Abstract:
Road accidents contributes as one of the major causes of deaths in the world. Irresponsible
driving behaviors such as not obeying road rules, driving under the influence of alcohol and
drugs and driving when sleepy and tired accounts for most of the road accidents making human
error the main reason. Tech giants in the industry such as Tesla, Baidu and Google are
conducting immense research on autonomous driving to let artificial intelligence take the
responsibility of driving because compared to humans it doesn’t possess weaknesses such as
being drowsy and distracted and it possess quality attributes such as faster decision taking. It
could assist in bringing down the deaths happening due to road accidents drastically. As most
of the research on this area is done in countries with disciplined drivers, it is hard to expect that
they will perform the same in countries like Sri Lanka which has bad driving patterns.
An approach which has produced promising results in the area of autonomous driving is
imitation learning which the actions of an expert driver is imitated. Despite the promising
results, the approach could produce erroneous results on scenarios which the AI has not yet
experienced. Reinforcement learning is a good approach to learn different type of scenarios by
exploring the environment by itself. However, the research conducted in the autonomous
driving area by using reinforcement learning alone hasn’t produced promising results compared
to imitation learning. In this paper it is focused to reduce the erroneous results of imitation
learning in scenarios which the AI has not yet experienced by combining it with reinforcement
learning.
