Using Infrastructure-to-Vehicle (I2V) information can be of great benefit
when driving autonomously in high-density traffic situations with limited
visibility, since the sensing capabilities of the vehicle are enhanced by
external sensors. In this research, a method is introduced to increase the
vehicle’s self-awareness in intersections for one of the largest foreseen
challenges when using I2V communication: cyber security. The introduced anomaly
detection algorithm, running on the automated vehicle, assesses the health of
the I2V communication against multiple cyber security attacks. The analysis is
done in a simulation environment, using cyber-attack scenarios from the
Secredas Project (Cyber Security for Cross Domain Reliable Dependable Automated
Systems) and provides insights into the limitations the vehicle has when facing
I2V cyber attacks of different types and amplitudes and when sensor redundancy
is lost. The results demonstrate that anomalies injected can be robustly
detected and mitigated by the autonomous vehicle, allowing it to react more
safely and comfortably and maintaining correct object tracking in

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