1 code implementation • 6 Feb 2025 • Edgar Ramirez-Sanchez, Catherine Tang, Yaosheng Xu, Nrithya Renganathan, Vindula Jayawardana, Zhengbing He, Cathy Wu
Therefore, NeuralMOVES significantly enhances accessibility while maintaining the accuracy of MOVES, simplifying CO2 evaluation for transportation analyses and enabling real-time, microscopic applications across diverse scenarios without reliance on complex software or extensive computational resources.
1 code implementation • 19 Oct 2024 • Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Zhongxia Yan, Cathy Wu
Despite the popularity of multi-agent reinforcement learning (RL) in simulated and two-player applications, its success in messy real-world applications has been limited.
no code implementations • 10 Oct 2024 • Sirui Li, Janardhan Kulkarni, Ishai Menache, Cathy Wu, Beibin Li
To address this shortcoming, we take a foundation model training approach, where we train a single deep learning model on a diverse set of MILP problems to generalize across problem classes.
no code implementations • 21 Sep 2024 • Yuxuan Zhu, Shiyi Wang, Wenqing Zhong, Nianchen Shen, Yunqi Li, Siqi Wang, Zhiheng Li, Cathy Wu, Zhengbing He, Li Li
We further analyze the potential limitations and challenges that LLMs may encounter in promoting the development of AD technology.
no code implementations • 14 Sep 2024 • Cameron Hickert, Zhongxia Yan, Cathy Wu
Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities.
no code implementations • 5 Sep 2024 • Han Zheng, Zhongxia Yan, Cathy Wu
In the evolving landscape of urban mobility, the prospective integration of Connected and Automated Vehicles (CAVs) with Human-Driven Vehicles (HDVs) presents a complex array of challenges and opportunities for autonomous driving systems.
no code implementations • 15 Aug 2024 • Tianyue Zhou, Jung-Hoon Cho, Cathy Wu
It is thus crucial in cyber-physical recommendation systems to operate with an interaction model that is aware of such user behavior, lest the user abandon the recommendations altogether.
no code implementations • 10 Aug 2024 • Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Edgar Sanchez, Catherine Tang, Mark Taylor, Blaine Leonard, Cathy Wu
The sheer scale and diversity of transportation make it a formidable sector to decarbonize.
1 code implementation • 8 Aug 2024 • Jung-Hoon Cho, Vindula Jayawardana, Sirui Li, Cathy Wu
Deep reinforcement learning (RL) is a powerful approach to complex decision making.
no code implementations • 30 Jun 2024 • Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, Katherine Driggs-Campbell
Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers.
no code implementations • 7 Mar 2024 • Vindula Jayawardana, Sirui Li, Cathy Wu, Yashar Farid, Kentaro Oguchi
To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning.
no code implementations • 1 Feb 2024 • Zhongxia Yan, Han Zheng, Cathy Wu
Anticipating possible future deployment of connected and automated vehicles (CAVs), cooperative autonomous driving at intersections has been studied by many works in control theory and intelligent transportation across decades.
no code implementations • 26 Jan 2024 • Tianyue Zhou, Jung-Hoon Cho, Babak Rahimi Ardabili, Hamed Tabkhi, Cathy Wu
To the best of the authors knowledge, this is the first work to analyze the regret of an integrated expert algorithm with k-Means clustering.
no code implementations • 16 Dec 2023 • Dajiang Suo, Vindula Jayawardana, Cathy Wu
To overcome these challenges and enhance real-world applicability in near-term, we propose a model-free approach employing deep reinforcement learning (DRL) for designing CAV control strategies, showing its reduced overhead in designing and greater scalability and performance compared to model-based methods.
no code implementations • 5 Dec 2023 • Edgar Ramirez Sanchez, Shreyaa Raghavan, Cathy Wu
Identifying stop-and-go events (SAGs) in traffic flow presents an important avenue for advancing data-driven research for climate change mitigation and sustainability, owing to their substantial impact on carbon emissions, travel time, fuel consumption, and roadway safety.
no code implementations • 27 Nov 2023 • Jung-Hoon Cho, Sirui Li, Jeongyun Kim, Cathy Wu
We introduce Temporal Transfer Learning (TTL) algorithms to select source tasks for zero-shot transfer, systematically leveraging the temporal structure to solve the full range of tasks.
no code implementations • 7 Nov 2023 • M. Umar B. Niazi, Jung-Hoon Cho, Munther A. Dahleh, Roy Dong, Cathy Wu
Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks.
no code implementations • 11 Oct 2023 • Sirui Li, Roy Dong, Cathy Wu
Through examining the influence of the lane-switch frequency on the system's stability, the analysis offers a principled explanation to the traffic break phenomena, and further discovers opportunities for less-intrusive traffic smoothing by employing less frequent lane-switching.
no code implementations • 1 Aug 2023 • Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, Katherine Driggs-Campbell
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
3 code implementations • 29 Jun 2023 • Federico Berto, Chuanbo Hua, Junyoung Park, Laurin Luttmann, Yining Ma, Fanchen Bu, Jiarui Wang, Haoran Ye, Minsu Kim, Sanghyeok Choi, Nayeli Gast Zepeda, André Hottung, Jianan Zhou, Jieyi Bi, Yu Hu, Fei Liu, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Davide Angioni, Wouter Kool, Zhiguang Cao, Qingfu Zhang, Joungho Kim, Jie Zhang, Kijung Shin, Cathy Wu, Sungsoo Ahn, Guojie Song, Changhyun Kwon, Kevin Tierney, Lin Xie, Jinkyoo Park
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
no code implementations • 17 Feb 2023 • Aamir Hasan, Neeloy Chakraborty, Cathy Wu, Katherine Driggs-Campbell
The effects of traffic congestion are widespread and are an impedance to everyday life.
no code implementations • 10 Jan 2023 • Sirui Li, Roy Dong, Cathy Wu
While previous theoretical studies consider stability analysis for continuous AV control, this article presents the first integrated theoretical analysis that directly relates the guidance provided to the human drivers to the traffic flow stability outcome.
no code implementations • 16 Oct 2022 • Vindula Jayawardana, Catherine Tang, Sirui Li, Dajiang Suo, Cathy Wu
We show that in comparison to evaluating DRL methods on select MDP instances, evaluating the MDP family often yields a substantially different relative ranking of methods, casting doubt on what methods should be considered state-of-the-art.
1 code implementation • 30 Jul 2022 • Zhongxia Yan, Abdul Rahman Kreidieh, Eugene Vinitsky, Alexandre M. Bayen, Cathy Wu
This is a key challenge to efficient analysis of diverse vehicular and mobility systems.
no code implementations • 26 Apr 2022 • Vindula Jayawardana, Cathy Wu
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions.
no code implementations • 7 Mar 2022 • Dingyi Zhuang, Yuzhu Huang, Vindula Jayawardana, Jinhua Zhao, Dajiang Suo, Cathy Wu
The Braess's Paradox (BP) is the observation that adding one or more roads to the existing road network will counter-intuitively increase traffic congestion and slow down the overall traffic flow.
no code implementations • 14 Dec 2021 • Cameron Hickert, Sirui Li, Cathy Wu
A key takeaway is the potential value of cooperation in enabling the deployment of autonomy at scale.
1 code implementation • 8 Nov 2021 • Zhongxia Yan, Cathy Wu
We propose a model-free reinforcement learning method for controlling mixed autonomy traffic in simulated traffic networks with through-traffic-only two-way and four-way intersections.
no code implementations • 16 Aug 2021 • Sachin Gavali, Chuming Chen, Julie Cowart, Xi Peng, Shanshan Ding, Cathy Wu, Tammy Anderson
Furthermore, we discovered that, as the epidemic has shifted from legal (i. e., prescription opioids) to illegal (e. g., heroin and fentanyl) drugs in recent years, the correlation of environment, crime and health related variables with the opioid risk has increased significantly while the correlation of economic and socio-demographic variables has decreased.
1 code implementation • NeurIPS 2021 • Sirui Li, Zhongxia Yan, Cathy Wu
We frame subproblem selection as regression and train a Transformer on a generated training set of problem instances.
1 code implementation • 9 Mar 2021 • Mengmeng Ma, Jian Ren, Long Zhao, Sergey Tulyakov, Cathy Wu, Xi Peng
A common assumption in multimodal learning is the completeness of training data, i. e., full modalities are available in all training examples.
no code implementations • ICLR 2018 • Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham Kakade, Igor Mordatch, Pieter Abbeel
To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.
16 code implementations • 16 Oct 2017 • Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, Alexandre M. Bayen
Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic - surpassing all known model-based controllers to achieve near-optimal performance - and generalize to out-of-distribution traffic densities.
no code implementations • WS 2017 • Gang Li, Cathy Wu, K. Vijay-Shanker
Distant supervision has been applied to automatically generate labeled data for biomedical relation extraction.
no code implementations • WS 2017 • Samir Gupta, A.S.M. Ashique Mahmood, Karen Ross, Cathy Wu, K. Vijay-Shanker
Comparison sentences are very commonly used by authors in biomedical literature to report results of experiments.