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

TransAID Deliverable 6.2/2 - Assessment of Traffic Management Procedures in Transition Areas

This Deliverable 6.2 of the TransAID project presents and evaluates the simulation results obtained for the scenarios considered during the project's first and second iterations. To this end, driver- and AV-models designed in WP3, traffic management procedures developed in WP4, and V2X communication protocols and models from WP5 were implemented within the iTETRIS simulation framework. Previous main results from Deliverable 4.2, where baseline and traffic management measures without V2X communication were compared, have been confirmed. While not all TransAID scenarios' traffic KPIs were affected, the realistic simulation of V2X communication has shown a discernible impact on some of them, which makes it an indispensable modelling aspect for a realistic performance evaluation of V2X traffic scenarios. Flaws of the first iteration's traffic management algorithms concerning wireless V2X communication and the accompanying possibility of packet loss were identified and have been addressed during the project's second iteration. Finally, lessons learned while working on these simulation results and assessments have additionally been described in the form of recommendations for the real-world prototype to be developed in WP7. We conclude that all results obtained for all scenarios when employing ideal communication confirmed the statistical trends of the results from the original TM scenarios as reported in Deliverable 4.2 where no V2X communication was considered. Furthermore, the performance evaluation of the considered scenarios and parameter combinations has shown the following, which held true in both the first and second iterations: (1) The realistic simulation of V2X communication has an impact on traffic scenarios, which makes them indispensable for a realistic performance evaluation of V2X traffic scenarios. (2) Traffic management algorithms need to account for sporadic packet loss of various message types in some way. (3) Although important, the realistic modelling and simulation of V2X communication also induces a significant computational overhead. Thus, from a general perspective, a trade-off between computation time and degree of realism should be considered

Joint Deployment of Infrastructure-Assisted Traffic Management and Cooperative Driving around Work Zones

Highway work zones can induce significant delays and undermine traffic safety. The recent advent of connected and automated vehicles (CAVs) can pose an additional threat to traffic flow performance and safety around highway work zones. CAVs equipped with low - medium level automation systems that cannot reliably address work zone scenarios under all circumstances could induce control transitions and imminent Minimum Risk Manoeuvers (MRMs) that would result in significant traffic disruption and multiple safety critical events. The latter negative effects could be mitigated via the introduction of highly automated vehicles that could utilize sophisticated infrastructure assistance to traverse highway work zones without disengaging automation systems. This study develops novel and utilizes existing vehicle-driver models to simulate manual driving, mixed traffic and infrastructure-assisted highly automated traffic around highway work zones. Traffic operations are evaluated for the latter fleet mixes and three different traffic demand levels. Simulation results indicate that joint deployment of infrastructure-assisted traffic management and cooperative driving can ensure increased traffic efficiency and safety levels for high traffic intensity in a fully connected and automated road environment

TransAID Deliverable 3.2: Cooperative maneuvring in the presence of hierarchical traffic management (2nd iteration)

This present document is Deliverable D3.2 entitled "Cooperative manoeuvring in the presence of hierarchical traffic management", which was prepared in the context of the WP3 framework of the TransAID project. The scope of this document encompasses the modelling and simulation of cooperative manoeuvring in the context of the microscopic traffic simulation activities conducted within TransAID. Initially, the state of the art in the domain of cooperative manoeuvring is provided and then two different cooperative manoeuvring frameworks are introduced. The first one is a decentralized framework where cooperative manoeuvring is solely based on vehicle-to-vehicle (V2V) communications, while the second one is a centralized framework that utilizes vehicle-toanything (V2X) communications. A work zone scenario is used to elaborate on the operation of the centralized approach. The logic for simulating the decentralized approach in the microscopic traffic simulator SUMO is subsequently introduced along with the corresponding functionalities that were developed within SUMO for this purpose. Cooperative manoeuvring is coupled with hierarchical traffic management by explaining how the decentralized approach can be integrated in the traffic management plans that were developed for each use case examined in the context of TransAID. Cooperative manoeuvring is coupled with traffic separation in SUMO and a timeline of cooperative manoeuvring actions in the simulation is presented. Coupling with communications is also addressed. Moreover, adaptations to the driver-vehicle models encompassing communication requirements are proposed to enable integration in iTETRIS. Finally, recommendations for finetuning of driver-vehicle models in simulation are provided based on the findings of the real-world prototype experiments

TransAID Deliverable 4.3 (second iteration): Translation of traffic management measures to iCS, scale-up, and wider deployment

This deliverable explains how simulations of both the baseline (WP3) and the traffic management schemes (WP4) can be ported from the SUMO simulation environment (with the help of the TraCI interface and Python scripts) to the iCS environment (using the C++ language).We first gave an explanation on how to set up the creation of a traffic management application in the context of the iCS. Details were given on how to prepare the development of an application, based on the source code in the repository. We also explained the interactions between the iCS, SUMO, ns-3, and the various applications, using subscriptions and the exchange of messages.To this end, the TransAID version of the iTETRIS platform defined in WP6 includes a basic application known as baseAppthat manages the exchange of information between the applications and the iCS modules. The application developed for the different services of the TransAID project will inherit form this baseAppand extend the functionality with the traffic management procedures defined in WP4. In order to develop these applications,a new branch (transaid-apps) is added to the git repository. Note that all TransAID applications developed share the same baseApp. Hence, commits to the baseApp should be strictly separated from thecommits to the TransAID applications in development. Changes to the baseApp as well as other iTETRIS modules like iCS or ns-3 should be integrated into the transaid-dev branch. When porting the traffic management code from the WP4 to the WP6 environments, we need to make sure that the same logic is preserved. In order to guarantee this, all applications implemented in the use cases should create test suites, similarly as described in Deliverable D6.1. We use the same testing framework, called TextTest. Tests are created in the transaid-apps branch of the repository, separated for each use case individually. All tests are stored in the transaid/TransAIDScenarios/tests/scenariosfolder. All relevant data pertaining to a specific use case (i.e. SUMO networks, configuration files, ...) are copied to the relevant scenario in the tests folder. Just as before, the testing concept employed by TextTest is to compare expected output of an entire program run with actual output (output files or stdout and stderr). However, here we need to be a bit more careful and considerate of the complexity involved with comparing the various iCS traffic management applications to their previously created SUMO counterparts.We explain this via a method of aggregate quantities, rather than explicitly comparing time-space diagrams.A more detailed comparison of simulation outputs would be to use detector measurements and/or explicit vehicle trajectories, create time-space diagrams from these (of average speeds or flows), and then compare these with each other and define whether or not the deviation is significant. However, even though this type of analysis would certainly allow us to detect deviations in the time-space plane (e.g.,congested areas that may appear/disappear as artefacts, ...), it would be out of scope. In addition, such analyses have not been done widespread before, as they are also difficult to interpret, and still require some aggregation in order to test these 'automatically'

SHOW Deliverable 10.2:Pilot guiding simulation results

This deliverable describes the first pool of simulation results covering nine representative pilot sites of SHOW. The initial aim of this deliverable was to provide the first pool of simulation results fed by pre-demo utilizing input from the pre-demo evaluation round and to revise the data inputs required from SHOW sites during real-life demo activities. Since output data from the pre-demo phase is not yet available, a modified approach was used for the simulations. On the one hand, special attention was paid to ensuring that the simulations were very closely coordinated with the pre-demos. This was achieved by having the same partners from the simulations involved in the pre-demos in all nine selected sites, avoiding communication risks between pre-demos and simulations. Secondly, the exact same data that functioned as input for the pre-demos was used as input for the simulations. These are e.g. HD maps of the surveyed target routes or existing data of the target vehicles from other projects. This enabled very realistic simulations to be achieved, although the output of the pre-demos is still pending and will be incorporated into the follow-up deliverable. From the simulation results for the nine sites (Aachen, Brainport, Graz, Karlsruhe, Linkoping, Madrid, Salzburg, Tampere and Trikala), it was found that the introduction of automated shuttles leads to an increase in delay times due to slow shuttle speeds (e.g. in Trikala, Linköping, Karlsruhe, Brainport), while safety aspects should be taken into account, as shown in Karlsruhe and Tampere. Furthermore, passenger behaviour and comfort are of great importance for the successful introduction of automated services, which was first observed in Graz. However, in Madrid, Trikala and Salzburg it was shown that increasing the penetration rate and the area of operation of the automated shuttle leads to a reduction in delays and travel time as well as an increase in speed. The results presented provide a very good basis for the further expansion of the simulations in the subsequent deliverables and to derive the impact assessment important in WP13. Output from the pre-demos will be added to the simulations in the next iteration that is reported within D10.3 in 2022