Markovian Model-Based Safety Analysis in Perception Systems Inside Self-Driving Cars

Abstract

At present, the development of self-driving car systems has been increasing. The need for man to control all possible scenarios has led to the inclusion of theories such as human perception. This means identifying how the human brain recognizes its environment and translating it into data that a machine can learn and make decisions. For this, great doubts have been generated concerning safety; in the present work, the Markovian model is implemented as a stochastic method in a constantly changing system. The model shows possible forms, future states' transitions rate of changes, and probabilities without depending on past states. Markovian models can also recognize patterns, make predictions, and learn sequential statistics.