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This work is devoted to the construction of feedback laws, which guarantee the robust global exponential stability of the uncongested equilibrium point for general discrete-time freeway models. The feedback construction is based on a control Lyapunov function approach and exploits certain important properties of freeway models. The developed feedback laws are tested in simulation, and a detailed comparison is made with an existing feedback law reported in the literature and employed in practice. The robustness properties of the corresponding closed-loop system with respect to measurement errors are also studied.

In this work we consider the problem of path planning for an autonomous vehicle that moves on a freeway. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, we propose the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. We consider driving scenarios where the road is occupied both by autonomous and manual driving vehicles. To the best of our knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, we present some initial results regarding the effect of autonomous vehicles' behavior on the overall traffic flow.