Adaptive Analytic Hierarchy Process-Based Decision Making to Enhance Vehicle Autonomy

In this paper, an autonomous vehicle function management methodology is studied. At the decision-making level, the proposed system chooses the optimal function [adaptive cruise control (ACC) or a lane change maneuver (LCM)] that is consistent with the user-selected driving mode, such as fast travel, cautious driving, and efficient travel mode, whereas the control level is implemented via a neuromorphic strategy based on the brain limbic system. To realize the decision-making strategy, the analytic hierarchy process (AHP) is used by considering driving safety, traffic flow, and fuel efficiency as objectives, while LCM and ACC are chosen as the alternative functions. The adaptive AHP is further suggested to cope with the dynamically changing traffic environment. The proposed adaptive AHP algorithm provides an optimal relative importance matrix that is essential to making decisions under varying traffic situations and driving modes. The simulation results show that the proposed structure produces an effective approach to autonomous vehicle function management.

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