One of the reasons we don’t yet have self-driving cars and mini-helicopters delivering online purchases is that autonomous vehicles tend not to perform well under pressure. A system that can flawlessly parallel park at 5 mph may have trouble avoiding obstacles at 35 mph.
Part of the problem is the time it takes to produce and interpret camera data. An autonomous vehicle using a standard camera to monitor its surroundings might take about a fifth of a second to update its location. That’s good enough for normal operating conditions but not nearly fast enough to handle the unexpected.
Andrea Censi, a research scientist in MIT’s Laboratory for Information and Decision Systems, thinks the solution could be to supplement cameras with a new type of sensor called an event-based (or “neuromorphic”) sensor, which can take measurements a million times a second.
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