37 citations · 90 across the 19 of their papers we have counts for
19 papers
Designing Rewards for Rewarding Designs: Demonstrating the Impact of Rewards on the Creative Design Process
Surabhi S Nath, Vindula Jayawardana, Monica Van +2
The creative design process involves transforming abstract goals into concrete outcomes through a series of decisions made under constraints. While such processes are commonly shap…
Noise-Aware Generative Microscopic Traffic Simulation
Vindula Jayawardana, Catherine Tang, Junyi Ji +3
Accurately modeling individual vehicle behavior in microscopic traffic simulation remains a key challenge in intelligent transportation systems, as it requires vehicles to realisti…
A Roadmap for Climate-Relevant Robotics Research
Alan Papalia, Charles Dawson, Laurentiu L. Anton +25
Climate change is one of the defining challenges of the 21st century, and many in the robotics community are looking for ways to contribute. This paper presents a roadmap for clima…
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems
Vindula Jayawardana, Sirui Li, Yashar Farid +1
Autonomous vehicles (AVs) are becoming increasingly popular, with their applications now extending beyond just a mode of transportation to serving as mobile actuators of a traffic…
NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning
Edgar Ramirez-Sanchez, Catherine Tang, Yaosheng Xu +4
The transportation sector significantly contributes to greenhouse gas emissions, necessitating accurate emission models to guide mitigation strategies. Despite its field validation…
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
Vindula Jayawardana, Baptiste Freydt, Ao Qu +3
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. A key cha…
Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale
Vindula Jayawardana, Baptiste Freydt, Ao Qu +6
The sheer scale and diversity of transportation make it a formidable sector to decarbonize. Here, we consider an emerging opportunity to reduce carbon emissions: the growing adopti…
Model-Based Transfer Learning for Contextual Reinforcement Learning
Jung-Hoon Cho, Vindula Jayawardana, Sirui Li +1
Deep reinforcement learning (RL) is a powerful approach to complex decision making. However, one issue that limits its practical application is its brittleness, sometimes failing t…
What is a typical signalized intersection in a city? A pipeline for intersection data imputation from OpenStreetMap
Ao Qu, Anirudh Valiveru, Catherine Tang +3
Signalized intersections, arguably the most complicated type of traffic scenario, are essential to urban mobility systems. With recent advancements in intelligent transportation te…
Generalizing Cooperative Eco-driving via Multi-residual Task Learning
Vindula Jayawardana, Sirui Li, Cathy Wu +2
Conventional control, such as model-based control, is commonly utilized in autonomous driving due to its efficiency and reliability. However, real-world autonomous driving contends…
Model-free Learning of Corridor Clearance: A Near-term Deployment Perspective
Dajiang Suo, Vindula Jayawardana, Cathy Wu
An emerging public health application of connected and automated vehicle (CAV) technologies is to reduce response times of emergency medical service (EMS) by indirectly coordinatin…
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
Vindula Jayawardana, Catherine Tang, Sirui Li +2
Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of scientific progress of the field. Beyond designing DRL methods for general intelligence, designing…
Learning Eco-Driving Strategies at Signalized Intersections
Vindula Jayawardana, Cathy Wu
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a l…
The Braess Paradox in Dynamic Traffic
Dingyi Zhuang, Yuzhu Huang, Vindula Jayawardana +3
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 overa…
Fleet management for ride-pooling with meeting points at scale: a case study in the five boroughs of New York City
Motahare Mounesan, Vindula Jayawardana, Yaocheng Wu +2
Introducing meeting points to ride-pooling (RP) services has been shown to increase the satisfaction level of both riders and service providers. Passengers may choose to walk to a…
Legal Document Retrieval using Document Vector Embeddings and Deep Learning
Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva +4
Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. Many researchers have incorporated different te…
Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings
Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva +4
In many modern day systems such as information extraction and knowledge management agents, ontologies play a vital role in maintaining the concept hierarchies of the selected domai…
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity
Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva +4
Semantic similarity measures are an important part in Natural Language Processing tasks. However Semantic similarity measures built for general use do not perform well within speci…
Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings
Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva +3
Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks. Traditionally, the mean or median vector is selected. Ontology classe…