End-to-End Autonomous Driving

End-to-end autonomous driving is the use of a single, unified model to handle the full driving task—from perception of the environment through prediction of other agents’ behavior to planning and control of the vehicle. Instead of stitching together many hand‑engineered modules for object detection, lane following, path planning, and actuation, this approach learns a direct mapping from raw sensor inputs (such as cameras, LiDAR, and radar) to driving decisions. The goal is to create a simpler, more robust stack that can better generalize across cities, road layouts, and rare edge cases. This application matters because traditional autonomous driving stacks are complex, costly to maintain, and fragile when scaled to diverse geographies and long‑tail scenarios. As fleets collect massive amounts of driving data, end‑to‑end models can leverage that data more effectively, improving safety, adaptability, and development speed. By reducing engineering overhead and enabling faster iteration, end‑to‑end autonomous driving promises more scalable deployment of self‑driving capabilities for passenger vehicles, robo‑taxis, and commercial fleets.