Autonomous Vehicles

Autonomous vehicles have moved from research labs to public roads. Waymo now operates fully driverless robo-taxi services across multiple US cities, while Cruise and others race to scale commercial deployments. The technology stack powering these vehicles—spanning perception, planning, and control—represents one of the most complex engineering challenges in robotics.

Autonomous trucking is widely seen as the biggest near-term commercial opportunity. Long-haul freight routes on highways present a simpler driving domain than dense urban streets, and the economics are compelling: driver shortages, rising labor costs, and the ability for autonomous trucks to operate around the clock are accelerating adoption. Companies like Aurora, TuSimple, and Kodiak Robotics are leading the charge toward hub-to-hub autonomous freight.

The sensor debate continues to shape the industry. Tesla pursues a camera-only approach with its Full Self-Driving system, arguing that computer vision alone can achieve human-level driving. Waymo, Cruise, and most other players rely on LiDAR combined with cameras and radar for redundant 3D perception. Meanwhile, LiDAR costs have plummeted from $75,000 per unit to under $500, making the technology accessible to a broader range of vehicle platforms.

Regulatory progress varies globally. The US has seen state-by-state approvals, with California, Arizona, and Texas leading in permits for driverless operations. China is advancing rapidly with companies like Baidu Apollo and Pony.ai securing commercial licenses. Europe is taking a more cautious, standards-driven approach with the EU AI Act setting frameworks for autonomous system safety.