Adaptive Cooperative Highway Platooning and Merging

As low-cost reliable sensors are introduced to market, research efforts in autonomous driving are increasing. Traffic congestion is a major problem for nearly all metropolis'. Assistive driving technologies like cruise control and adaptive cruise control are widely available today. While these control systems ease the task of driving, the driver still needs to be fully alert at all times. While these existing structures are helpful in alleviating the stress of driving to a certain extent, they are not enough to improve traffic flow. Two main causes of congestion are slow response of drivers to their surroundings, and situations like highway ramp merges or lane closures. This thesis will address both of these issues. A modified version of the widely available adaptive cruise control systems, known as cooperative adaptive cruise control, can work at all speeds with additional wireless communication that improves stability of the controller. These structures can tolerate much smaller desired spacing and can safely work in stop and go traffic. This thesis proposes a new control structure that combines conventional cooperative adaptive cruise control with rear end collision check. This approach is capable of avoiding rear end collisions with the following car, as long as it can still maintain the safe distance with the preceding vehicle. This control structure is mainly intended for use with partially automated highways, where there is a risk of being rear-ended while following a car with adaptive cruise control. Simulation results also shows that use of bidirectional cooperative adaptive cruise control also helps to strengthen the string stability of the platoon. Two different control structures are used to accomplish this task: MPC and PD based switching controller. Model predictive control (MPC) structure works well for the purpose of bidirectional platoon control. This control structure can adapt to the changes in the plant with the use of a parameter estimator. Constraints are set to make sure that the controller outputs are always within the boundaries of the plant. Also these constraints assures that a certain gap will always be kept with the preceding vehicle. PD based switching controller offers an alternative to the MPC structure. Main advantage of this control structure is that it is designed to be robust to certain level of sensor noise. Both these control structures gave good simulation results. The thesis makes use of the control structures developed in the earlier chapters to continue developing structures to alleviate traffic congestions. Two merging schemes are proposed to find a solution to un-signaled merging and lane closures. First problem deals with situations where necessary levels of communication is not present to inform surrounding drivers of merging intention. Second structure proposes a merging protocol for cases where two platoons are approaching a lane closure. This structure makes use of the modified cooperative adaptive cruise control structures proposed earlier in the thesis

Research on Information Flow Topology for Connected Autonomous Vehicles

Information flow topology plays a crucial role in connected autonomous vehicles (CAVs). It describes how CAVs communicate and exchange information with each other. It predominantly affects the platoon\u27s performance, including the convergence time, robustness, stability, and scalability. It also dramatically affects the controller design of CAVs. Therefore, studying information flow topology is necessary to ensure the platoon\u27s stability and improve its performance. Advanced sliding mode controllers and optimisation strategies for information flow topology are investigated in this project. Firstly, the impact of information flow topology on the platoon is studied regarding tracking ability, fuel economy and driving comfort. A Pareto optimal information flow topology offline searching approach is proposed using a non-dominated sorting genetic algorithm (NSGA-II) to improve the platoon\u27s overall performance while ensuring stability. Secondly, the concept of asymmetric control is introduced in the topological matrix. For a linear CAVs model with time delay, a sliding mode controller is designed to target the platoon\u27s tracking performance. Moreover, the Lyapunov analysis is used via Riccati inequality to guarantee the platoon\u27s internal stability and input-to-output string stability. Then NSGA-II is used to find the homogeneous Pareto optimal asymmetric degree to improve the platoon\u27s performance. A similar approach is designed for a nonlinear CAVs model to find the Pareto heterogeneous asymmetric degree and improve the platoon\u27s performance. Thirdly, switching topology is studied to better deal with the platoon\u27s communication problems. A two-step switching topology framework is introduced. In the first step, an offline Pareto optimal topology search with imperfect communication scenarios is applied. The platoon\u27s performance is optimised using a multi-objective evolutionary algorithm based on decomposition (MOEA/D). In the second step, the optimal topology is switched and selected from among the previously obtained Pareto optimal topology candidates in real-time to minimise the control cost. For a continuous nonlinear heterogeneous platoon with actuator faults, a sliding mode controller with an adaptive mechanism is developed. Then, the Lyapunov approach is applied to the platoon\u27s tracking error dynamics, ensuring the systems uniformly ultimately bounded stability and string stability. For a discrete nonlinear heterogeneous platoon with packet loss, a discrete sliding mode controller with a double power reaching law is designed, and a modified MOEA/D with two opposing adaptive mechanisms is applied in the two-step framework. Simulations verify all the proposed controllers and frameworks, and experiments also test some. The results show the proposed strategy\u27s effectiveness and superiority in optimising the platoon\u27s performance with multiple objectives