Enabling Situation Awareness at Intersections for IVC Congestion Control Mechanisms

IAS aim to assist road users in avoiding collisions at intersections, either by warning the driver or by triggering automated actions. Such a system can be realized based on passive scanning only (e.g., using LiDAR) or supported by active IVC. The main reason to use IVC is its ability to provide situation awareness even when a possible crash candidate is not yet in visual range. The IVC research community has identified beaconing, i.e., one-hop broadcast, as the primary communication primitive for vehicular safety applications. Recently, adaptive beaconing approaches have been studied and different congestion control mechanisms have been proposed to cope with the diverse demands of vehicular networks. In this paper, we show that current state-of-the-art congestion control mechanisms are not able to support IAS adequately. Specifically, current approaches fail due to their inherent fairness postulation, i.e., they lack fine grained prioritization. We propose a solution that extends congestion control mechanisms by allowing temporary exceptions for vehicles in dangerous situations, that is, situation-based rate adaptation. We show the applicability for two state-of-the-art congestion control mechanisms, namely TRC and DynB, in two different vehicular environments, rural and downtown.