Modern vehicles are increasingly complicated systems dependent on an array of computing hardware and software. To meet the growing demand for increased safety and new features, manufacturers are using more sophisticated in-vehicle networks to connect more Electronic Control Units (ECU) and actuators. Autonomous vehicles are even more complicated, creating additional possible attack angles for hackers and a greater potential for security vulnerabilities. A security breach could cause malfunction or unexpected behavior of ECUs and the vehicle as a whole, which lead to damages from reputational to serious safety accidents. One of Motional's core values is: "Safety as our bedrock." To practice this, we reduce cybersecurity vulnerabilities through system hardening; or reducing our attack surface and introducing fundamental security mechanisms to mitigate threats.

Learning by observation can be of key importance whenever agents sharing similar features want to learn from each other. This paper presents an agent architecture that enables software agents to learn by direct observation of the actions executed by expert agents while they are performing a task. This is possible because the proposed architecture displays information that is essential for observation, making it possible for software agents to observe each other. The agent architecture supports a learning process that covers all aspects of learning by observation, such as discovering and observing experts, learning from the observed data, applying the acquired knowledge and evaluating the agent's progress. The evaluation provides control over the decision to obtain new knowledge or apply the acquired knowledge to new problems. We combine two methods for learning from the observed information. The first one, the recall method, uses the sequence on which the actions were observed to solve new problems. The second one, the classification method, categorizes the information in the observed data and determines to which set of categories the new problems belong. Results show that agents are able to learn in conditions where common supervised learning algorithms fail, such as when agents do not know the results of their actions a priori or when not all the effects of the actions are visible. The results also show that our approach provides better results than other learning methods since it requires shorter learning periods.