Gestão de interseção para veículos autônomos: estratégias de imputação de dados contra falhas de comunicação.

Abstract

In addition to smart traffic lights, systems are being advanced for the efficient management of intersections, including controlling the flow of pedestrians, cyclists and autonomous vehicles. On the other hand, inefficient management of intersections can lead to congestion, delays and increased risk of accidents. Furthermore, failures in data communication, resulting from problems such as physical obstacles, interference, network failures and faulty sensors, can generate gaps in information transmission, adversely impacting management solutions. In this context, a proposal for an intersection management system is presented, aiming to improve the safety and efficiency of urban traffic for autonomous vehicles. The system's decision-making is based on continuous data communication between vehicles and infrastructure. Based on this data, the system performs a conflict analysis that identifies possible collisions between vehicles and performs dynamic speed adjustment of these vehicles. In order to mitigate the negative effects of missing information, we incorporated missing data imputation methods by using cubic polynomial segmented interpolation (PCHIP), a process we call DAICS. The results indicate that DAICS was stable in all scenarios with data loss, keeping the average total travel time in simulations 68.4\% lower than that of the baseline algorithm, Intersection Management for Autonomous Vehicles (IMAV).