arXiv subjects

Jelena Trisovic

Publications and source records attributed to Jelena Trisovic.

3 recordsLinked to original sources

arXiv2026

Eigenvalues as a Metric for Memory Dynamics in Sequence Models

While softmax attention drives state-of-the-art performance in sequence modeling, its quadratic complexity motivates linear alternatives such as state space models (SSMs). Structural differences between the two model classes, however, hinder direct comparisons of their memory dynamics, creating the need for a common metric to analyze, interpret, and improve their information processing capabilities. Inspired by recent advances in SSM performance driven by eigenvalue-guided insights, we leverage the dynamical systems framework to bring attention models into a unified analytical framework with SSMs. This allows us to perform a structured analysis, which investigates the applicability of an eigenvalue-spectrum memory dynamics metric to attention models. To this end, we first conduct an extensive empirical study across diverse attention-based models and SSMs on a range of benchmarks. We show that, for both model classes, eigenvalues influence key aspects of memory and long-range dependency modeling, revealing spectral signatures that align with task requirements. Building on these findings, we show how spectral signatures can motivate architectural modifications, how they can be guided through the training process, and how they can provide information about feature importance. The results thereby enable and emphasize the role of eigenvalue analysis as a principled metric for interpreting, explaining, and ultimately improving the capabilities of sequence models.

cs.LG↗

arXiv2026

Predictive Control for Driving under Uncertain Road Geometry Estimated from Onboard Vision

Autonomous vehicles driving on unknown roads must estimate the road geometry from onboard sensors and follow the resulting reference while respecting road boundaries. When the perception-induced estimation error is non-negligible relative to the lateral constraint margins, treating the estimated reference as ground-truth can lead to safety constraint violations. This paper proposes a perception-based control framework that integrates road geometry estimation and the resulting geometric uncertainty with constrained control. A parametric model of the road curvature is identified from RGB-D measurements via constrained nonlinear optimization, enforcing geometric consistency. The residual perception uncertainty is then captured by constructing a set of curvature profiles consistent with measurements by perturbing the estimated parameters. Using the Frenet-frame vehicle model, the curvature uncertainty is propagated through the model via a scenario model predictive control scheme as parametric model uncertainty, thus enforcing constraints across all sampled curvature realizations. The curvature estimation module is evaluated both in high-fidelity simulation and on real-world image data, confirming reliable operation under realistic visual and depth noise. The full perception-to-control pipeline is validated in simulation, demonstrating that the uncertainty-aware controller maintains tighter adherence to road boundaries compared to its nominal counterpart.

eess.SY↗

arXiv2024

Moving Horizon Estimation for Simultaneous Localization and Mapping with Robust Estimation Error Bounds

This paper presents a robust moving horizon estimation (MHE) approach with provable estimation error bounds for solving the simultaneous localization and mapping (SLAM) problem. We derive sufficient conditions to guarantee robust stability in ego-state estimates and bounded errors in landmark position estimates, even under limited landmark visibility which directly affects overall system detectability. This is achieved by decoupling the MHE updates for the ego-state and landmark positions, enabling individual landmark updates only when the required detectability conditions are met. The decoupled MHE structure also allows for parallelization of landmark updates, improving computational efficiency. We discuss the key assumptions, including ego-state detectability and Lipschitz continuity of the landmark measurement model, with respect to typical SLAM sensor configurations, and introduce a streamlined method for the range measurement model. Simulation results validate the considered method, highlighting its efficacy and robustness to noise.

eess.SY↗