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arXiv:2608. 11790v1 Announce Type: new Abstract: Accurate Global Navigation Satellite System (GNSS)-based localization is essential for safe and reliable autonomous driving.
The paper introduces a small language model (SLM)-based framework that detects and classifies GNSS spoofing attacks on autonomous vehicles by converting vehicle states from GNSS and other sensors into structured semantic narratives. The SLM achieves performance comparable to large language models, with an average accuracy of 96.99%, while offering lower inference latency and reduced GPU memory usage. Field tests in Clemson, South Carolina, confirm the framework’s real‑time detection capabilities on resource‑constrained vehicular platforms.
arXiv:2605. 21446v2 Announce Type: replace-cross Abstract: Interpretable autonomous driving planners depend not only on generating explanations, but also on those explanations remaining reliable under real-world sensor degradation.
arXiv:2608. 01587v1 Announce Type: cross Abstract: Machine-learning benchmarks often pair a label that aggregates a long temporal horizon with input observed through one or a few short windows.
arXiv:2508. 21797v2 Announce Type: replace-cross Abstract: Industry 4.
arXiv:2607. 05669v1 Announce Type: cross Abstract: Reliable localization in GNSS-denied environments remains a fundamental challenge for intelligent vehicles, as inertial navigation systems accumulate unbounded drift without external correction.