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Movement strategies for connected and automated vehicles in lane-free traffic using optimal control

Faros Ioannis

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Summary

In a variety of fields, automation has enhanced production and reduced errors to a large extent. Automation in road vehicle driving using only connected and automated vehicles (CAVs) is expected to provide similar results. However, issues such as road safety, human-inspired driving, and road rules result in reduced road usage. Automated vehicles, aided by vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, have the potential to improve road safety and traffic flow significantly, by being exempted or relaxed from the road rules designed for human drivers.According to a recently proposed novel traffic paradigm, called TrafficFluid, there is no need to duplicate the human lane-based driving task. The future of road driving is moving towards complete automation and cooperation among CAVs. Advanced vehicle sensors and communications enable CAVs to "float" securely and efficiently, restoring lost capacity and enhancing traffic safety on various types of roadways, based on appropriate movement strategies.The research so far, proposes, among other strategies, a strategy based on nonlinear optimal control for CAV path planning in lane-free traffic, as well as a feasible direction algorithm for its computationally efficient numerical solution. The optimal control problem (OCP) considers an objective function that minimizes fuel consumption, improves passenger comfort, reaches desired speed, and avoids obstacles. State-dependent bounds on control input are used to ensure that the vehicles stay within the road boundaries and prevent crashes in emergency situations. The OCP is solved repeatedly for short time horizons within a Model Predictive Control (MPC) framework, while the vehicle advances. The proposed approach is shown to give good results in a traffic simulation with wide range of vehicle densities on a lane-free ring-road and can be considered a contender for use in future advancements linked to lane-free CAV traffic.Towards this direction, in this thesis, a strategy is developed that distributes the vehicles laterally based on their longitudinal desired speed and aims to improve the traffic flow, while avoiding collisions. Firstly, the desired lateral position is selected based on the distribution of the desired longitudinal speeds. Afterward, the desired lateral speed of each vehicle is calculated and applied in real-time, as it depends on its current lateral position. The proposed strategy is being simulated in a lane-free environment using a custom-made extension, namely TrafficFluid-Sim, which is built for the Simulation of Urban MObility (SUMO) simulator, considering various densities for different scenarios in a ring-road for comparative analysis. Several quantities, like the average traffic flow and statistical measures of the error in the lateral direction, are calculated to evaluate the method and confirm its effectiveness.

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