It also paves the way for the implementation of effective cooperative strategies between vehicles. The automation of driving, which has been a central concern of the automotive industry since the 2000s, is expected to reduce the number of accidents, improve the comfort of users and also reduce the ecological footprint of road traffic in general. Numerous applications have been developed in recent years in these areas, each with the objective of improving the quality of life of users. It is included in the context of intelligent transport systems and the smart city. This thesis addresses the problem of communication in the context of a fleet of autonomous and connected vehicles. The results confirm the robustness of the approach and its ability in damping disturbances and mitigating stop-and-go effects according to the theoretical derivation. The effectiveness of the proposed cooperative strategy in longer queues of vehicles is, instead, investigated through PLEXE, an inter-vehicular communication and mobility simulator that includes features for autonomous vehicles as well as for the realistic emulation of the IEEE 802.11p standard. The performance of the control strategy is disclosed by using hardware-in-the-loop real-time simulation for an exemplary pattern of three vehicles. The asymptotic stability of the algorithm is mathematically proved by leveraging a Lyapunov-Krasovskii functional, while the head-to-tail stability tool is exploited for the tuning of the control gains. The effectiveness of the strategy, and ability to cope with multiple and time-varying delays originated by the non-ideal wireless communication among connected vehicles, is both analytically and numerically analyzed.
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The control protocol, driving the longitudinal motion of the autonomous vehicles, is designed for damping down traffic waves.
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This paper addresses the problem of traffic congestion mitigation in a mixed scenario composed of connected human-driven and autonomous vehicles.
ANGELO COPPOLA FLOWSTATE COLLECTIVE DRIVER
Human driver behavior strongly influences traffic flow by increasing the spread of shock waves in a downstream direction.