This is just a database of normal driving. That's useful for learning how to follow the car in front and avoid stationary objects, but not much else. It's going to result in systems that drive like humans right up to the point they do something really bad.
A more useful database would be the one Nexar is accumulating.[1] They collect dashcam imagery of events where the driver did a hard brake or the system detected some other hazardous condition. That database could be used to train a system which recognizes trouble before braking starts.
Both systems need a much wider field of view. Probably at least 160 degrees, so cross traffic shows up before the collision.
Definitely good points. We have three cameras that are arranged colinearly along the whole width of the windshield, so this dataset has a pretty big effective field of view. And while it is currently limited in a lot of ways, it's just the beginning of the types of data we will be releasing. Everything will start scaling up to cover more use cases, as this data is mainly meant to support the training of a visual network for steering wheel predictions. For the moment, we actually do want to train the networks to drive like "normal" humans in normal situations. Thanks for your thoughts!
A more useful database would be the one Nexar is accumulating.[1] They collect dashcam imagery of events where the driver did a hard brake or the system detected some other hazardous condition. That database could be used to train a system which recognizes trouble before braking starts.
Both systems need a much wider field of view. Probably at least 160 degrees, so cross traffic shows up before the collision.
[1] http://spectrum.ieee.org/cars-that-think/transportation/sens...