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Found 15 papers, 1 papers with code
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
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.
Physical Backdoor Trigger Activation of Autonomous Vehicle using Reachability Analysis
Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers.
Optimal Smoothing Distribution Exploration for Backdoor Neutralization in Deep Learning-based Traffic Systems
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
Learning-based solutions to nonlinear hyperbolic PDEs: Empirical insights on generalization errors
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.
Generalized adaptive smoothing based neural network architecture for traffic state estimation
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.
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks
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.
Learning Traffic Speed Dynamics from Visualizations
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.
Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
The results demonstrate that anisotropic kernels significantly reduce model complexity and model over-fitting, and improve the physical correctness of the estimated speed fields.
A Real-Time Dispatching Strategy for Shared Automated Electric Vehicles with Performance Guarantees
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.
Short-Term Traffic Forecasting Using High-Resolution Traffic Data
The formulation allows us to capture both nonlinear dependencies between forecasting inputs and outputs but also allows us to capture dependencies among the inputs.
Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems
We develop a trigger design methodology that is based on well-established principles of traffic physics.
Traffic Data Imputation using Deep Convolutional Neural Networks
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information.
Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning
This paper addresses the problem of short-term traffic prediction for signalized traffic operations management.
Learning Traffic Flow Dynamics using Random Fields
This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories.
Sparse Travel Time Estimation from Streaming Data
The second shortcoming is the wide-spread use of Gaussian probability densities as mixture components.
