Multi-Level Cognitive, Risk-Aware Reconfiguration of the Level of Autonomy in Highly Automated Vehicles

Highly automated driving continues to attract massive research efforts at a global scale, leading to developments that pave the way towards the future of mobility. Despite the innumerable innovations that have already penetrated our lives, there are still several challenges to overcome in this area. Indicatively, highly automated driving decisions are often associated with undertaking partially unknown risks, which are critical for fail-safe and fail-operational components and systems of modern vehicles, operating at alternative Levels of Autonomy (LoA). As such, novel functionality is required to a-priori assess those risks and propose decisions that affect vehicular behavior, in a cognitive (knowledge-based) manner. This paper proposes an in-vehicle cognitive management functionality that dynamically suggests the most appropriate LoA, by (a) incorporating in the decision-making process the "a priori risk assessment" associated with every possible decision, by applying the Failure Mode and Effect Analysis (FMEA) to model the identified risks of each candidate LoA, (b) predicting at a 1st level the most suitable LoA by means of Bayesian networks, (c) further enhancing predictions at a 2nd level, by using neural networks, namely a Binary Classification model and a MultiClass Classification model and (d) storing and exploiting in the future all associated decisions through experience creation (3rd cognition level). A simulation scenario is used to validate the effectiveness of the proposed functionality. Results showcase that the method can a priori, quickly, and effectively lead to optimum LoA decisions with minimum risk, contributing further to safer roads.