Adaptive Decision Making based on Temporal Information Dynamics

To increase road safety and efficiency, connected vehicles rely on the exchange of information. On each vehicle, a decision-making algorithm processes the received information and determines the actions that are to be taken. State-of-the-art decision approaches focus on static information and ignore the temporal dynamics of the environment, which is characterized by high change rates in a vehicular scenario. Hence, they keep outdated information longer than necessary and miss optimization potential. To address this problem, we propose a quality of information (QoI) weight based on a Hidden Markov Model for each information type, e.g., a road congestion state. Using this weight in the decision process allows us to combine detection accuracy of the sensor and the information lifetime in the decision-making, and, consequently, adapt to environmental changes significantly faster. We evaluate our approach for the scenario of traffic jam detection and avoidance, showing that it reduces the costs of false decisions by up to 25% compared to existing approaches.