Crowd-sourcing autonomous vehicle training

特許 係属中 Definitions: When driving, human drivers encounter non-events (expected events like traffic lights turning red) as well as events (unexpected events like an unaccompanied child standing at the edge of the road). Events are captured from crowd-sourced participants driving their own cars during their routine lives. The determination that a particular scenario is an unexpected event is carried out automatically (without manual input) by sensing eye, foot and hand positions and movements, and comparing it to a map. This arrangement can not only sense the driver’s actions to change speed or direction in response to such unexpected events, it can also detect the driver’s intentions to do so. It can also determine various driving attributes and mental components of the driver. This in turn allows drivers to be scored and ranked in each geographical region, so that expert drivers can be identified. Signatures for events extracted from the driving patterns of such expert drivers are more reliable, and form better training routines for improving autonomous vehicle software. A database of thousands of such signatures, and their variations, are obtained by crowd-sourced expert drivers. These signatures are then used by autonomous vehicles to identify potential or real events, and react to these events in a human-like manner. Companies developing AVs need not wait for a history of “millions of miles tested”, but instead can rely on extremely quick, cheap and highly reliable, human-like training sub-routines extracted from crowd-sourced drivers. This scheme allows millions of volunteer drivers to transform their routine driving into event-capturing runs, with no training or expensive equipment required.