Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an Intersection Manager (IM) controls CAVs approaching an intersection in order to maximize intersection capacity while minimizing the CAVs’ gas consumption. Contrary to most of the works in the literature, the CAVs’ location uncertainty is accounted for and CAVs controls are re-optimized periodically, allowing for the optimization of system performance while creating safe trajectories. To improve scalability for high-traffic intersections, an event-triggering approach is also developed (AVOID-EVENT), which reaches a better trade-off among performance and computational and communication complexity. In a realistic simulation scenario, AVOID-EVENT reduces the number of re-optimizations required by 92.2%, while retaining most of the benefits introduced by AVOID-PERIOD.


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    Titel :

    Periodic and Event-Triggering for Joint Capacity Maximization and Safe Intersection Crossing


    Beteiligte:


    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    1255289 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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