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Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections
Motivated by the need to develop simulation tools for verification and v...
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The State of Modeling, Simulation, and Data Utilization within Industry: An Autonomous Vehicles Perspective
The aviation industry has a market driven need to maintain and develop e...
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Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach
Autonomous vehicles (AVs) are inevitably entering our lives with potenti...
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Analytically Modeling Unmanaged Intersections with Microscopic Vehicle Interactions
With the emergence of autonomous vehicles, it is important to understand...
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Effects of Controller Heterogeneity on Autonomous Vehicle Traffic
Interactions between road users are both highly non-linear and profoundl...
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RMT: Rule-based Metamorphic Testing for Autonomous Driving Models
Deep neural network models are widely used for perception and control in...
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Intersection-Traffic Control of Autonomous Vehicles using Newton-Raphson Flows and Barrier Functions
This paper concerns an application of a recently-developed nonlinear tra...
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Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation
For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with human-driven vehicles. The AV planning and control systems need extensive testing, including early-stage testing in simulations where the interactions among autonomous/human-driven vehicles are represented. Motivated by the need for such simulation tools, we propose a game-theoretic approach to modeling vehicle interactions, in particular, for urban traffic environments with unsignalized intersections. We develop traffic models with heterogeneous (in terms of their driving styles) and interactive vehicles based on our proposed approach, and use them for virtual testing, evaluation, and calibration of AV control systems. For illustration, we consider two AV control approaches, analyze their characteristics and performance based on the simulation results with our developed traffic models, and optimize the parameters of one of them.
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