Decentralized Optimal Control for Multiple Autonomous Vehicles in Traffic Scenarios

Abstract: New transport technologies have the potential to create more efficient modes of transport and transforming cities for the better by improving urban productivity and increasing efficiency of its transport system to move consumers, labor, and freight. Traffic accidents, energy consumption, pollution, congestion, and long commuting times are main concerns and new transport technologies with autonomous vehicles have the potential to be part of the solution to these important challenges. An autonomous, or highly automated car is a vehicle that can operate with little to no human assistance. This technology is not yet generally available, but if fully realized have the potential to fundamentally change the transportation system. The passenger experience will fundamentally change, but there are also possibilities to increase traffic flow, form platoons of transport vehicles to reduce air-drag and thereby energy consumption, and a main challenge is to realize all this in a safe way in uncertain and complex traffic situations on highways and in urban scenarios. The key topic of this dissertation is how optimal control techniques, more specifically Model Predictive Control (MPC), can be applied in autonomous driving in dynamic environments and with dynamic constraints on vehicle behavior. The main problem studied is how to control multiple vehicles in an optimal, safe, and collision free way in complex traffic scenarios, e.g., laneswitching, merging, or intersection situations in the presence of moving obstacles, i.e., other vehicles whose behavior and intent may not be known. Further, the controller needs to take maneuvering capabilities of the vehicle into account, respecting road boundaries, speed limitations, and other traffic rules. Optimization-based techniques for control are interesting candidates for multi-vehicle problems, respecting well-defined rules in traffic while still providing a high degree of decision autonomy to each vehicle. To ensure autonomy, it is studied how to decentralize the control approach to not rely on a centralized computational resource. Different methods and approaches are proposed in the thesis with guaranteed convergence and collision-avoidance features. To reduce the computational complexity of the controller, a Gaussian risk model for collision prediction is integrated and also a technique that combines MPC with learning methods is explored. Main contributions of this dissertation are control methods for autonomous vehicles that provide safety and comfort of passengers even in uncertain traffic situations where the behavior of surrounding vehicles is uncertain, and the methods are computationally fast enough to be used in real time. An important property is that the proposed algorithms are general enough to be used in different traffic scenarios, hence reducing the need for specific solutions for specific situations. 

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