arXiv subjects

Simran Kumari

Publications and source records attributed to Simran Kumari.

5 recordsLinked to original sources

arXiv2025

Distributionally Robust Safe Motion Planning with Contextual Information

We present a distributionally robust approach for collision avoidance by incorporating contextual information. Specifically, we embed the conditional distribution of future trajectory of the obstacle conditioned on the motion of the ego agent in a reproducing kernel Hilbert space (RKHS) via the conditional kernel mean embedding operator. Then, we define an ambiguity set containing all distributions whose embedding in the RKHS is within a certain distance from the empirical estimate of conditional mean embedding learnt from past data. Consequently, a distributionally robust collision avoidance constraint is formulated, and included in the receding horizon based motion planning formulation of the ego agent. Simulation results show that the proposed approach is more successful in avoiding collision compared to approaches that do not include contextual information and/or distributional robustness in their formulation in several challenging scenarios.

cs.RO↗

arXiv2025

Development of a Scaled Setup for Experimental Study of the Effect of Lateral Dynamics on Energy Consumption in Electric Vehicles: An Extension

Most of the existing state-of-the-art approaches for energy consumption analysis do not account for the effect of lateral dynamics on energy consumption in electric vehicles (EVs) during vehicle maneuvers. This paper aims to validate this effect through an experimental study. We develop a scaled model using a radio-controlled (RC) car, modified to achieve dynamic similitude with on-road vehicles, to conduct scaled experiments. The experimental results confirm the impact of lateral dynamics on both energy demand and driving range in electric vehicles, aligning with our previous findings [1], and emphasize the need to incorporate these factors into energy consumption models. This is an extended version of a paper accepted at IEEE ITEC 2025. It includes additional results and analysis.

eess.SY↗

arXiv2023

Energy Consumption of Electric Vehicles: Effect of Lateral Dynamics

Current research on energy related problems such as eco-routing, eco-driving and range prediction for electric vehicles (EVs) primarily considers the effect of longitudinal dynamics on EV energy consumption. However, real-world driving includes longitudinal as well as lateral motion. Therefore, it is important to understand the effects of lateral dynamics on battery energy consumption. This paper conducts an analysis of the stated effect and validates its significance through simulations. Specifically, this study demonstrates that inclusion of the effect of lateral dynamics can improve accuracy and reliability of solutions in eco-routing, eco-driving and range prediction applications.

eess.SY↗

arXiv2023

Data-Driven Robust Optimization for Energy-Aware Safe Motion Planning of Electric Vehicles

In this paper, we simultaneously address the problems of energy optimal and safe motion planning of electric vehicles (EVs) in a data-driven robust optimization framework. Safe maneuvers, especially in urban traffic, are characterized by frequent lateral motions, such as lane changes, overtakes and turning along curved roads. Motivated by our previous work which shows a 3-10 % increase in energy consumption due to lateral motion when an electric vehicle changes its lane once every kilometer while following standard drive cycles, we incorporate vehicle lateral dynamics in the modeling and control synthesis, which is in contrast with most prior works. In the context of safety, we leverage past data of obstacle motion to construct a future occupancy set with probabilistic guarantees, and formulate robust collision avoidance constraints with respect to such an occupancy set using convex programming duality. Consequently, we formulate a finite-horizon optimal control problem subject to robust collision avoidance constraints while penalizing resulting energy consumption, and solve it in a receding horizon fashion. Finally, we show the effectiveness of the proposed approach in reducing energy consumption and collision avoidance via numerical simulations involving curved roads and multiple obstacles. A detailed analysis of energy consumption along different components of EV motion highlights appreciable improvement under the proposed approach.

eess.SY↗

arXiv2020

Prediction of phonon-mediated superconductivity with high critical temperature in the two-dimensional topological semimetal W2N3

Two-dimensional superconductors attract great interest both for their fundamental physics and for their potential applications, especially in the rapidly growing field of quantum computing. Despite intense theoretical and experimental efforts, materials with a reasonably high transition temperature are still rare. Even more rare are those that combine superconductivity with a non-trivial band topology, to potentially host exotic states of matter such as Majorana fermions. Here, we predict a remarkably high superconducting critical temperature of 21-28 K in the easily exfoliable, topologically non-trivial 2D semimetal W2N3 . By studying its electronic and superconducting properties as a function of doping and strain, we find large changes in the electron-phonon interactions that make this material a unique platform to study different coupling regimes and test the limits of current theories of superconductivity. Last, we discuss the possibility of tuning the material to achieve coexistence of superconductivity and topologically non-trivial edge states.

cond-mat.supr-con↗