System Design for Autonomous Vehicles with Drago Anguelov - #454

Autonomous driving requires a holistic system design that integrates hardware, software, and rigorous simulation to achieve safe, scalable operations. Waymo leverages a sensor-fusion approach, combining LiDAR, radar, and cameras to ensure redundancy and robustness against the long-tail of edge cases. Machine learning serves as a critical enabler for this scalability, transitioning from traditional expert-designed modules to more adaptive, data-driven models. While deep learning models offer powerful predictive capabilities, they must be balanced with hybrid architectures that maintain safety in unfamiliar environments. Real-world deployment in cities like Phoenix provides essential data for refining behavior models and navigating complex urban interactions. Ultimately, the transition from standalone models to integrated, system-wide architectures represents the next frontier in robotics, moving beyond simple perception to sophisticated, context-aware decision-making.