As of now, driverless car technology is very temperamental, and errors are still far too common to be able to safely rely on them. The eventual aim is to achieve artificial general intelligence where the technology can replicate the human driver without replicating human mistakes.
Google wants to implement a service where the public can hail down autonomous cars on the street to take them wherever they desire, whereas we already regularly see Apple’s autonomous vehicles on the street. However, what is most staggering is that Uber, one of the UKs biggest taxi services is in talks with Waymo about adding some of its 62,000 autonomous minivan fleet to their taxi cars.
One major problem is that in theory, machine learning will teach the technology the rules of the road and how to legally drive, but this technology does not have the judgement or the responses of actual human beings. As a result, it doesn´t always take into account what other drivers are doing or the ever-changing environment around it and therefore, AI decision-making is becoming more of an obstacle when it comes to gaining the trust of the public.
Very common occurrences, ranging from safely allowing another vehicle to pass which may involve a lane change, to adapting to varying weather changes are proving to be challenges for various companies within this field. The weather is a particular worry for some, as human beings find driving in harsh conditions difficult at the best of times. We have already seen that the sensors on Google AI cars have been affected by rain as it can affect the LIDAR´s ability to read the road signs which shows just how vulnerable even the most advanced technology can be.
“The idea is to use humans to bridge the gap between simulation and the real world”.
The researchers states that over time, a ‘list’ is compiled of different situations and what the correct and incorrect reactions are in the hope that through machine learning, this AI technology can, over time, improve greatly.
