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Understanding and adhering to traffic regulations is essential for autonomous vehicles to ensure safety and trustworthiness. However, traffic regulations are complex, context-dependent, and differ between regions, posing a major challenge to conventional rule-based decision-making approaches. We present an interpretable, regulation-aware decision-making framework, DriveReg, which enables autonomous vehicles to understand and adhere to region-specific traffic laws and safety guidelines. The framework integrates a Retrieval Augmented Generation (RAG)-based Traffic Regulation Retrieval Agent, which retrieves relevant rules from regulatory documents based on the current situation, and a Large Language Model (LLM)-powered Reasoning Agent that evaluates actions for legal compliance and safety. Our design emphasizes interpretability to enhance transparency and trustworthiness. To support systematic evaluation, we introduce DriveReg Scenarios Dataset, a comprehensive dataset of driving scenarios across Boston, Singapore, and Los Angeles, with both hypothesized text-based cases and real-world driving data, specifically constructed and annotated to evaluate models’ capacity for regulation understanding and reasoning. We validate our framework on the DriveReg Scenarios Dataset and real-world deployment, demonstrating strong performance and robustness across diverse environments. 

This paper presents the first demonstration and comparison of two identical oscillator circuits employing piezoelectric zinc oxide (ZnO) microelectromechanical systems (MEMS) resonators, implemented on conventional printed-circuit-board (PCB) and three-dimensional (3D)-printed acrylonitrile butadiene styrene (ABS) substrates. Both oscillators operate simultaneously at dual frequencies (260 MHz and 437 MHz) without the need for additional circuitry. The MEMS resonators, fabricated on silicon-on-insulator (SOI) wafers, exhibit high-quality factors (Q), ensuring superior phase noise performance. Experimental results indicate that the oscillator packaged using 3D-printed chip-carrier assembly achieves a 2–3 dB improvement in phase noise compared to the PCB-based oscillator, attributed to the ABS substrate’s lower dielectric loss and reduced parasitic effects at radio frequency (RF). Specifically, phase noise values between −84 and −77 dBc/Hz at 1 kHz offset and a noise floor of −163 dBc/Hz at far-from-carrier offset were achieved. Additionally, the 3D-printed ABS-based oscillator delivers notably higher output power (4.575 dBm at 260 MHz and 0.147 dBm at 437 MHz). To facilitate modular characterization, advanced packaging techniques leveraging precise 3D-printed encapsulation with sub-100 μm lateral interconnects were employed. These ensured robust packaging integrity without compromising oscillator performance. Furthermore, a comparison between two transistor technologies—a silicon germanium (SiGe) heterojunction bipolar transistor (HBT) and an enhancement-mode pseudomorphic high-electron-mobility transistor (E-pHEMT)—demonstrated that SiGe HBT transistors provide superior phase noise characteristics at close-to-carrier offset frequencies, with a significant 11 dB improvement observed at 1 kHz offset. These results highlight the promising potential of 3D-printed chip-carrier packaging techniques in high-performance MEMS oscillator applications.