Transitions of Control (ToC) play an important role in the simulative impact assessment of automated driving because they may represent major perturbations of smooth and safe traffic operation. The drivers' efforts to take back control from the automation are accompanied by a change of driving behavior and may lead to increased error rates, altered headways, safety critical situations, and, in the case of a failing takeover, even to minimum risk maneuvers. In this work we present modeling approaches for these processes, which have been introduced into SUMO recently in the framework of the TransAID project. Further, we discuss the results of an evaluation of some hierarchical traffic management (TM) procedures devised to ameliorate related disturbances in transition areas, i.e., zones of increased probability for the automation to request a ToC.

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Technological advancements in the field of transportation are gradually enabling cooperative, connected and automated mobility (CCAM). The progress in information and communication technology (ICT) has provided mature solutions for infrastructure-to-vehicle (I2V) communication, which enables the deployment of Cooperative-ITS (C-ITS) services that can foster comfortable, safe, environmentally friendly, and more efficient traffic operations. This study focuses on the enhancement of speed advice comfort and safety in the proximity of signalized intersections, while ensuring energy and traffic efficiency. A detailed microscopic simulation model of an urban network in the city of Thessaloniki, Greece is used as test bed. The performance of dynamic eco-driving is evaluated for different penetration rates of the dynamic eco-driving technology and varying traffic conditions. The simulation analysis indicates that speed advice can be comfortable and safe without adversely impacting energy and traffic efficiency. However, efficient deployment of dynamic eco-driving depends on road design characteristics, activation distance of the service, traffic signal plans, and prevailing traffic conditions.

An important aspect of automated driving is to handle situations where it fails or is not allowed in specific traffic situations. This case study explores means, by which control transitions in a mixed autonomy system can be organized in order to minimize their adverse impact on traffic flow. We assess a number of different approaches for a coordinated management of transitions, covering classic traffic management paradigms and AI-driven controls. We demonstrate that they yield excellent results when compared to a do-nothing scenario. This text further details a model for control transitions that is the basis for the simulation study presented. The results encourage the deployment of reinforcement learning on the control problem for a scenario with mandatory take-over requests.Index Terms-connected automated vehicles (CAV), reinforcement learning (RL), take-over request (ToR), traffic management (TM), transition of control (ToC) NOMENCLATURE AV Automated vehicle CAV Connected automated vehicle CV Connected vehicle LoD Level of demand MDP Markov decision process MRM Minimum risk manoeuvre MV Manual vehicle No-AD No automated driving RL Reinforcement learning RSI Roadside infrastructure TM Traffic management TMC Traffic management center ToC Transition of control ToR Take-over request

Emerging developments in the field of automotive technologies, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems, are expected to enhance traffic efficiency and safety on highways and urban roads. For this reason, substantial effort has been made by researchers to model and simulate these automation systems over the last few years. This study aims to integrate a recently developed car-following model for ACC and CACC equipped vehicles in the microscopic traffic simulation tool SUMO; the implemented ACC/CACC simulation models originate from empirical ones, ensuring the collision-free property in the full-speed-range operation. Simulation experiments for different penetration rates of cooperative automated vehicles, desired time-gap settings and network topologies are conducted to test the validity of the proposed approach and to assess the impact of ACC and CACC equipped vehicles on traffic flow characteristics.