Published on January 13, 2026 by Claudio Cabete
Autonomous vehicles are often marketed as the pinnacle of modern engineering — a fusion of AI, robotics, and safety‑first design. But beneath the glossy demos and carefully curated test drives, there’s a growing problem in the industry: an overreliance on LIDAR as a safety blanket.
LIDAR is impressive. It’s precise, high‑resolution, and expensive enough to make investors feel like they’re funding “real” innovation. But it also creates a dangerous illusion: that autonomy is a sensor problem rather than a cognition problem.
And that illusion is slowing down real progress.
Humans have been driving for over a century with:
That’s it. No spinning lasers. No millimeter‑wave radar arrays. No 3D point clouds.
Yet humans can:
We do this not because our sensors are extraordinary, but because our brain is extraordinary.
So when a modern autonomous car ships with eight cameras, multiple microphones, and a supercomputer, it already has far more raw sensory input than a human driver. The bottleneck isn’t the sensors.
The bottleneck is the brain.
Here’s the uncomfortable truth: LIDAR makes developers lazy.
Not intentionally — but structurally.
When a team knows they have a perfect 3D point cloud, they stop pushing the vision system to understand the world. They stop refining the neural networks that interpret motion, depth, and intent. They stop building the kind of robust, generalizable perception that humans rely on every day.
LIDAR becomes a crutch:
But “later” never comes, because the system appears to work well enough in controlled demos.
The result is a vehicle that doesn’t truly understand the world — it just measures it.
And measurement is not comprehension.
If autonomous vehicles are ever going to surpass human drivers, they need more than sensor fusion. They need world models — internal representations of physics, motion, behavior, and cause‑and‑effect.
A real autonomous driving brain should be able to:
These are not LIDAR problems. These are cognition problems.
And cognition only improves when the system is forced to rely on the same constraints humans do: vision, sound, and experience.
A camera‑based system has several advantages:
Roads, signs, signals, markings, and behaviors are designed for human eyes. Not lasers.
Depth from motion, object permanence, prediction — these are the foundations of intelligence.
Cameras are cheap. LIDAR is not.
Humans drive in rain, snow, fog, glare, and darkness.
When you remove the shortcut, you force the brain to evolve.
It’s a software problem. A world‑modeling problem. A cognition problem.
Throwing more sensors at the car doesn’t make it smarter — it just makes it more expensive.
If we want autonomous vehicles that truly understand the world, we need to stop building sensor towers and start building better brains.
The companies that embrace this will lead the future. The ones that cling to LIDAR will keep producing impressive demos… and disappointing reality.