Search Results for author: Cathy Wu

Found 36 papers, 9 papers with code

NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning

1 code implementation6 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.

IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning

1 code implementation19 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.

Benchmarking Multi-agent Reinforcement Learning +3

Towards Foundation Models for Mixed Integer Linear Programming

no code implementations10 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.

A Survey on Large Language Model-empowered Autonomous Driving

no code implementations21 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.

Autonomous Driving Language Modeling +3

Multi-agent Path Finding for Mixed Autonomy Traffic Coordination

no code implementations5 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.

Autonomous Driving Multi-Agent Path Finding

The Nah Bandit: Modeling User Non-compliance in Recommendation Systems

no code implementations15 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.

Recommendation Systems

Cooperative Advisory Residual Policies for Congestion Mitigation

no code implementations30 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.

Autonomous Vehicles

Generalizing Cooperative Eco-driving via Multi-residual Task Learning

no code implementations7 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.

Autonomous Driving Deep Reinforcement Learning +1

Multi-agent Path Finding for Cooperative Autonomous Driving

no code implementations1 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.

Autonomous Driving Multi-Agent Path Finding +1

Expert with Clustering: Hierarchical Online Preference Learning Framework

no code implementations26 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.

Clustering

Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective

no code implementations16 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.

Deep Reinforcement Learning

Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion

no code implementations5 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.

Decision Making

Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy

no code implementations27 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.

Deep Reinforcement Learning Reinforcement Learning (RL) +1

Incentive Design for Eco-driving in Urban Transportation Networks

no code implementations7 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.

Hybrid System Stability Analysis of Multi-Lane Mixed-Autonomy Traffic

no code implementations11 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.

Autonomous Vehicles

PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems

no code implementations1 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.

Towards Co-operative Congestion Mitigation

no code implementations17 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.

Integrated Analysis of Coarse-Grained Guidance for Traffic Flow Stability

no code implementations10 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.

Autonomous Vehicles

The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

no code implementations16 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.

Decision Making Deep Reinforcement Learning +3

Learning Eco-Driving Strategies at Signalized Intersections

no code implementations26 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.

Autonomous Vehicles Reinforcement Learning (RL)

The Braess Paradox in Dynamic Traffic

no code implementations7 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.

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

no code implementations14 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.

Autonomous Vehicles

Reinforcement Learning for Mixed Autonomy Intersections

1 code implementation8 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.

Multi-Task Learning reinforcement-learning +2

Understanding the factors driving the opioid epidemic using machine learning

no code implementations16 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.

BIG-bench Machine Learning

Learning to Delegate for Large-scale Vehicle Routing

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.

SMIL: Multimodal Learning with Severely Missing Modality

1 code implementation9 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.

Meta-Learning

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

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.

Deep Reinforcement Learning Policy Gradient Methods +2

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

16 code implementations16 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.

Autonomous Vehicles Deep Reinforcement Learning +1

Identifying Comparative Structures in Biomedical Text

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.

Sentence

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