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three techniques to enable the self-driving car to
make a decision would yield the best possible results.
6 CONCLUSIONS & FUTURE
WORK
The aim of the paper was to resolve conflicts that
could arise in a self-driving car in the event of un-
avoidable accidents. A conflict resolution technique
was implemented using the Thomas-Kilmann Con-
flict Model, along with the Verifying Nodes technique
to resolve the conflicts that may arise. Both tech-
niques were applied over a decision tree that was con-
structed based on the MIT Moral Machine Experi-
ment results (Awad and Dsouza, 2018). We managed
to create a system where we tested 80 different con-
flicting scenarios achieving a decidability of 97% in
the scenarios, and an average accuracy of 80%. Over-
all, we managed to resolve 97% of the conflicting sce-
narios generated, in an average of just 0.2 seconds,
leaving out only 3% where the scenarios were fairly
identical that randomly choosing one over the other
would have made no difference.
Future research can go in several directions. First,
the presented testing scenarios can be enriched by
gathered more data about the environment using im-
age processing techniques. The behaviour of the pro-
posed conflict-resolution model can then be further
verified. It is also worth investigating the combina-
tion of the three different resolution techniques which
could potentially result in faster and even more ac-
curate results. Extending the work presented in this
paper beyond the single car case to resolve conflicts
among a swarm of cars is also a natural next step.
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