UNMANNED GROUND VEHICLES

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PUBLICATIONS

  • N. M. Bunger*, S. Panjwani*, M. Meghjani*, Z. Huang, M. H. Ang Jr., D. Rus, “Context and Orientation Aware Path Tracking”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.

  • Y. Luo*, M. Meghjani*, Q. H. Ho*, D. Hsu, D. Rus, “Interactive Planning for Autonomous Urban Driving in Adversarial Scenarios”, IEEE International Conference on Robotics and Automation (ICRA), 2021. 
    [PDF] [Video]

  • M. Meghjani, Y. Luo, Q. H. Ho, P. Cai, S. Verma, D. Rus, and D. Hsu, “Context and Intention Aware Planning for Urban Driving”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. 

  • H. Guo, Z. Meng, Z. Huang, W. Leong, Z. Chen, M. Meghjani, M. H Ang Jr, D. Rus, “Safe Path Planning with Gaussian Process Regulated Risk Map”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. 

  • M. Meghjani, H. Guo, Z. Meng, S. Hao, F. Mengdan, W. K. Leong, K. Chaudhary, M. H. Ang Jr., D. Rus, “Mobility-on-Demand using Autonomous Vehicles: Systems, Solutions and Challenges”, Cooperative Intelligent Transport Systems: Towards High-Level Automated Driving, The Institution of Engineering and Technology (IET), 2019. 

  • M. Meghjani, Y. H. Eng, S. Verma, D. Rus and M. H. Ang Jr., “Context-Aware Intention and Trajectory Prediction for Urban Driving Environment”, International Symposium on Experimental Robotics (ISER), 2018. 

  • S. Verma, Y. H. Eng, H. X. Kong, H. Andersen, M. Meghjani, W. K. Leong, X. Shen, C. Zhang, M. H. Ang Jr. and D. Rus, “Vehicle Detection, Tracking and Behavior Analysis in Urban Driving Environments using Road Context”, IEEE International Conference on Robotics and Automation (ICRA), 2018. 

  • S. D. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani, Y. H. Eng, D. Rus, and M. H Ang Jr., “Perception, Planning, Control, and Coordination for Autonomous Vehicles”, in Machines, 2017. 

*All authors contributed equally.

mULTI CLASS MOBILITY-ON-DEMAND

This project aims to seamlessly connect first mile to last mile of a passenger journey using multiple modes of transport including (walk, cycle, scooter, drive, ride in public transport). We also address the problem of fleet sizing to trade-off the travel time and capital budget. 

Multi-class mobility-on-demand: https://www.youtube.com/watch?v=1aiEJETbjRs 

Shared autonomous scooters: https://senseable.mit.edu/rebound/ 

  • D. Kondor, X. Zhang, M. Meghjani, P. Santi, J. Zhao, C. Ratti, “Estimating the potential for shared autonomous scooters”, IEEE Transactions on Intelligent Transportation Systems, 2021. 

  • M. Meghjani, S. D. Pendleton, K. Marczuk, Y. H. Eng, X. Shen, D. Rus and M. H. Ang Jr., “Multiclass Fleet Sizing and Mobility-on-Demand Service”, Complex Systems Design & Management (CSD&M), 2018. 

  • M. Meghjani, K. Marczuk, “A Hybrid Approach to Matching Taxis and Customers”, IEEE Region Ten Conference (TENCON) – Technologies for Smart Nation, 2016. 

  • M. Meghjani, S. Manjanna and G. Dudek, “Fast and Efficient Rendezvous in Street Networks”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016. 

  • M. Meghjani and G. Dudek, “Multi-Agent Rendezvous on Street Networks”, IEEE International Conference on Robotics and Automation (ICRA), 2014. 

AUTONOMOUS URBAN DRIVING

This project aims to develop an autonomous driving system for urban environment that detect surrounding vehicles, infer their intents and driving styles to plan the path for an autonomous vehicle. We have successfully validated our planner in simulation and in real-world environments.