Autonomous Intersection Management (AIM) is of high importance to avoid traffic jams. Intelligent Vehicles (IVs) and cooperative navigation have received recently much attention in this context. More importantly, the local motion planning approaches for IVs may become impracticable due to traffic disturbances. To face this problem, this paper presents a two-layer Decision-Making and Motion Planning (DMMP) framework to perform trajectory-based IVs hierarchical control in multiple intersections. It includes a microscopic car-following model while taking into account the decisions made by the intersection management layer. This layer is ensured via a local supervisor that detects the vehicle flow rate of traffic downstream. Accordingly, the “road-weight” of each lane is designated to alleviate the traffic congestion. Simultaneously, an aggregated velocity is assigned for approaching IVs in the low-level motion planner layer. Hence, lane's priorities are specified by the local supervisor based on a predefined strategy to manage the distributed IVs system. The Probability Collectives (PC) algorithm is also adopted to ensure an optimal collision-free trajectory regarding the aggregated velocity. Simulations including two adjacent unsignalized intersections are presented to validate the DMMP coordination framework. The overall navigation performances and the traffic flow density are remarkably improved by the supervised AIM.


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    Title :

    Hierarchical Control for Trajectory-based Intelligent Navigation in Urban Adjacent Intersections


    Contributors:


    Publication date :

    2021-09-19


    Size :

    996154 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English