Full self driving tested in china. Tesla wins.

[syed putra]

But I Hate Tesla cars. So I will keep using my old ICE. Until Chinese cars level up.

 

[ftan42]

Influencers will often only promote their favorite idol products, ignoring potential flaws if any.

 

[k1976]

But I Hate Tesla cars. So I will keep using my old ICE. Until Chinese cars level up.

cum…join the dark sai

 

[Hightech88]

Tesla FTW! Buying Model 3 nao!!! Oh wait.. EV's PCB road tax, insurance and not forgetting COE so high. Fark the PAP NBCB.

 

[Leongsam]

Today, big discussion with my engineers (at Argil) about why Elon kicked LIDAR out of his self-driving cars. Radical choice, mocked for years, and as usual he was right from the start.

LIDAR is a laser that sweeps the environment and spits out a 3D point cloud. On paper, you get the exact geometry of the world. In real life, it's a technological wart stuck on the roof because we don't know how to do better with vision alone.

Problem number one: it adds a modality to the model's training. Your network has to learn to fuse vision + LIDAR + radar + ultrasonics. Every extra sensor is a source of disagreement to arbitrate, not an additional source of info. Handcrafted sensor fusion = permanent technical debt.

Problem number two, Rich Sutton's bitter lesson: scaling compute on a single modality systematically beats hand-built architectures. Tesla dropped radar, then ultrasonics, went full end-to-end vision. Their curve on edge cases accelerated AFTER, not before. Waymo does the opposite and stays stuck in geofenced ops.

Problem number three, the most fundamental: LIDAR sees geometry, not semantics. It knows there's something, not what it is or what it's going to do. The last 9s of reliability are cognition problems, not raw perception ones. One more sensor solves nothing, it adds noise.

Sébastien Loeb throws a 208 T16 at 180 down a muddy Corsican path in the rain with zero LIDAR. Two eyes, one brain. Evolution gave predators eyes for 500 million years, not lasers. There's a reason.

LIDAR is the equivalent of Marxism applied to the economy. A planned, centralized solution that claims to explicitly model what should emerge from a distributed and adaptive system. You replace intelligence with measurement, understanding with data, emergence with control. It reassures engineers who want to specify everything upfront, just like planning reassured Soviet economists. And it fails for the same reasons: reality is too rich to be captured by a sensor, just as it's too rich to be captured by a five-year plan.

True intelligence, whether Hayek's or Tesla's, is about trusting a system that learns from experience rather than pre-encoding everything. The elegance of a solution is its signal-to-complexity ratio. LIDAR blows up the denominator.

Defending LIDAR in 2026 means preferring to stack hacks rather than solving the real problem. It's intellectual laziness dressed up as engineering rigor. The same people who defended expert systems in 2012 against deep learning. They'll end up the same way.

Never bet against end-to-end. Never bet against simplicity. Never bet against Elon.

 

[Leongsam]