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Autonomous vehicles that are able to communicate together allowed the emergence of a new way of controlling intersections. Recently an active research community stakes on cooperative intersection management. Vehicles and intersection are able to communicate together in order to improve traffic condition at intersections. Many simulations have shown that the cooperative intersection management outperforms traffic lights because there are no limited stages as within traffic light and authorized movements are adapted to the current situation. This paper focusses on speed synchronization at intersections. More precisely, a virtual platoon is formed by vehicles coming from different lanes. When traffic rate is low this allows avoiding useless stops and thus the speed synchronization saves energy and increases the average speed. However, experiments showed that some parameters need to be considered. Hence, as the traffic rate grows, vehicles need to stop and speed synchronization is no more efficient. The main reason of this drawback is that the studied concepts are limited to First Come First Served or to First Out First-Served. This paper extends the virtual platooning, where new rules form new virtual platoon able to adjust dynamically the whole behavior according to the traffic flow growth under safety conditions. Simulations based on real-world experiences show that the proposed approach is very efficient by increasing the average speed while keeping a high capacity of the intersection.

Among the innovations aimed at tackling the transportation issues in urban areas, one of the most promising solutions is the possibility of making virtual trains of vehicles so as to provide a new kind of transportation system. Even if this kind of solution is now widespread in literature, some difficulties still need to be resolved. For instance, one must find solutions to make the crossing of the train possible while maintaining train composition (trains must not be split) and safety conditions. This paper proposes a multi-level decision process aimed at dealing with this issue. This proposal is based on dynamic adaptation of train parameters which lead to trains crossing without stopping any of them. Results, obtained in simulations, make the comparison with a classical crossing strategy.

Vehicle platoon approaches found in literature deal generally with column formations adapted to urban or highway transportation systems. This paper presents an approach in which each platoon vehicle follows a virtual vehicle, in order to cope with issues such as different platoon geometries. Those different types of formations can be encountered in a wide range of field such as the military or agriculture. A platoon formation is composed of a vehicle which assumes the platoon leader role (generally human driven) and other vehicles which play the follower role. A follower vehicle assigns a local leader role to one of the vehicles it perceives. The approach presented here bases on a predefined translation of position, by a follower vehicle, calculated from the perceived position of its local leader vehicle. This translation depends on the desired platoon geometry, expressed in terms of a predefined longitudinal and lateral distance of a follower vehicle relatively to its local leader position. Each vehicle is implemented as an agent which makes decisions depending only on its own perception.

This work presents an approach to the obstacle avoidance problem, applicable in the frame of driver assistance. A decision, expressed as a proposed acceleration vector for the vehicle, is elaborated from the evaluation of a set of indicators characterizing the global state of a system of reactive agents (RMAS). Those agents evolve in a virtual environment produced on the base of vehicle’s perceptions of the material environment around it. Agent-to-agent and agent-to-environment interactions are defined in order to produce a distribution of agents over the virtual environment. This distribution, taken as the global state of the system, is analyzed by applying a set of indicators inspired from statistical physics, to calculate a new vehicle’s acceleration vector. This work presents the details of the RMAS model and its interaction laws, together with the global state evaluation functions. The approach has been applied to experimentation with a laboratory vehicle. Some experimental results are presented here.