Connected and Automated Vehicles (CAVs)

Connected and automated vehicles (CAVs) provide the most intriguing opportunity for enabling users (including individual vehicles and traffic control centers) to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. Many stakeholders intuitively see the benefits of multiscale vehicle control systems and have started to develop business cases for their respective domains, including the automotive and insurance industries, government and service providers. It seems clear that the availability of vehicle-to-vehicle communication has the potential to reduce traffic accidents and ease congestion by enabling vehicles to more rapidly account for changes in their mutual environment. Likewise, vehicle-to-infrastructure communication, e.g., communication with traffic structures, nearby buildings, and traffic lights, should allow for individual vehicle control systems to account for unpredictable changes in local infrastructure.

In a rapidly urbanizing world, we need to make fundamental transformations in how we use and access transportation. This starts with the observation that the purpose of a transportation system is not mobility but rather accessibility to goods, services, and activities. Mobility is only an unintended outcome of our accessibility needs and may be viewed as an intermediate service (the means) on the way to what we really want: access. Today’s private vehicle mobility paradigm often requires dedicated parking, a need which may fade in shared mobility systems such as car share or ride share. Such systems also become more viable in dense, high demand regions, because the downtime between trips is small and overlapping trips can be shared. Distinguishing between mobility and accessibility is essential in determining the policy issues that need to be resolved and which are closely related to land-use decisions.

We are currently witnessing an increasing integration of our energy, transportation, and cyber networks, which, coupled with the human, or communication network, is giving rise to a new level of complexity in connected communities. Progress in pervasive sensing brings an unprecedented volume of data allowing us to observe, measure, and evaluate the transactions, performance, and efficiency of the critical infrastructures. Recent emergence and explosion of data from citizen sensors via social media and other cyber platforms provides a unique opportunity to understand and anticipate accessibility needs in future connected communities.

A fundamental obstacle in seizing this opportunity is our present lack of understanding of the interactions between vehicles, people, and infrastructure. Developing a novel science and technology is necessary to observe, measure, analyze, and model transportation, using a data-driven understanding of complex connected communities that are governed by both physical and behavioral sciences.

Relevant Publications:

    1. Valencia, A.,  and Malikopoulos, A.A., “On Safety of Passengers Entering a Bus Rapid Transit System from Scheduled Stops,” Proceedings of 7th IEEE Conference on Control Technology and Applications (CCTA), 2023 (to appear).
    2. Mahbub, A M. I., Le, V.-A., and Malikopoulos, A.A., “A Safety-Prioritized Receding Horizon Control Framework for Platoon Formation in a Mixed Traffic Environment,” Automatica, 2023 (pdf).
    3. Bang, H., and Malikopoulos, A.A., “Re-Routing Strategy of Connected and Automated Vehicles Considering Coordination at Intersections,” Proceedings of 2023 American Control Conference, pp. 4419-4424, 2023 (pdf).
    4. Le, V.-A., and Malikopoulos, A.A., “Optimal Weight Adaptation for Model Predictive Control of Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization,” Proceedings of 2023 American Control Conference,  pp. 1183-1188, 2023 (pdf).
    5. Chalaki, B., Beaver, L. E., Mahbub, A M. I., Bang, H., and Malikopoulos, A.A., “A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments”, IEEE Control Systems,  Vol. 42, 6, pp. 20– 34, 2022 (pdfrelevant videos).
    6. Mahbub, A M. I., Chalaki, B., and Malikopoulos, A.A., “A Constrained Optimal Control Framework for Vehicle Platoons with Delayed Communication,” Networks & Heterogeneous Media, Special Issue: Traffic and Autonomy, 2022 (in press, pdf).
    7. Chalaki, B., and Malikopoulos, A.A., “A Barrier-Certified Optimal Coordination Framework for Connected and Automated Vehicles,” Proceedings of 61st IEEE Conference on Decision and Control, pp. 2264-2269, 2022 (pdf).
    8. Bang, H., Chalaki, B., and Malikopoulos, A.A., “Combined Optimal Routing and Coordination of Connected and Automated Vehicles,” Proceedings of 61st IEEE Conference on Decision and Control, 2022 (pdf) — see IEEE Control Systems Letters, 6, pp. 2749 – 2754, 2022.
    9. Le, V.-A., and Malikopoulos, A.A., “A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation,” Proceedings of 61st IEEE Conference on Decision and Control, pp.6272-6277 2022 (pdf). 
    10. Ratnagiri, M., O’Dwyer, C., Beaver, L. E., Bang, H., Chalaki, B., and Malikopoulos, A.A., “A Scalable Last-Mile Delivery Service: From Simulation to Scaled Experiment,” Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, pp. 4163-4168, 2022 (pdf — relevant videos).
    11. Zayas, R., Beaver, L. E., Chalaki, B., Bang, H., and Malikopoulos, A.A., “A Digital Smart City for Emerging Mobility Systems,” Proceedings of the 2nd IEEE conference on Digital Twin and Parallel Intelligence, 2022 – Best Paper Award. (pdf). 
    12. Chalaki, B., and Malikopoulos, A.A., “Time-Optimal Coordination for Connected and Automated Vehicles at Adjacent Intersections,” IEEE Trans. Intell. Transp. Syst., Vol. 23, 8, pp. 13330 – 13345, 2022 (pdfrelevant videos).
    13. Kumaravel, S.D., Malikopoulos, A. A., and Ayyagari, R., “Optimal Coordination of Platoons of Connected and Automated Vehicles at Signal-Free Intersections,” IEEE Trans. Intell. Veh., Vol. 7, 2, pp. 186 – 197, 2022 (pdf).
    14. Mahbub, A M. I., Le, V.-A., and Malikopoulos, A.A., “Safety-Aware and Data-Driven Predictive Control for Connected Automated Vehicles at a Mixed Traffic Signalized Intersection,” Proceedings of the 10th IFAC Symposium: Advances In Automotive Control, pp. 51-56, 2022 (pdf).
    15. Valencia, A., Mahbub, A M. I., and Malikopoulos, A.A., “Performance Analysis of Optimally Coordinated Connected Automated Vehicles in a Mixed Traffic Environment,” Proceedings of the 30th Mediterranean Conference on Control and Automation, pp. 1053-1058, 2022 (pdf).
    16. Bang, H., Chalaki, B., and Malikopoulos, A.A., “Combined Optimal Routing and Coordination of Connected and Automated Vehicles,” IEEE Control Systems Letters, 6, pp. 2749 – 2754, 2022 (pdf).
    17. Nakka, S. K S., Chalaki, B., and Malikopoulos, A.A., “A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways,” Proceedings of 2022 American Control Conference, pp. 3297-3302, 2022 (pdf).
    18. Chalaki, B., and Malikopoulos, A.A., “Robust Learning-Based Trajectory Planning for Emerging Mobility Systems,” Proceedings of 2022 American Control Conference, pp. 2154-2159, 2022 (pdfrelated videos).
    19. Bang, H., and Malikopoulos, A.A., “Congestion-Aware Routing, Rebalancing, and Charging for Shared Autonomous Electric Vehicles,” Proceedings of 2022 American Control Conference, pp. 3152-3157, 2022 (pdf).
    20. Mahbub, A M. I., and Malikopoulos, A.A., “Platoon Formation in a Mixed Traffic Environment: A Model-Agnostic Optimal Control Approach,” Proceedings of 2022 American Control Conference, pp. 4746-4751, 2022 (pdf).
    21. Chalaki, B., and Malikopoulos, A.A., “A Priority-Aware Replanning and Resequencing Framework for Coordination of Connected and Automated Vehicles,” Proceedings of 2022 American Control Conference, pp. 2533-2538, 2022 — see IEEE Control Systems Letters, 6, pp. 1772-1777, 2022 (pdf).
    22. Zhao, L., and Malikopoulos, A.A., “Enhanced Mobility with Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems,” IEEE Intelligent Transportation Systems Magazine, 14, 1, pp. 87 – 102, 2022 (pdf).
    23. Mahbub, A M. I., and Malikopoulos, A.A., “A Platoon Formation Framework in a Mixed Traffic Environment,” Proceedings of the 60th IEEE Conference on Decision and Control, pp. 1935-1940, 2021 — see IEEE Control Systems Letters, 6, 1370-1375, 2022 – IEEE Control Systems Society TC-SC Outstanding Student Paper Prize. (pdfrelevant videos).
    24. Chalaki, B., and Malikopoulos, A.A., “Optimal Control of Connected and Automated Vehicles at Multiple Adjacent Intersections,” IEEE Trans. on Control Systems Tech., Vol. 30, 3, pp. 972-984, 2022 (pdfrelevant videos).
    25. Mahbub, A M. I., and Malikopoulos, A.A., “Conditions to Provable System-Wide Optimal Coordination of Connected and Automated Vehicles,” Automatica, 131, 109751, 2021 (pdf).
    26. Malikopoulos, A.A., Beaver, L.E., and Chremos, I.V., “Optimal Time Trajectory and Coordination for Connected and Automated Vehicles,” Automatica, 125, 109469, 2021 (pdfrelevant videos).
    27. Connor, W.D., Wang, Y., Malikopoulos, A.A., Advani, S.G., and Prasad, A. K., “Impact of Connectivity on Energy Consumption and Battery Life for Electric Vehicles,” IEEE Trans. Intell. Veh., 6, 1, 14–23, 2021 (pdf).
    28. Mahbub, A M. I., and Malikopoulos, A.A., “A Platoon Formation Framework in a Mixed Traffic Environment,” Proceedings of the 60th IEEE Conference on Decision and Control, 2021 (to appear); see IEEE Control Systems Letters, 6, 1370–1375, 2022 – IEEE Control Systems Society TC-SC Outstanding Student Paper Prize.
    29. Chalaki, B., and Malikopoulos, A.A., “A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities,” Proceedings of the 2021 European Control Conference, pp. 16–21, 2021 (pdf).
    30. Kumaravel, S.D., Malikopoulos, A. A., and Ayyagari, R., “Decentralized Cooperative Merging of Platoons of Connected and Automated Vehicles at Highway On-Ramps,” Proceedings of 2021 American Control Conference, pp. 2051–2056, 2021 (pdf).
    31. Mahbub, A M., I., Malikopoulos, A.A., and Zhao, L., “Decentralized Optimal Coordination of Connected and Automated Vehicles for Multiple Traffic Scenarios,” Automatica, 117, 108958, 2020 (pdf).
    32. Beaver, L. E., Chalaki, B., Mahbub, A M., I., Zhao, L., Zayas, R., and Malikopoulos, A.A., “Demonstration of a Time-Efficient Mobility System Using a Scaled Smart City,” Vehicle System Dynamics, 58, 5, 787— 804, 2020 (pdfrelevant videos).
    33. Zhao, L., and Malikopoulos, A.A., “Enhanced Mobility with Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems,” IEEE Intelligent Transportation Systems Magazine, 2020 (pdf).
    34. Chalaki, B., Beaver, L. E., Remer, B., Jang, K., Vinitsky, E., Bayen, A., and Malikopoulos, A.A., “Zero-Shot Autonomous Vehicle Policy Transfer: From Simulation to Real-World via Adversarial Learning,” 16th IEEE International Conference on Control & Automation (IEEE ICCA 2020), 2020 (pdflink to relevant videos) — Best Student Paper (finalist).
    35. Chalaki, B., Beaver, L. E., and Malikopoulos, A.A., “Experimental Validation of a Real-Time Optimal Controller for Coordination of CAVs in a Multi-Lane Roundabout,” Proceedings of IEEE Intelligent Vehicles Symposium– IV2020, 2020 (pdf).
    36. Mahbub, A. and Malikopoulos, A., “Concurrent Optimization of Vehicle Dynamics and Powertrain Operation Using Connectivity and Automation,” SAE Technical Paper 2020-01-0580, 2020, doi:10.4271/2020-01-0580 (link).
    37. Mahbub, A.M., Karri, V., Parikh, D., Jade, S., Malikopoulos, A., “A Decentralized Time- and Energy-Optimal Control Framework for Connected Automated Vehicles: From Simulation to Field Test,” SAE Technical Paper 2020-01-0579, 2020, doi:10.4271/2020-01-0579. (link).
    38. Mahbub, A M., I., Malikopoulos, A.A., and Zhao, L., “Impact of Connected and Automated Vehicles in a Corridor,” Proceedings of 2020 American Control Conference, pp. 1185–1190, 2020 (pdf).
    39. Mahbub, A M., I., and Malikopoulos, A.A., “Conditions for Activating State and Control Constraints in Coordination of Connected and Automated Vehicles,” Proceedings of 2020 American Control Conference, pp. 436–441, 2020 (pdf).
    40. Malikopoulos, A. A., Hong, S., Park, B., Lee, J., and Ryu, S. “Optimal Control for Speed Harmonization of Automated Vehicles,” IEEE Trans. Intell. Transp. Syst., 20, 7, 2405–2417, 2019 (pdf).
    41. Malikopoulos, A.A., and Zhao, L., “Optimal Path Planning for Connected and Automated Vehicles at Urban Intersections,” Proceedings of the 58th IEEE Conference on Decision and Control, 2019 (pdf).
    42. Chalaki, B., and Malikopoulos, A.A., “An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected intersections,” Proceedings of 2019 IEEE Conference on Control Technology and Applications, 2019, pp. 888-893, 2019 (pdf).
    43. Zhao, L., Malikopoulos, A.A., and Rios-Torres, J., “On the Traffic Impacts of Optimally Controlled Connected and Automated Vehicles,” Proceedings of 2019 IEEE Conference on Control Technology and Applications, 2019, pp. 882-887, 2019 (pdf).
    44. Zhao, L., Mahbub, A M., I., and Malikopoulos, A.A., “Optimal Vehicle Dynamics and Powertrain Control for Connected and Automated Vehicles,” Proceedings of 2019 IEEE Conference on Control Technology and Applications, 2019, pp. 33-38, 2019 (pdf).
    45. Malikopoulos, A.A., and Zhao, L., “A Closed-Form Analytical Solution for Optimal Coordination of Connected and Automated Vehicles,” Proceedings of 2019 American Control Conference, 2019, pp. 3599–3604, 2019 (pdf).
    46. Mahbub, A. M., Zhao, L., Assanis, D, D., and Malikopoulos, A.A., “Energy-Optimal Coordination of Connected and Automated Vehicles at Multiple Intersections,” Proceedings of 2019 American Control Conference, 2019, pp. 2664–2669, 2019 (pdf).
    47. Jang, K., Vinitsky, E., Chalaki, B., Remer, B., Beaver, L. E., Malikopoulos, A.A., and Bayen, A., “Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles,” Proceedings of International Conference on Cyber-Physical Systems (ICCPS), pp. 291–300, 2019 (pdflink to relevant videos).
    48. Rios-Torres, J., and Malikopoulos, A.A., “Impact of Partial Penetrations of Connected and Automated Vehicles on Fuel Consumption and Traffic Flow,” IEEE Trans. Intell. Veh., Vol. 3, 4, pp. 453–462, 2018 (pdf).
    49. Malikopoulos, A.A., Cassandras, C.G., and Zhang, Y.Z, “A Decentralized Energy-Optimal Control Framework for Connected Automated Vehicles at Signalized-Free Intersections,” Automatica, 93, 244–256, 2018 (pdf).
    50. Assanis, D, D., Zhao, L., and Malikopoulos, A.A., “Characterization of the new Class of Driving Cycles for Connected and Automated Vehicles,” Proceedings of 2018 IEEE 21st International Conference on Intelligent Transportation Systems, pp. 3668–3673, 2018 (pdf).
    51. Zhao, L., and Malikopoulos, A.A., “Decentralized Optimal Control of Connected and Automated Vehicles in a Corridor,” Proceedings of 2018 IEEE 21st International Conference on Intelligent Transportation Systems, pp. 1252–1257, 2018 (pdf).
    52. Stager, A., Bhan, L., Malikopoulos, A.A., L. Zhao, “A Scaled Smart City for Experimental Validation of Connected and Automated Vehicles”, Proceedings of the 15th IFAC Symposium on Control in Transportation Systems (CTS 2018), pp. 120–135, 2018 (pdf).
    53. Zhao, L., Malikopoulos, A.A., Rios-Torres, J., “Optimal Control of Connected and Automated Vehicles at Roundabouts: An Investigation in a Mixed-Traffic Environment,” Proceedings of the 15th IFAC Symposium on Control in Transportation Systems (CTS 2018), pp. 73–78, 2018 (pdf).
    54. Rios-Torres, J., and Malikopoulos, A.A., “A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps,” IEEE Trans. Intel. Trans. Syst., Vol. 18, 5, pp. 1066-1077, 2017 (pdf).
    55. Rios-Torres, J., and Malikopoulos, A.A., “Automated and Cooperative Vehicle Merging at Highway On-Ramps,” IEEE Trans. Intell. Transp. Syst., Vol. 18, 4, pp. 780-789, 2017 (pdf).
    56. Zhang, Y.Z, Malikopoulos, A.A., and Cassandras, C.G., “Decentralized Optimal Control for Connected and Automated Vehicles at Intersections Including Left and Right Turns,” Proceedings of the 56th IEEE Conference on Decision and Control, 2017 (pdf).
    57. Rios-Torres, J., and Malikopoulos, A.A., “Impact of Connected and Automated Vehicles on Traffic Flow,” Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems, 2017 (pdf).
    58. Zhang, Y.Z, Cassandras, C.G., Malikopoulos, A.A., “Optimal Control of Connected Automated Vehicles at Urban Traffic Intersections: A Feasibility Enforcement Analysis,” Proceedings of the 2017 American Control Conference, pp. 3548-3553, 2017. 2017 (pdf).
    59. Hong, S., Malikopoulos, A. A., Lee, J., and Park, B., “Development and Evaluation of Speed Harmonization using Optimal Control Theory: A Simulation-Based Case Study at a Speed Reduction Zone,” in 96th Annual Meeting Transportation Research Board, 2017 (pdf).
    60. Rios-Torres, J., and Malikopoulos, A.A., “Connected and Automated Vehicle Merging at Highway On- Ramps,” in 96th Annual Meeting Transportation Research Board, 2017 (pdf).
    61. Rios-Torres, J., and Malikopoulos, A.A., “Energy Impact of Different Penetrations of Connected and Automated Vehicles: A Preliminary Assessment,” in Proceedings of 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 2016 (pdf).
    62. Zhang, Y.Z, Malikopoulos, A.A., and Cassandras, C.G., “Optimal Control and Coordination of Connected and Automated Vehicles at Urban Traffic Intersections,” Proceedings of the 2016 American Control Conference, pp. 6227-6232, 2016 (pdf).
    63. Rios-Torres, J., Malikopoulos, A.A., and Pisu, P, “Online Optimal Control of Connected Vehicles for Efficient Traffic Flow at Merging Roads,” Proceedings of 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 2432-2437, 2015 (pdf).