Trajectory Planning and Routing for Autonomous Vehicles

Our research explores how autonomous and connected vehicles can make intelligent, coordinated decisions about where and how to move in complex transportation systems. At a high level, we focus on developing mathematical models, machine learning methods, and control strategies that enable vehicles to plan safe and efficient trajectories while interacting with other agents and traffic infrastructure. By combining ideas from operations research, artificial intelligence, and transportation engineering, we aim to ensure that vehicles can anticipate the behavior of surrounding agents, adapt to real-time conditions, and cooperate with one another to improve traffic flow, energy efficiency, and safety.

Key publications Example projects

Illustration of autonomous vehicle trajectory planning

Technical approach

Trajectory optimization, Reinforcement learning, Graph-based modeling, Markov decision processes, Probabilistic prediction, Deep learning for behavior and trajectory forecasting, Robust and stochastic control, Multi-agent coordination, and Model-predictive control.

Example projects

Center for Smart Vehicles in a Smart World: Cooperative Planning
CPS: Small: Behaviorally Compatible, Energy Efficient, and Network-Aware Vehicle Platooning Using Connected Vehicle Technology

Key publications

Zhao, S., Zhang, J., Masoud, N., Jiang, Y., Huang, H., & Liu, T. (2025). Drift cornering control and real-vehicle deployment for electric vehicles . IEEE Transactions on Industrial Electronics, (2025).
Zhao, S., Zhang, J., Masoud, N., Jiang, Y., Huang, H., & Liu, T. (2024). High-speed cornering control and real-vehicle deployment for autonomous electric vehicles . https://arxiv.org/abs/2411.11762.
Yang, Z., Zhang, R., Pandey, G., Masoud, N., & Liu, H. X. (2023). A hierarchical vehicle behavior prediction framework with traffic signals and interactive agents . IEEE Transactions on Intelligent Transportation Systems, 24(10), 11066-11079.
Zhang, E., Zhang, R., & Masoud, N. (2023). Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning . Transportation Research Part C: Emerging Technologies, 149, 104063.
Zhang, R., Masoud, S., & Masoud, N. (2023). Impact of autonomous vehicles on the car-following behavior of human drivers . Journal of Transportation Engineering, Part A: Systems, 149(3), 04022152.
Zhang, E., Masoud, N., Bandegi, M., Lull, J., & Malhan, R. K. (2022). Step attention: Sequential pedestrian trajectory prediction . IEEE Sensors Journal, 22(8), 8071-8083.
Liu, X., Zhao, G., Masoud, N., & Zhu, Q. (2021). Trajectory planning for connected and automated vehicles: cruising, lane changing, and platooning . SAE International Journal of Connected and Automated Vehicles, 4(4), 315-333.
Liu, X., Masoud, N., Zhu, Q., & Khojandi, A. (2022). A Markov decision process framework to incorporate network-level data in motion planning for connected and automated vehicles . Transportation Research Part C: Emerging Technologies, 136, 103550.
Zhang, E., Pizzi, S., & Masoud, N. (2021). A learning-based method for predicting heterogeneous traffic agent trajectories: Implications for transfer learning . In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 1853-1858. IEEE.
Zhang, E., & Masoud, N. (2020). V2xsim: A V2X simulator for connected and automated vehicle environment simulation . In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 1-6. IEEE.
Abdolmaleki, M., Shahabi, M., Yin, Y., & Masoud, N. (2021). Itinerary planning for cooperative truck platooning . Transportation Research Part B: Methodological, 153, 91-110.