发表机构
UIUC（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出两步策略修正基于学习的感知不确定性，利用感知契约原像和风险启发式选择状态，在ACC场景中73%保持安全且仅增加2.8%完成时间。
AI 中文摘要
基于学习的感知在许多自主系统中至关重要。与传统传感器不同，机器学习感知在何种边界内有效或无效的特征化尚不充分。不正确的感知可能导致不安全或过度保守的下游控制动作。在本文中，我们提出了一种两步策略来修正基于机器学习的状态估计。首先，使用离线计算来表征由机器学习模块的状态估计所产生的不确定性，利用感知契约的原像。其次，在运行时，使用风险启发式方法从不确定的估计中选择特定状态来驱动控制决策。我们对这种运行时感知修正策略进行了广泛的基于仿真的评估，针对不同的基于视觉的自适应巡航控制（ACC）模块，在不同的天气条件和道路场景下进行。在45个原始基于感知的控制系统（使用Yolo和LaneNet）导致安全违规的ACC场景中，我们的运行时感知修正在73%的场景中保持了安全性；在27%的场景中，由于感知契约原像的构建不完全符合要求，我们的方法无法恢复。此外，我们的运行时感知修正策略并不过度保守——在修正后的场景中，平均仅增加了2.8%的完成时间，且干预温和。
英文摘要
Learning-enabled perception is important in many autonomous systems. Unlike traditional sensors, the boundary where ML perception does or does not work is poorly characterized. Incorrect perception can lead to unsafe or overtly conservative downstream control actions. In this paper, we propose a two-step strategy for correcting ML-based state estimation. First, an offline computation is used to characterize the uncertainties resulting from the ML module's state estimation, using preimages of perception contracts. Second, at runtime, a risk heuristic is used to choose particular states from the uncertain estimates to drive the control decisions. We perform extensive simulation-based evaluation of this runtime perception correction strategy on different vision-based adaptive cruise controllers (ACC modules), in different weather conditions, and road scenarios. Out of 45 ACC scenarios where the original perception-based control system using Yolo and LaneNet led to safety violations, in 73% of the scenarios, our runtime perception correction preserved safety; our method wouldn't be able to recover 27% of the scenarios where the construction of the preimages of perception contracts is not fully conformant. Further, our runtime perception correction strategy is not overly conservative---on the average only a 2.8% increase in completion time is experienced in the corrected scenarios, with mild interventions.
