no code implementations • 14 Aug 2023 • Bilal Thonnam Thodi, Sai Venkata Ramana Ambadipudi, Saif Eddin Jabari
In this framework, an operator is trained to map heterogeneous and sparse traffic input data to the complete macroscopic traffic state in a supervised learning setting.
no code implementations • 24 Mar 2023 • Wenqing Li, Yue Wang, Muhammad Shafique, Saif Eddin Jabari
Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers.
no code implementations • 24 Mar 2023 • Yue Wang, Wending Li, Michail Maniatakos, Saif Eddin Jabari
The effectiveness of the proposed method is verified on a simulated traffic system based on a microscopic traffic simulator, where experimental results showcase that the smoothed traffic controller can neutralize all trigger samples and maintain the performance of relieving traffic congestion
no code implementations • 16 Feb 2023 • Bilal Thonnam Thodi, Sai Venkata Ramana Ambadipudi, Saif Eddin Jabari
We empirically quantify the generalization/out-of-sample error of the $\pi$-FNO solver as a function of input complexity, i. e., the distributions of initial and boundary conditions.
no code implementations • 9 Jan 2023 • Chuhan Yang, Sai Venkata Ramana Ambadipudi, Saif Eddin Jabari
In this work, we propose a neural network based on the ASM which tunes those parameters automatically by learning from sparse data from road sensors.
no code implementations • 17 Mar 2022 • Yue Wang, Wenqing Li, Esha Sarkar, Muhammad Shafique, Michail Maniatakos, Saif Eddin Jabari
Based on our theoretical analysis and experimental results, we demonstrate the effectiveness of PiDAn in defending against backdoor attacks that use different settings of poisoned samples on GTSRB and ILSVRC2012 datasets.
no code implementations • 4 May 2021 • Bilal Thonnam Thodi, Zaid Saeed Khan, Saif Eddin Jabari, Monica Menendez
We present a deep learning method to learn the macroscopic traffic speed dynamics from these space-time visualizations, and demonstrate its application in the framework of traffic state estimation.
no code implementations • 4 Feb 2021 • Bilal Thonnam Thodi, Zaid Saeed Khan, Saif Eddin Jabari, Monica Menendez
The results demonstrate that anisotropic kernels significantly reduce model complexity and model over-fitting, and improve the physical correctness of the estimated speed fields.
no code implementations • 28 Jun 2020 • Li Li, Theodoros Pantelidis, Joseph Y. J. Chow, Saif Eddin Jabari
To overcome this complexity, we employ an online minimum drift plus penalty (MDPP) approach for SAEV systems that (i) does not require a priori knowledge of customer arrival rates to the different parts of the system (i. e. it is practical from a real-world deployment perspective), (ii) ensures the stability of customer waiting times, (iii) ensures that the deviation of dispatch costs from a desirable dispatch cost can be controlled, and (iv) has a computational time-complexity that allows for real-time implementation.
no code implementations • 22 Jun 2020 • Wenqing Li, Chuhan Yang, Saif Eddin Jabari
The formulation allows us to capture both nonlinear dependencies between forecasting inputs and outputs but also allows us to capture dependencies among the inputs.
no code implementations • 17 Mar 2020 • Yue Wang, Esha Sarkar, Wenqing Li, Michail Maniatakos, Saif Eddin Jabari
We develop a trigger design methodology that is based on well-established principles of traffic physics.
1 code implementation • 21 Jan 2020 • Ouafa Benkraouda, Bilal Thonnam Thodi, Hwasoo Yeo, Monica Menendez, Saif Eddin Jabari
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information.
no code implementations • 8 Jan 2020 • Wenqing Li, Chuhan Yang, Saif Eddin Jabari
This paper addresses the problem of short-term traffic prediction for signalized traffic operations management.
no code implementations • 22 Jun 2018 • Saif Eddin Jabari, Deepthi Mary Dilip, DianChao Lin, Bilal Thonnam Thodi
This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories.
no code implementations • 22 Apr 2018 • Saif Eddin Jabari, Nikolaos M. Freris, Deepthi Mary Dilip
The second shortcoming is the wide-spread use of Gaussian probability densities as mixture components.