@inproceedings{7ad0e2a9df2e48d289babe4bb69ae9ba,
title = "Verifiable Obstacle Detection",
abstract = "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.",
keywords = "Autonomous vehicles, Object detection, Vehicle safety",
author = "Ayoosh Bansal and Hunmin Kim and Simon Yu and Bo Li and Naira Hovakimyan and Marco Caccamo and Lui Sha",
note = "The material presented in this paper is based upon work supported by the National Science Foundation (NSF) under grant no. CNS 1932529, ECCS 2020289, the Air Force Office of Scientific Research (AFOSR) under grant no. #FA9550-21-1-0411, the National Aeronautics and Space Administration (NASA) under grant no. 80NSSC20M0229, AWD-000577-G1, and University of Illinois Urbana-Champaign under grant no. STII-21-06. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and don{\textquoteright}t necessarily reflect the views of the sponsors.; 33rd IEEE International Symposium on Software Reliability Engineering, ISSRE 2022 ; Conference date: 31-10-2021 Through 03-11-2021",
year = "2022",
doi = "10.1109/ISSRE55969.2022.00017",
language = "English (US)",
series = "Proceedings - International Symposium on Software Reliability Engineering, ISSRE",
publisher = "IEEE Computer Society",
pages = "61--72",
booktitle = "Proceedings - 2022 IEEE 33rd International Symposium on Software Reliability Engineering, ISSRE 2022",
}