[DennisP]

Conflicting data is an issue even when you just have lidar, and it's not hard to deal with.

The cofounder of Waymo taught one of the first Udacity courses on this subject. He went through a small Python project that processed lidar point clouds for self-driving. The data is noisy, you get conflicting information from different points, and the code aggregates all that into the most likely 3D model of the world.

Additional sensor inputs are just more of the same, and neural nets are pretty good at this sort of thing. They'd even learn which sensors are more reliable in different scenarios.

As for "more data isn't always better," I've mostly seen that applied to training, not inference in real-time control systems. Even for training, it turned out people had been fooled by a local maximum, and once past that, more data really was better.