Adaptive strategies for platooning

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Abstract

Automated driving, one of the rapidly growing research topics in the field of smart traffic, has proved to be a recognized solution for potentially improving road throughput and reducing vehicles' energy consumption by grouping individual vehicles into platoons controlled by one leading vehicle.

The advances in distributed inter-vehicle communication networks have stimulated a fruitful line of research in Cooperative Adaptive Cruise Control (CACC). In CACC, individual vehicles, grouped into platoons, must automatically adjust their own speed using on-board sensors and communication with the preceding vehicle so as to maintain a safe inter-vehicle distance. The importance of CACC lies in the fact that it enables small inter-vehicle time gaps, which leads to a major reduction of the aerodynamical drag force applied on vehicles in such a driving pattern. Consequently, vehicle emissions, which play a main role in trucks and heavy automobiles, are expected to be highly reduced.

However, a crucial limitation of the state-of-the-art research in this control scheme is that the string stability of the platoon can be proven only when the vehicles in the platoon have identical driveline dynamics and perfect engine performance (homogeneous platoon), and possibly an ideal communication channel.

Thus, the objective of this MSc thesis is to address the problem of CACC for heterogeneous platoons under realistic inter-vehicle network conditions. In the first part, we propose a novel CACC strategy that overcomes the homogeneity assumption and that is able to adapt its action and achieve string stability for uncertain heterogeneous platoons under ideal inter-vehicle network conditions. In the second part, in order to handle the inevitable communication losses, we formulate an extended average dwell-time framework and design an adaptive switched control strategy which activates an augmented CACC or an augmented Adaptive Cruise Control strategy depending on communication reliability.

Stability of the proposed control strategies is proven analytically and simulations are conducted to validate the theoretical analysis.