Baiting Luo

Baiting Luo

Publications

You can also find my articles on my Google Scholar profile.

Runtime Safety Assurance of Autonomous Vehicles

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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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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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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Decision Making in Non-Stationary Environment

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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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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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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Securing Connected and Autonomous Vehicles

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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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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