This is why we use a redundant geometric approach (with LiDAR) as a fallback to NNs in our perception stack. NNs can have false negatives which is unacceptable, but dealing with false positives is more manageable and safer.
Does lidar have anything to do with the redundancy?
Since a lidar uses a discrete frequency band and it's own energy source, it's much easier to process the signal for depth information (using the lag between sending and receiving a return pulse(s) and the wavelength). Arguably such a system might provide a better basis for a backup system based on modelling obstacles and their distances from the vehicle.