PerceMon: Online Monitoring for Perception Systems
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| author | Balakrishnan, Anand Deshmukh, Jyotirmoy Hoxha, Bardh Yamaguchi, Tomoya Fainekos, Georgios |
| author_facet | Balakrishnan, Anand Deshmukh, Jyotirmoy Hoxha, Bardh Yamaguchi, Tomoya Fainekos, Georgios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2108_08289 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | PerceMon: Online Monitoring for Perception Systems Balakrishnan, Anand Deshmukh, Jyotirmoy Hoxha, Bardh Yamaguchi, Tomoya Fainekos, Georgios Robotics 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. |
| title | PerceMon: Online Monitoring for Perception Systems |
| topic | Robotics |
| url | https://arxiv.org/abs/2108.08289 |