Kana Nagai, Yihe Chen, M. Spenko, R. Henderson, B. Pervan
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Integrity with Extraction Faults in LiDAR-Based Urban Navigation for Driverless Vehicles
This paper examines the safety of LiDAR-based navigation for driverless vehicles and aims to reduce the risk of extracting information from undesired obstacles. We define the faults of a LiDAR navigation system, derive the integrity risk equation, and suggest landmark environments to reduce the risk of fault-free position error and data association faults. We also present a method to quantify feature extraction risk using reflective tape on desired landmarks to enhance the intensity of returned signals. The high-intensity returns are used in feature extraction decisions between obstacles and pre-defined landmarks using the Neyman-Pearson Lemma. Our experiments demonstrate that the probability of incorrect extraction is below 10−14, and the method is sufficient to ensure safety.