193 citations · 261 across the 11 of their papers we have counts for
12 papers
S2Vec: Self-Supervised Geospatial Embeddings for the Built Environment
Shushman Choudhury, Elad Aharoni, Chandrakumari Suvarna +4
Scalable general-purpose representations of the built environment are crucial for geospatial artificial intelligence applications. This paper introduces S2Vec, a novel self-supervi…
Scalable Learning of Segment-Level Traffic Congestion Functions
Shushman Choudhury, Abdul Rahman Kreidieh, Iveel Tsogsuren +3
We propose and study a data-driven framework for identifying traffic congestion functions (numerical relationships between observations of traffic variables) at global scale and se…
Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs
Jonathan W. Lee, Han Wang, Kathy Jang +61
The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,…
Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic
Han Wang, Zhe Fu, Jonathan Lee +14
This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was valid…
Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments
Zhe Fu, Abdul Rahman Kreidieh, Han Wang +3
Autonomous driving systems present promising methods for congestion mitigation in mixed autonomy traffic control settings. In particular, when coupled with even modest traffic stat…
Learning energy-efficient driving behaviors by imitating experts
Abdul Rahman Kreidieh, Zhe Fu, Alexandre M. Bayen
The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are…
Unified Automatic Control of Vehicular Systems with Reinforcement Learning
Zhongxia Yan, Abdul Rahman Kreidieh, Eugene Vinitsky +2
Emerging vehicular systems with increasing proportions of automated components present opportunities for optimal control to mitigate congestion and increase efficiency. There has b…
Non-local Evasive Overtaking of Downstream Incidents in Distributed Behavior Planning of Connected Vehicles
Abdul Rahman Kreidieh, Yashar Farid, Kentaro Oguchi
The prevalence of high-speed vehicle-to-everything (V2X) communication will likely significantly influence the future of vehicle autonomy. In several autonomous driving application…
Learning Generalizable Multi-Lane Mixed-Autonomy Behaviors in Single Lane Representations of Traffic
Abdul Rahman Kreidieh, Yibo Zhao, Samyak Parajuli +1
Reinforcement learning techniques can provide substantial insights into the desired behaviors of future autonomous driving systems. By optimizing for societal metrics of traffic su…
Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers
Jonathan W. Lee, George Gunter, Rabie Ramadan +25
This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular atten…
Inter-Level Cooperation in Hierarchical Reinforcement Learning
Abdul Rahman Kreidieh, Glen Berseth, Brandon Trabucco +3
Hierarchies of temporally decoupled policies present a promising approach for enabling structured exploration in complex long-term planning problems. To fully achieve this approach…
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
Cathy Wu, Aboudy Kreidieh, Kanaad Parvate +2
The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progres…