Reinforcement Learning-based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

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

Enhanced Traffic Management Procedures of Connected and Autonomous Vehicles in Transition Areas

In light of the increasing trend towards vehicle connectivity and automation, there will be areas and situations on the roads where high automation can be granted, and others where it is not allowed or not possible. These are termed ‘Transition Areas’. Without proper traffic management, such areas may lead to vehicles issuing take-over requests (TORs), which in turn can trigger transitions of control (ToCs), or even minimum-risk manoeuvres (MRMs). In this respect, the TransAID Horizon 2020 project develops and demonstrates traffic management procedures and protocols to enable smooth coexistence of automated, connected, andconventional vehicles, with the goal of avoiding ToCs and MRMs, or at least postponing/accommodating them. Our simulations confirmed that proper traffic management, taking the traffic mix into account, can prevent drops in traffic efficiency, which in turn leads to a more performant, safer, and cleaner traffic system, when taking the capabilities of connected and autonomous vehicles into account

Control Transitions in Level 3 Automation: Safety Implications in Mixed-Autonomy Traffic

Level 3 automated driving systems could introduce challenges to traffic systems as they require a specific lead time in their procedures to ensure the safe return of vehicle control to the driver. These processes, called 'transitions of control', may particularly pose complications in accelerating traffic flows when regulations mandate control transitions due to an operational speed limitation of 60 km/h as established in recent certification processes based on UNECE regulations from 2021. To investigate these concerns, we conducted a comprehensive simulation study to examine potential safety implications arising from control transitions within mixed-autonomy traffic. The simulation results indicate adverse safety impacts due to increased safety-relevant interactions between vehicles caused by transitions of control in dynamic traffic flow conditions. Our findings also reveal that those effects could become stronger once string unstable ACC controllers are deployed as well

Remarks on Traffic Signal Coordination

The co-ordination between traffic signals is assumed to be important for the good organization of a transport system. By using an artificial approach to create and analyze a multitude of transportation systems, a few different simple traffic signals programs has been put to the test and compared to each other. The result is that a well co-ordinated system can be outperformed by a non-coordinated signal set-up, where all signals controlers run in (single intersection) actuated mode. Clearly, these results are preliminary and require more investigation

Traffic Management for Connected and Automated Vehicles on Urban Corridors - Distributing Take-Over Requests and Assigning Safe Spots

In light of the increasing trend towards vehicle connectivity and automation, there will be areas and situations on the roads where high automation can be granted, and others where it is not allowed or not possible. These are termed Transition Areas. Without proper traffic management (TM), such areas may lead to vehicles issuing take-over requests (TORs), which in turn can trigger transitions of control (ToCs), or even minimum-risk manoeuvres (MRMs) where a vehicle can come to a full stop in a safe spot. In this respect, the TransAID Horizon 2020 project develops and demonstrates TM procedures and protocols to enable smooth coexistence of automated, connected, and conventional vehicles, with the goal of avoiding ToCs and MRMs, or at least postponing/accommodating them. This paper investigates how TM can successfully assist connected and automated vehicles (CAVs) when they are approaching no automated driving zones (No-AD zone). Our simulation results indicate that a combined approach for distributing TORs and assigning safe spots significantly improves traffic efficiency and safety for such mixed autonomy situations in urban areas

From Automated to Manual - Modeling Control Transitions with SUMO

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

Infrastructure Support for Cooperative Maneuvers in Connected and Automated Driving

Connected and automated vehicles can exploit V2X communications to coordinate their maneuvers and improve the traffic safety and efficiency. To support such coordination, ETSI is currently defining the Maneuver Coordination Service (MCS). The current approach is based on a distributed solution where vehicles coordinate their maneuvers using V2V (Vehicle-to-Vehicle) communications. This paper proposes to extend this concept by adding the possibility for the infrastructure to support cooperative maneuvers using V2I (Vehicle-to-Infrastructure) communications. To this aim, we propose a Maneuver Coordination Message (MCM) that can be used in cooperative maneuvers with or without road infrastructure support. First results show the gains that cooperative maneuvers can achieve thanks to the infrastructure support. This paper also analyses and discusses the need to define MCM generation rules that decide when MCM messages should be exchanged. These rules have an impact on the effectiveness of cooperative maneuvers and on the operation and scalability of the V2X network