ODD-Based Long-Term Decision-Making for Intelligent Vehicles

In complex environments, Intelligent Vehicles (IVs) require reliable decision-making to ensure safe and efficient navigation. To guarantee the proper operation of the IV, it must operate within its Operational Design Domain (ODD) [1]. This means that, in a specific context, the vehicle must have the necessary capabilities to guarantee the efficient and robust working of its functions. Current decision-making approaches primarily address dynamic constraints but often fail to consider the full vehicle's ODD. When the ODD is considered, it is monitored to make only an immediate decision. For instance, if it starts raining, the vehicle decides to reduce speed and increase the following distance to ensure safe braking on wet roads. However, neglecting future long-term states could lead the vehicle to an imminent departure from its ODD, and this could increase the appearance of risky situations. This paper presents a decision-making architecture, focused on the tactical level, designed to address these gaps. By evaluating the vehicle's ODD across both current and future states, the proposed framework constructs a reachable horizon that supports long-term decision-making. The proposed architecture, called ODD-aptive, formalizes the decision-making process as a Markov Decision Process (MDP), enabling a systematic analysis of vehicle capabilities and reachable states confined to the vehicle's ODD. By focusing on long-term decision-making, this approach ensures IVs to remain functional, adaptable, and safe even under dynamic and evolving conditions, supporting a reliable autonomy of IVs.