Toward an Efficient Ambient Guidance for Transport Applications

We address smart transportation systems whose computational process is based on autonomous and context-aware intelligent agents. As a use case, a pharmacy round tour application is presented, then an original guidance algorithm is proposed to handle the various intentions to be executed during a delivery mission. The efficiency of the algorithm relates to a combined approach involving planning, learning and physical path optimizations. In particular, the past experiences of actions are used to better select the plan which guides the driver contextually. Moreover, the move actions in the plan are concretely refined on shortest physical paths taking into account the spatio-temporal evolution of the driving context.