Edit: the reason here is that road paint (and the marking on a sign) is typically retroreflective, while roads (and regular painted sheet metal) are not. Retroreflective materials are very easy to detect with LIDAR. Even if you are on older roads that don't have retroreflective paint, white objects and black objects also tend to show up differently.
Edit2: there's also no reason you can't use detection algorithms to characterize pointclouds. You can also fuse the returned objects from the lidar sensor(s) with the objects from the camera(s). You have the latter problem even with just multiple cameras. Inevitably, whatever you are using for segmentation/classification will return different results on different sensors, for sensors that share the same FOV - you do need a strategy for handling this.
Tesla has not added LIDAR because of cost (and because of Elon's ego...) - they are one of the few companies going directly for consumer sales - more parts that cost $$$ and require calibration aren't good for that.
Single cameras (even stereo cameras) require a lot more assumptions about contrast and environment lighting to do image segmentation and depth maps. They're rarely as accurate as LIDAR and often far worse.
Depth maps and image segmentation are the building blocks of object identification. You can try to AI your way into those with a single camera but it'll be less accurate and more error prone.
It will mean Tesla is done with trying to avoid adding it. They test with lidar so they might as well start using it:
https://insideevs.com/news/508669/tesla-model-y-luminar-lida...
https://electrek.co/2021/05/26/lucid-reveals-ux-intuitive-us...