Verifiable Obstacle Detection
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866914636091097088 |
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| author | Bansal, Ayoosh Kim, Hunmin Yu, Simon Li, Bo Hovakimyan, Naira Caccamo, Marco Sha, Lui |
| author_facet | Bansal, Ayoosh Kim, Hunmin Yu, Simon Li, Bo Hovakimyan, Naira Caccamo, Marco Sha, Lui |
| contents | Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate from obstacle existence detection. Open source autonomous driving implementations show a perception pipeline with complex interdependent Deep Neural Networks. These networks are not fully verifiable, making them unsuitable for safety-critical tasks.
In this work, we present a safety verification of an existing LiDAR based classical obstacle detection algorithm. We establish strict bounds on the capabilities of this obstacle detection algorithm. Given safety standards, such bounds allow for determining LiDAR sensor properties that would reliably satisfy the standards. Such analysis has as yet been unattainable for neural network based perception systems. We provide a rigorous analysis of the obstacle detection system with empirical results based on real-world sensor data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_14403 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Verifiable Obstacle Detection Bansal, Ayoosh Kim, Hunmin Yu, Simon Li, Bo Hovakimyan, Naira Caccamo, Marco Sha, Lui Robotics Computer Vision and Pattern Recognition Systems and Control D.2.4; I.2.9; I.4.8 Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate from obstacle existence detection. Open source autonomous driving implementations show a perception pipeline with complex interdependent Deep Neural Networks. These networks are not fully verifiable, making them unsuitable for safety-critical tasks. In this work, we present a safety verification of an existing LiDAR based classical obstacle detection algorithm. We establish strict bounds on the capabilities of this obstacle detection algorithm. Given safety standards, such bounds allow for determining LiDAR sensor properties that would reliably satisfy the standards. Such analysis has as yet been unattainable for neural network based perception systems. We provide a rigorous analysis of the obstacle detection system with empirical results based on real-world sensor data. |
| title | Verifiable Obstacle Detection |
| topic | Robotics Computer Vision and Pattern Recognition Systems and Control D.2.4; I.2.9; I.4.8 |
| url | https://arxiv.org/abs/2208.14403 |