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Lioutas, Vasileios

University of British Columbia

The development of autonomous vehicles requires extensive testing in simulated environments before deployment; however, current simulation approaches often fail to capture the complex, interactive nature of human driving behavior, relying instead on simplified or scripted agent behaviors. This thesis presents novel methods for generating realistic multi-agent driving behavior in simulated environments. We introduce ITRA (Imagining The Road Ahead), a generative model based on recurrent variational neural networks that captures complex spatial and temporal dependencies in driving behavior. To address ITRA's tendency to generate unsafe trajectories, we develop TITRATED, which combines amortized rejection sampling with differentiable infraction losses, and CriticSMC, a novel algorithm that enhances planning efficiency through learned value function heuristics. We then present Control-ITRA, enabling precise control over agent behavior through waypoint specification and target speeds while maintaining behavioral realism. Our extensive experimental evaluation demonstrates that these methods improve the realism and safety of simulated driving behavior while providing flexible control mechanisms for scenario generation, advancing the state-of-the-art in autonomous vehicle simulation and testing.

Text

2025-05-13

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/91124

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/