Cognitive Risk-Assessment and Decision-Making Framework for Increasing in-Vehicle Intelligence

The key challenge for future automated driving systems is the need to imitate intelligence and ability of human drivers, both in terms agility, as well their intuitive understanding surroundings dynamics vehicle. In this paper a model that utilizes data from different sources coming vehicular sensor networks presented. processed an intelligent manner while integrating knowledge experience associated with potential any decision. Moreover, appropriate directives safety vehicle alerts case upcoming emergencies are provided driver. innovation lies attributing human-like cognitive capabilities—non-causal reasoning, predictive decision-making, learning—integrated into processes perception decision-making safety-critical autonomous use cases. overall approach described formulated, heuristic function proposed assisting driver reaching decisions. Comprehensive results our experiments showcase its efficiency, simplicity, scalability.