Supervised Learning via Crowdsourcing
It isn't a new concept that training deep learning systems requires massive amounts of data. In many cases, this data exists in the form of database content or even web crawling output. AI systems for medical applications can often be trained with gigabytes of readily available data.
How, then, do you train systems that need to be able to differentiate between dangerous and non-dangerous situations based on visual input? Crowdsourcing can be a solution. While many projects in autonomous driving research rely on live LIDAR analysis, these systems are still relatively expensive when compared to standard video cameras. Also, interpreting LIDAR results can be challenging, especially in fog or rain, where the LASER beams are basically completely obliterated.
Several recent efforts in autonomous driving research are focussing on the analysis of standard high-resolution video camera streams via deep learning systems. As with any deep learning AI, it needs to be trained in order to identify dangerous situations or situations that occur very rarely (so-called "edge cases") but still require a proper and safe reaction of the autonomous vehicle. A frequently cited edge case, as an example, is the driver of an electric wheelchair on the street.
Researchers are using crowdsourcing to train these systems - with smartphone apps where users pick out and identify objects and / or situations and get paid for their work. Crowdsourcing is ideal for this type of training, for several reasons. One is the relatively low cost associated with getting feedback. The apps that present the images to be worked with use gamification to make the work more appealing, and this combined with a relatively low payout serves to keep the crowd-workforce active. Getting feedback from many different people serves to reduce bias in the training set, though a challenge here is keeping a good mix of participants from different continents, as priorities in driving are quite different between the US, Europe and Asia. Take Stop-signs as an example - these have very different impact on a driver depending on where he or she is from.
All in all, crowdsourced training of deep learning AI may be the most efficient way forward make get autonomous driving possible using just video camera input - even if this technology is combined with other sensors (feeding into other AI systems), it will benefit the introduction of high-safety autonomous driving in the near future.