  Under unexpected conditions or scenarios, autonomous vehicles (AV) are more
likely to follow abnormal unplanned actions, due to the limited set of rules or
amount of experience they possess at that time. Enabling AV to measure the
degree at which their movements are novel in real-time may help to decrease any
possible negative consequences. We propose a method based on the Local Outlier
Factor (LOF) algorithm to quantify this novelty measure. We extracted features
from the inertial measurement unit (IMU) sensor's readings, which captures the
vehicle's motion. We followed a novelty detection approach in which the model
is fitted only using the normal data. Using datasets obtained from real-world
vehicle missions, we demonstrate that the suggested metric can quantify to some
extent the degree of novelty. Finally, a performance evaluation of the model
confirms that our novelty metric can be practical.
