Tesla Vision vs LiDAR in 2026: Why Cameras Are Winning the Autonomous Driving War | Taha Abbasi

The Vision-Only Bet Is Paying Off

Taha Abbasi analyzes why Tesla's controversial decision to abandon radar and LiDAR in favor of camera-only perception is looking increasingly vindicated in 2026.

When Tesla removed radar sensors from its vehicles and doubled down on camera-only perception, critics called it reckless. LiDAR manufacturers published papers arguing depth sensors were essential for safety. Waymo, Cruise, and Zoox all used LiDAR-heavy sensor suites. Tesla was the outlier — betting that cameras plus neural networks could achieve what physics-based sensors provide.

In 2026, the scoreboard is becoming clear. Tesla's FSD is operating in more cities, on more roads, and handling more edge cases than any LiDAR-based system outside of Waymo's geofenced zones. As Taha Abbasi has argued, the question was never whether LiDAR works — it's whether it scales.

The Scaling Argument

LiDAR systems cost thousands of dollars per vehicle and require high-definition 3D maps of every road the vehicle will travel. This works for geofenced robotaxi zones like Waymo's San Francisco service area. It doesn't work for a consumer vehicle that needs to drive anywhere its owner wants to go.

Tesla's cameras cost under $100 per vehicle and work anywhere there's visible light. The intelligence comes from the neural network, which improves with every mile driven by every Tesla on the road. As Taha Abbasi explains, this is the fundamental advantage: Tesla has millions of vehicles collecting training data globally. LiDAR-based systems have thousands of vehicles in specific cities.

The Depth Perception Breakthrough

The biggest criticism of camera-only systems was depth estimation — cameras capture 2D images, not 3D point clouds. Tesla's answer: train neural networks to estimate depth from stereo vision and temporal information, the same way humans do. Recent analysis from NotATeslaApp confirms that Tesla's depth estimation has reached remarkable accuracy, rivaling LiDAR in many scenarios.

Taha Abbasi sees this as a vindication of first-principles thinking. Humans drive with two cameras (eyes) and a neural network (brain). If you build a sufficiently capable neural network, cameras provide enough information. The question was always whether the neural network could be made good enough — and the answer is increasingly yes.

What This Means Going Forward

The vision vs. LiDAR debate isn't completely settled, but the momentum has shifted decisively toward Tesla's approach. Waymo continues to demonstrate excellent performance within its geofenced zones, proving that LiDAR works for controlled environments. But for the broader challenge of autonomous driving everywhere — which is what consumers actually want — camera-based systems are pulling ahead.

---

About the Author: Taha Abbasi is a technology executive, CTO, and applied frontier tech builder. Read more on Grokpedia | YouTube: The Brown Cowboy | tahaabbasi.com