Baiting Luo

Baiting Luo

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publications

Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework

Published in 2021 IEEE Intelligent Vehicles Symposium (IV), 2021

This paper presents an efficient dual cyber-physical blockchain framework for securing connected vehicle applications, addressing challenges in road transportation and intelligent vehicle systems.

Recommended citation: Liu, X., Luo, B., Abdo, A., Abu-Ghazaleh, N., & Zhu, Q. (2021). "Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework." 2021 IEEE Intelligent Vehicles Symposium (IV). 393-400.
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Credibility Enhanced Temporal Graph Convolutional Network Based Sybil Attack Detection On Edge Computing Servers

Published in 2021 IEEE Intelligent Vehicles Symposium (IV), 2021

This paper presents a Credibility Enhanced Temporal Graph Convolutional Network for detecting Sybil attacks on edge computing servers in vehicular ad hoc networks.

Recommended citation: Luo, B., Liu, X., & Zhu, Q. (2021). "Credibility Enhanced Temporal Graph Convolutional Network Based Sybil Attack Detection On Edge Computing Servers." 2021 IEEE Intelligent Vehicles Symposium (IV). 524-531.
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Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems

Published in 2022 IEEE International Conference on Assured Autonomy (ICAA), 2022

This paper presents a risk-aware scene sampling approach for dynamic assurance of autonomous systems, addressing challenges in cyber-physical systems and uncertainty management.

Recommended citation: Ramakrishna, S., Luo, B., Barve, Y., Karsai, G., & Dubey, A. (2022). "Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems." 2022 IEEE International Conference on Assured Autonomy (ICAA). 107-116.
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ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA

Published in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022

This paper presents ANTI-CARLA, an adversarial testing framework for autonomous vehicles using the CARLA simulator.

Recommended citation: Ramakrishna, S., Luo, B., Kuhn, C. B., Karsai, G., & Dubey, A. (2022). "ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA." 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 2620-2627.
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Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems

Published in Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023), 2023

This paper proposes a dynamic simplex strategy with online controller switching logic for balancing safety and performance in autonomous cyber-physical systems.

Recommended citation: Luo, B., Ramakrishna, S., Pettet, A., Kuhn, C., Karsai, G., & Mukhopadhyay, A. (2023). "Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems." Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023). 177-186.
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Adaptive Decision-Making in Non-Stationary Markov Decision Processes

Published in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

This research addresses the challenge of effective decision-making in non-stationary environments, focusing on “anytime” decision-making and adaptive strategies that balance performance optimization and safety prioritization.

Recommended citation: Luo, B. (2024). "Adaptive Decision-Making in Non-Stationary Markov Decision Processes." Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 2755-2757.
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Decision Making in Non-Stationary Environments with Policy-Augmented Search

Published in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

This paper introduces Policy-Augmented Monte Carlo tree search (PA-MCTS), which combines out-of-date policy estimates with online search using up-to-date environment models for decision-making in non-stationary environments.

Recommended citation: Pettet, A., Zhang, Y., Luo, B., Wray, K., Baier, H., Laszka, A., Dubey, A., & Mukhopadhyay, A. (2024). "Decision Making in Non-Stationary Environments with Policy-Augmented Search." Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 2417-2419.
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Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes

Published in Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

This paper presents a heuristic search algorithm called Adaptive Monte Carlo Tree Search (ADA-MCTS) for decision-making in non-stationary Markov decision processes.

Recommended citation: Luo, B., Zhang, Y., Dubey, A., & Mukhopadhyay, A. (2024). "Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes." Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. 1301-1309.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.