This paper surveys a number of recent developments in modern Global\nNavigation Satellite Systems (GNSS) and investigates the possible impact on\nautonomous driving architectures. Modern GNSS now consist of four independent\nglobal satellite constellations delivering modernized signals at multiple civil\nfrequencies. New ground monitoring infrastructure, mathematical models, and\ninternet services correct for errors in the GNSS signals at continent scale.\nMass-market automotive-grade receiver chipsets are available at low Cost, Size,\nWeight, and Power (CSWaP). The result is that GNSS in 2020 delivers better than\nlane-level accurate localization with 99.99999% integrity guarantees at over\n95% availability. In autonomous driving, SAE Level 2 partially autonomous\nvehicles are now available to consumers, capable of autonomously following\nlanes and performing basic maneuvers under human supervision. Furthermore, the\nfirst pilot programs of SAE Level 4 driverless vehicles are being demonstrated\non public roads. However, autonomous driving is not a solved problem. GNSS can\nhelp. Specifically, incorporating high-integrity GNSS lane determination into\nvision-based architectures can unlock lane-level maneuvers and provide\noversight to guarantee safety. Incorporating precision GNSS into LiDAR-based\nsystems can unlock robustness and additional fallbacks for safety and utility.\nLastly, GNSS provides interoperability through consistent timing and reference\nframes for future V2X scenarios.\n