7 citations · 7 across the 4 of their papers we have counts for
5 papers
Mesoscale Traffic Forecasting for Real-Time Bottleneck and Shockwave Prediction
Raphael Chekroun, Han Wang, Jonathan Lee +4
Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In particular, the CIRCLES consortium project necessitates predictive techniques to m…
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…
MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
Raphael Chekroun, Thomas Gilles, Marin Toromanoff +2
We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent expla…
GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving
Raphael Chekroun, Marin Toromanoff, Sascha Hornauer +1
Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is no…