arXiv subjects

Sibibalan Jeevanandam

Publications and source records attributed to Sibibalan Jeevanandam.

3 recordsLinked to original sources

arXiv2026

A General Set-Based Framework for Cognitive State Estimation: Theory and Application to Conditionally Automated Driving

We present a set-based framework for estimating human cognitive states in human-automation interaction (HAI) contexts. Unlike probabilistic approaches dominant in the HAI literature, our framework treats process and measurement uncertainties as unknown but bounded, avoiding the need for large structured datasets or distributional assumptions on noise. We demonstrate the framework in the context of conditionally automated (SAE Level 3) driving, where we estimate three cognitive states (trust, perceived risk, and workload) that influence human reliance on the automation during a continuous, non-trial-based interaction. We leverage a hybrid dynamical modeling framework to identify individual-specific process and measurement models, systematically estimate noise bounds by enforcing reachset conformance, and identify the subset of cognitive states that influence each individual's reliance on the automation. Set-valued estimates of those states are then produced by fusing binary reliance observations and intermittent, quantized self-reports. The framework is evaluated through an in-person experiment in a medium-fidelity driving simulator with 20 participants. The set-valued estimator achieves at least 75% consistency for most participants during testing, and multi-step-ahead reliance predictions derived from the estimates outperform both an open-loop baseline and a particle filter across all choices of prediction horizons (15, 30, 45, 60 time steps), with the performance gap more evident at longer horizons. The proposed estimation framework can enable automation systems that are continuously aware of, and responsive to, the human driver's state.

eess.SY↗

arXiv2025

A Hybrid Dynamic Model for Predicting Human Cognition and Reliance during Automated Driving

We propose a simple (12 parameter) hybrid dynamic model that simultaneously captures the continuous-valued dynamics of three human cognitive states-trust, perceived risk, and mental workload-as well as discrete transitions in reliance on the automation. The discrete-time dynamic evolution of each cognitive state is modeled using a first-order affine difference equation. Reliance is defined as a single discrete-valued state, whose evolution at each time step depends on the cognitive states satisfying certain threshold conditions. Using data collected from 16 participants, we estimate participant-specific model parameters based on their reliance on the automation and intermittently self-reported cognitive states during a continuous drive in a vehicle simulator. The model can be estimated using a single user's trajectory data (e.g. 8 minutes of driving), making it suitable for online parameter adaptation methods. Our results show that the model fits the observed trajectories well for several participants, with their reliance behavior primarily influenced by trust, perceived risk, or both. Importantly, the model is interpretable, such that the variations in model parameters across participants provide insights into differences in the time scales over which cognitive states evolve, and how these states are influenced by task complexity. Implications on the design of human-centric vehicle automation design are discussed.

eess.SY↗

arXiv2023

Solar Cells, Lambert W and the LogWright Functions

Algorithms that calculate the current-voltage (I-V) characteristics of a solar cell play an important role in processes that aim to improve the efficiency of a solar cell. I-V characteristics can be obtained from different models used to represent the solar cell, and the single diode model is a simple yet accurate model for common field implementations. However, the I-V characteristics are obtained by solving implicit equations, which involve repeated iterations and inherent errors associated with numerical methods used. Some methods use the Lambert W function to get an exact explicit formula, but often causes numerical overflow problems. The present work discusses an algorithm to calculate I-V characteristics using the LogWright function, a transformation of the Lambert W function, which addresses the problem of arithmetic overflow that occurs in the Lambert W implementation. An implementation of this algorithm is presented and compared against other algorithms in the literature. It is observed that in addition to addressing the numerical overflow problem, the algorithm based on the LogWright function offers speed benefits while retaining high precision.

physics.comp-ph↗