4 citations · 11 across the 13 of their papers we have counts for
16 papers
Geometry vs Structure: Graph-Based Diagnostics for LiDAR Point-Cloud Simulation Fidelity
Ghazal Farhani, Taufiq Rahman
Digital twins provide a scalable and cost-effective complement to real-world testing for validating autonomous-driving and advanced driver-assistance system (ADAS) sensor pipelines…
UDAV: Uncertainty-Driven Adaptive VLM Waypoint Planner
Ghazal Farhani, Shabnam Shabani
Vision-language models (VLMs) can generate routes directly from aerial imagery for off-road navigation, but their predictions provide no indication of reliability. We present UDAV,…
Context-Aware Causal Gaze Forecasting for Human-Vehicle Interaction During In-Cabin Tracking Dropouts
Shabnam Shabani, Ghazal Farhani
Dashboard-mounted gaze trackers often lose sight of the driver's eyes during large head rotations, including shoulder checks, mirror glances, and intersection scanning. These maneu…
Closed Quantum Boltzmann Bridges: Coherent Revivals, Hidden Microstates, and the Emergence of Classical Two-Time Entropy Conditioning
Sina Kazemian, Ghazal Farhani, Younes Javanmard
The classical Boltzmann Bridge describes entropy histories conditioned on both an initial low-entropy macrostate and a later macrostate. Unlike the usual past-only formulation of t…
Bayesian Uncertainty Quantification with Anchored Ensembles for Robust EV Power Consumption Prediction
Ghazal Farhani, Taufiq Rahman, Kieran Humphries
Accurate EV power estimation underpins range prediction and energy management, yet practitioners need both point accuracy and trustworthy uncertainty. We propose an anchored-ensemb…
Comprehensive Assessment of LiDAR Evaluation Metrics: A Comparative Study Using Simulated and Real Data
Syed Mostaquim Ali, Taufiq Rahman, Ghazal Farhani +3
For developing safe Autonomous Driving Systems (ADS), rigorous testing is required before they are deemed safe for road deployments. Since comprehensive conventional physical testi…
An uncertainty-aware physics-informed neural network solution for the Black-Scholes equation: a novel framework for option pricing
Sina Kazemian, Ghazal Farhani, Amirhessam Yazdi
We present an uncertainty-aware, physics-informed neural network (PINN) for option pricing that solves the Black--Scholes (BS) partial differential equation (PDE) as a mesh-free, g…
3D Roadway Scene Object Detection with LIDARs in Snowfall Conditions
Ghazal Farhani, Taufiq Rahman, Syed Mostaquim Ali +4
Because 3D structure of a roadway environment can be characterized directly by a Light Detection and Ranging (LiDAR) sensors, they can be used to obtain exceptional situational awa…
A Comparative Study of Oscillatory Perturbations in Car-Following Models
Oumaima Barhoumi, Ghazal Farhani, Taufiq Rahman +2
As connected and autonomous vehicles become more widespread, platooning has emerged as a key strategy to improve road capacity, reduce fuel consumption, and enhance traffic flow. H…
Weather-Dependent Variations in Driver Gaze Behavior: A Case Study in Rainy Conditions
Ghazal Farhani, Taufiq Rahman, Dominique Charlebois
Rainy weather significantly increases the risk of road accidents due to reduced visibility and vehicle traction. Understanding how experienced drivers adapt their visual perception…
Fuel Consumption in Platoons: A Literature Review
Oumaima Barhoumi, Ghazal Farhani, Taufiq Rahman +3
Platooning has emerged as a promising strategy for improving fuel efficiency in automated vehicle systems, with significant implications for reducing emissions and operational cost…
Deep Learning-Based Analysis of Power Consumption in Gasoline, Electric, and Hybrid Vehicles
Roksana Yahyaabadi, Ghazal Farhani, Taufiq Rahman +3
Accurate power consumption prediction is crucial for improving efficiency and reducing environmental impact, yet traditional methods relying on specialized instruments or rigid phy…
Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations
Nima Hosseini Dashtbayaz, Ghazal Farhani, Boyu Wang +1
The residual loss in Physics-Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, r…
A Clustering Approach for Remotely Sensed Data in the Western United States
Ghazal Farhani
The increasing frequency and scale of wildfires carry significant ecological, socioeconomic, and environmental implications, prompting the need for a deeper grasp of wildfire chara…
A Bayesian Neural Network Approach for Tropospheric Temperature Retrievals from a Lidar Instrument
Ghazal Farhani, Giovanni Martucci, Tyler Roberts +2
We have constructed a Bayesian neural network able of retrieving tropospheric temperature profiles from rotational Raman-scatter measurements of nitrogen and oxygen and applied it…
Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks
Ghazal Farhani, Alexander Kazachek, Boyu Wang
Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs). However, they of…