PerceMon: Online Monitoring for Perception Systems
PerceMon: Online Monitoring for Perception Systems
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DOI：
10.1007/978-3-030-88494-9_18
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发表时间：
2021-08
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通讯作者：
Anand Balakrishnan;Jyotirmoy V. Deshmukh;Bardh Hoxha;Tomoya Yamaguchi;Georgios Fainekos
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作者：
Anand Balakrishnan;Jyotirmoy V. Deshmukh;Bardh Hoxha;Tomoya Yamaguchi;Georgios Fainekos
自动驾驶车辆中的感知算法对于车辆理解其周围环境的语义至关重要，包括检测和跟踪环境中的物体。这些算法的输出反过来又用于安全关键场景的决策，如碰撞避免和自动紧急制动。因此，在运行时监控这样的感知系统至关重要。然而，由于感知系统的输出的高层次，复杂的表示，这是一个挑战，以测试和验证这些系统，特别是在运行时。在本文中，我们提出了一个运行时监控工具，PerceMon，可以监控任意规格的时间质量时序逻辑（TQTL）及其扩展与空间运营商。我们将该工具与CARLA自动驾驶汽车仿真环境和ROS中间件平台集成，同时监控最先进的目标检测和跟踪算法的属性。
Perception algorithms in autonomous vehicles are vital for the vehicle to understand the semantics of its surroundings, including detection and tracking of objects in the environment. The outputs of these algorithms are in turn used for decision-making in safety-critical scenarios like collision avoidance, and automated emergency braking. Thus, it is crucial to monitor such perception systems at runtime. However, due to the high-level, complex representations of the outputs of perception systems, it is a challenge to test and verify these systems, especially at runtime. In this paper, we present a runtime monitoring tool, PerceMon that can monitor arbitrary specifications in Timed Quality Temporal Logic (TQTL) and its extensions with spatial operators. We integrate the tool with the CARLA autonomous vehicle simulation environment and the ROS middleware platform while monitoring properties on state-of-the-art object detection and tracking algorithms.