Larter, S. (2022). A Hierarchical Pedestrian Behaviour Model to Reproduce Realistic Human Behaviour in a Traffic Environment Retrieved from http://hdl.handle.net/10012/18094 (Original work published 2022)
References
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2022
Larter, S., Queiroz, R., Sedwards, S., Sarkar, A., & Czarnecki, K. (2022). A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation Aachen, Germany: IEEE. https://doi.org/10.1109/IV51971.2022.9827035 (Original work published 2022)
Pitropov, M. (2022). LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection Retrieved from http://hdl.handle.net/10012/18062 (Original work published 2022)
Bouchard, F., Sedwards, S., & Czarnecki, K. (2022). A Rule-Based Behaviour Planner for Autonomous Driving Berlin (virtual): Springer.
Sarkar, A., Larson, K., & Czarnecki, K. (2022). Generalized dynamic cognitive hierarchy models for strategic driving behavior. Presented at the Generalized Dynamic Cognitive Hierarchy Models for Strategic Driving Behavior. conference. Retrieved from https://arxiv.org/abs/2109.09861
Nguyen, V. D. (2022). Out-of-Distribution Detection for LiDAR-based 3D Object Detection Retrieved from http://hdl.handle.net/10012/17902 (Original work published 2022)
Kahn, M., Sarkar, A., & Czarnecki, K. (2022). I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames. Presented at the I Know You Can’t See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames. conference. Retrieved from https://arxiv.org/abs/2109.09807
2021
Lee, S., Lee, J., & Hasuo, I. (2021). Predictive PER: Balancing Priority and Diversity Towards Stable Deep Reinforcement Learning Shenzhen, China (virtual): IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534243
Lee, J., & Sutton, R. S. (2021). Policy iterations for reinforcement learning problems in continuous time and space \textemdash Fundamental theory and methods Automatica, 126, 109421, 15 pages. https://doi.org/10.1016/j.automatica.2020.109421 (Original work published 2021)
Lee, J., Sedwards, S., & Czarnecki, K. (2021). Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning Presented at the Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning conference. Online at http://modem2021.cs.nuigalway.ie/. Retrieved from https://arxiv.org/abs/2201.07958