Publications (37)
Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games
Maximilian Balandat, Walid Krichene, Claire Tomlin +1
We study a general version of the adversarial online learning problem. We are given a decision set in a reflexive Banach space and a sequence of reward vectors in…
Enabling Mixed Autonomy Traffic Control
Matthew Nice, Matt Bunting, Alex Richardson +8
We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other no…
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…
On the Approximability of Time Disjoint Walks
Alexandre Bayen, Jesse Goodman, Eugene Vinitsky
We introduce the combinatorial optimization problem Time Disjoint Walks (TDW), which has applications in collision-free routing of discrete objects (e.g., autonomous vehicles) over…
Limitations and Improvements of the Intelligent Driver Model (IDM)
Saleh Albeaik, Alexandre Bayen, Maria Teresa Chiri +7
This contribution analyzes the widely used and well-known "intelligent driver model (briefly IDM), which is a second order car-following model governed by a system of ordinary diff…
Solar phased arrays-based wireless power transfer for commercial airlines can reduce energy costs and carbon emissions in the United States
Tianyi Wang, Yiming Xu, Jiseop Byeon +5
Decarbonizing aviation remains challenging because energy-dense jet fuels dominate beyond short-range operations, while batteries impose severe range and payload penalties. Here we…
A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction
Victor Chan, Qijian Gan, Alexandre Bayen
Accurate and reliable prediction of traffic measurements plays a crucial role in the development of modern intelligent transportation systems. Due to more complex road geometries a…
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
Chao Yu, Akash Velu, Eugene Vinitsky +4
Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent…
Dynamic Lane Allocation in UAM Corridors for Efficient Multimodal Door-to-Door Mobility
Jung Ho Park, Jordan Kam, Vishwanath Bulusu +2
This article presents dynamic directional lane allocation in urban air mobility (UAM) corridors as a discrete-time mixed-integer linear program (MILP). This formulation activates,…
Quasi-Dynamic Traffic Assignment using High Performance Computing
Cy Chan, Anu Kuncheria, Bingyu Zhao +5
Traffic assignment methods are some of the key approaches used to model flow patterns that arise in transportation networks. Since static traffic assignment does not have a notion…
Computing the log-determinant of symmetric, diagonally dominant matrices in near-linear time
Timothy Hunter, Ahmed El Alaoui, Alexandre Bayen
We present new algorithms for computing the log-determinant of symmetric, diagonally dominant matrices. Existing algorithms run with cubic complexity with respect to the size of th…
HypNO: A Graph-Based Neural Operator with Physics-Informed Message Passing for Hyperbolic Conservation Laws
Dimitrije Ždrale, Cassie An Jeng, Katie Wang +3
We introduce HypNO, a graph-based neural operator for scalar hyperbolic conservation laws. HypNO operates directly on a space-time graph of finite-volume cells and uses adjacency-f…
Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL
Eugene Vinitsky, Nathan Lichtle, Kanaad Parvate +1
We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the proble…
Reevaluating Policy Gradient Methods for Imperfect-Information Games
Max Rudolph, Nathan Lichtle, Sobhan Mohammadpour +6
In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed nu…
Multi-Adversarial Safety Analysis for Autonomous Vehicles
Gilbert Bahati, Marsalis Gibson, Alexandre Bayen
This work in progress considers reachability-based safety analysis in the domain of autonomous driving in multi-agent systems. We formulate the safety problem for a car following s…
Robust Reinforcement Learning using Adversarial Populations
Eugene Vinitsky, Yuqing Du, Kanaad Parvate +3
Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are…
Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments
Joy Carpio, Sulaiman Almatrudi, Nour Khoudari +5
Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulatio…
So you think you can track?
Derek Gloudemans, Gergely Zachár, Yanbing Wang +10
This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10…
Parallel Network Flow Allocation in Repeated Routing Games via LQR Optimal Control
Marsalis Gibson, Yiling You, Alexandre Bayen
In this article, we study the repeated routing game problem on a parallel network with affine latency functions on each edge. We cast the game setup in a LQR control theoretic fram…
Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models
Qing Lyu, Zhe Fu, Alexandre Bayen
Identifying safety-critical scenarios is essential for autonomous driving, but the rarity of such events makes supervised labeling impractical. Traditional rule-based metrics like…
Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models
Fangyu Wu, Dequan Wang, Minjune Hwang +6
A significant portion of roads, particularly in densely populated developing countries, lacks explicitly defined right-of-way rules. These understructured roads pose substantial ch…
A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles
Nicolas Kardous, Amaury Hayat, Sean T. McQuade +7
In this paper, a multi-lane multi-population microscopic model, which presents stop and go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human…
A unified software framework for solving traffic assignment problems
Juliette Ugirumurera, Gabriel Gomes, Emily Porter +2
We describe a software framework for solving user equilibrium traffic assignment problems. The design is based on the formulation of the problem as a variational inequality. The so…
Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles
Kathy Jang, Eugene Vinitsky, Behdad Chalaki +4
Using deep reinforcement learning, we train control policies for autonomous vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library for deep reinforcement l…
Building-in-Briefcase (BiB)
Kevin Weekly, Ming Jin, Han Zou +3
A building's environment has profound influence on occupant comfort and health. Continuous monitoring of building occupancy and environment is essential to fault detection, intelli…
Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control
Fangyu Wu, Guanhua Wang, Siyuan Zhuang +4
Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, rema…
MOBILITY21: Strategic Investments for Transportation Infrastructure & Technology
Rahul Mangharam, Megan Reyerson, Steve Viscelli +6
America's transportation infrastructure is the backbone of our economy. A strong infrastructure means a strong America - an America that competes globally, supports local and regio…
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis, Natasha Jaques, Eugene Vinitsky +4
A wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of task…
Towards Automated Air Traffic Safety Assessment Around Non-Towered Airports Using Large Language Models
Torsten Darrell, Mahyar Ghazanfari, Jordan Kam +3
We investigate frameworks for post-flight safety analysis at non-towered airports using large language models (LLMs). Non-towered airports rely on the Common Traffic Advisory Frequ…
A Hierarchical MPC Approach to Car-Following via Linearly Constrained Quadratic Programming
Fangyu Wu, Alexandre Bayen
Single-lane car-following is a fundamental task in autonomous driving. A desirable car-following controller should keep a reasonable range of distances to the preceding vehicle and…
The path inference filter: model-based low-latency map matching of probe vehicle data
Timothy Hunter, Pieter Abbeel, Alexandre Bayen
We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduc…
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…
Car-Following Models: A Multidisciplinary Review
Tianya Zhang, Ph. D., Peter J. Jin +3
Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems…
Credit-Based Congestion Pricing: Equilibrium Properties and Optimal Scheme Design
Devansh Jalota, Jessica Lazarus, Alexandre Bayen +1
Credit-based congestion pricing (CBCP) has emerged as a mechanism to alleviate the social inequity concerns of road congestion pricing - a promising strategy for traffic congestion…
Arriving on time: estimating travel time distributions on large-scale road networks
Timothy Hunter, Aude Hofleitner, Jack Reilly +5
Most optimal routing problems focus on minimizing travel time or distance traveled. Oftentimes, a more useful objective is to maximize the probability of on-time arrival, which req…
Anatomy of a Crash
Aude Marzuoli, Emmanuel Boidot, Eric Feron +4
Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transport…
Reinforcement Learning versus PDE Backstepping and PI Control for Congested Freeway Traffic
Huan Yu, Saehong Park, Alexandre Bayen +2
We develop reinforcement learning (RL) boundary controllers to mitigate stop-and-go traffic congestion on a freeway segment. The traffic dynamics of the freeway segment are governe…