collision rates by 23.3% and attaining the highest EPDMS score on NAVSIM v2. These results indicate significant improvements in both safety and temporal consistency for long-horizon planning, validating the effectiveness of integrating structured memory mechanisms with stage-aware training in complex driving scenarios.