Robots_part 1

Self-driving cars vs autonomous robots

For the next two days, I’d like to dive a little deeper into the differences between self-driving cars and autonomous robots.

At first glance, they might seem like they’re doing pretty much the same thing—moving around without a human driver or operator. And in many ways, they are both part of the broader world of Physical AI. But when you look a little closer, there are some key differences in how they’re designed, trained, and deployed.

Self-driving cars are built for highly structured environments like roads, highways, and intersections, where the rules of behavior (at least in theory) are relatively clear. They need to detect lanes, signs, traffic lights, pedestrians, and other vehicles—and then make split-second decisions while moving at high speeds.

For localization, self-driving cars typically rely on GPS, combined with high-definition maps and real-time sensor data, to precisely determine their position. Outdoors, GPS signals are generally reliable enough to keep them on course, especially with enhancements like RTK (Real-Time Kinematic) corrections.

Because of the high speeds involved and the critical importance of safety, self-driving cars place a heavy emphasis on prediction accuracy, environmental awareness, and strict adherence to traffic rules. Mistakes here can have life-threatening consequences.

Interestingly, this reliance on external infrastructure like GPS and standardized environments highlights one of the core differences that make developing autonomous robots even more challenging.

In tomorrow’s journal, I’ll take a closer look at autonomous robots—how they navigate, why their environments are often more unpredictable, and what makes their development uniquely difficult.