Abstract. This article presents a local control approach to linear vehicle platooning. Linear platoon systems are sets of vehicles that use local or global perception capabilities to form a train configuration, without any hard grip element. Public transportation is beginning to interest in platoon systems as a technological base to conceive new services. The main problem related to platoon system's control corresponds with maintaining inter-vehicle distance. In literature, the platoon's geometry control problem is treated according to two approaches: global or local vehicle control. This paper focuses on a local approach which does not require sophisticated sensors and/or costly road equipment. This local control approach intends to obtain very good global matching to arbitrary trajectories, only from local perception which consists in measuring the vectorial distance between a given vehicle and its predecessor. The behavior of each platoon vehicle is determined from a physics inspired multi agent interaction model based on a virtual spring-damper. Furthermore, stability, platoon safety properties are checked using physics and mathematical proofs. Finally, simulation is used to measure trajectory error and inter-vehicle distance evolution.

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Modeling and understanding people's mobility at a temporal and geographical space are very strict requirements for developing better strategies of urban public and private transportation systems as well as establishing improved business techniques. This work proposes a random-search based approach to instantiate statistical indicators through an improved mobility scenario which provides specific information about people attending one or several days for some events. Then, we recreate that scenario with virtual humans, proposing a synthetic and open dataset that matches the original statistical data. The results show the proposed approach is very efficient to model people's mobility, and the generated data has a low error rate compared to the original one.

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.