You don't really ever have "two sensors" in the sense that it's two measurements. You have multiple measurements from each sensor every second. Then you accumulate that information over time to get a reliable picture. If the probability of failure on each frame were independent, it would be a relatively simple problem, but of course you're generally going to get a fairly high correlation from one frame to the next about whether or not there's a pedestrian in a certain location. The nice thing about having multiple sensing modalities is that the failure correlation between them is a lot lower.

For example, say you have a pedestrian that's partially obscured by a car or another object, and maybe they're wearing a hat or a mask or wearing a backpack or carrying a kid or something, it may look unusual enough that either the camera or the lidar isn't going to recognize it as a person reliably. However, since the camera is generally looking at color, texture, etc in 2D, and the Lidar is looking at 3D shapes, they'll tend to fail in different situations. If the car thinks there's a substantial probability of a human in the driving path, it's going to swerve or hit the brakes.