Vision vs. LiDAR: The Battle for Autonomous Driving’s Future

Autonomous driving is no longer a moonshot, it’s the future of mobility. But one of the biggest questions still divides the industry:

Tesla says no. Waymo says absolutely. And the rest of the industry is split down the middle.

But at Imagry, we’ve chosen a third path: A real-time, vision-based autonomous driving system that doesn’t rely on LiDAR, HD maps, or cloud connectivity, but which can integrate easily with other types of sensors if the customer so desires. And here’s the kicker, it’s already proving itself on real roads.

Let’s break down the two major approaches, why this debate matters, and where Imagry fits in.

LiDAR (left) vs Vision (right): Two different approaches to autonomous driving technology.

LiDAR (Light Detection and Ranging) uses lasers to build a precise 3D map of the environment around the vehicle. It’s like giving your car a sixth sense, measuring distance by firing light pulses and detecting how long they take to bounce back.

The Pros:

• Highly accurate depth perception
• Reliable in low-light or night conditions
• Useful for high-precision localization

The Cons:

• Expensive and bulky
• Data-heavy, requires powerful compute
• Can struggle in rain, snow, or fog
• Doesn’t scale easily in production fleets

What about Vision-Based Autonomy?

This approach relies on cameras, just like human eyes. Combined with advanced computer vision and AI, these systems interpret images into a 3D understanding of the world.

The Pros:

• Lower cost hardware
• More scalable for real-world deployment
• Mimics how human drivers perceive the road
• Easier to integrate into existing vehicles

The Cons:

• Depth perception is harder to calculate accurately
• Requires powerful AI models and data to perform well
• Performance can vary in poor lighting or extreme conditions

Tesla vs. Waymo

Tesla has gone all-in on vision-only autonomy. With its Full Self-Driving (FSD) system, launched in 2020, Tesla removed radar and LiDAR entirely. Elon Musk has repeatedly stated that “the road system is designed for eyes”, and therefore cameras are enough.

On the other hand, Waymo has built its system around LiDAR + HD maps. It relies on centimeter-level mapping and a full sensor stack (LiDAR, radar, cameras) to drive in highly geofenced zones.

This has led to a divide with Tesla prioritizing scalability, learning through data, and edge inference while Waymo prioritizes safety through redundancy, mapping, and control.

At Imagry, we’ve developed a third path. One that combines the scalability of vision with a real-time understanding of the world that doesn’t rely on pre-built maps or expensive sensors.

Here’s how we’re different:

• Vision-only sensor stack. No LiDAR. No radar. Just cameras and onboard AI.
• No HD Maps. Our system builds a dynamic, real-time view of the world, just like a human driver (only better).
• Real-world deployments. Already live in urban, suburban, and mixed-traffic environments across the U.S. and abroad.
• Open platform. Unlike Tesla, which has “walled garden” approach (developing solutions solely for its own brand), we’re built to integrate with existing vehicle platforms, so automakers and public transit fleets can plug it in, not rip and replace.

Why We Chose Vision-First

We believe that autonomy has to be affordable, adaptable, and scalable.

That means:

• Hardware needs to be cheap enough to put in every vehicle, no matter the model or trim.
• The system needs to generalize to new environments, not limit operation to pre-mapped roads.
• It needs to make decisions in real-time, not wait for the cloud.

LiDAR makes sense for research. HD maps make sense for short-term demos. But for autonomy to really scale, it has to work like a human driver: Perceive. Decide. Act.

The Real Future: Perception That Learns, Not Just Measures

The debate isn’t really about hardware. It’s about philosophy: Do we build self-driving systems that measure the world, or understand it? Do we encode rules, or build models that learn?

Our platform does just that. It learns from every experience, improves with every mile, and drives safely without needing a predefined map of the world.

We’re not just betting on cameras. We’re betting on real-time intelligence. On open systems. On freedom from costly dependencies.

In the battle between vision and LiDAR, we believe the winner won’t be the flashiest sensor…it’ll be the smartest system.