30 citations · 41 across the 19 of their papers we have counts for
20 papers
Encoding Robust Topological Signatures for Hyperdimensional Computing
Arpan Kusari
Hyperdimensional (HD) computing offers an attractive alternative to deep networks for edge learning due to its simplicity, fast prototype-based inference, and compatibility with on…
Embodied AI in Action: Insights from SAE World Congress 2026 on Safety, Trust, Robotics, and Real-World Deployment
Jan-Mou Li, Paul Schmitt, Wei Tong +4
Embodied artificial intelligence is rapidly moving from research into real-world systems such as autonomous vehicles, mobile robots, and industrial machines. As these systems becom…
Evaluating Parametric Car-Following Models in Naturalistic Congestion: Insights in Driver Behavior and Model Limitations
Huaidian Hou, Arpan Kusari, Brian T. W. Lin
Car-Following is a broadly studied state of driving, and many modeling approaches through various heuristics and engineering methods have been proposed. Congestion is a common traf…
Advanced Assistance for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction
Gerui Xu, Boyou Chen, Huizhong Guo +6
Traffic collision reconstruction traditionally relies on human expertise and can be accurate, but pre-crash reconstruction is more challenging. This study develops a multi-agent AI…
Mitigating Context Bias in Domain Adaptation for Object Detection using Mask Pooling
Hojun Son, Asma Almutairi, Arpan Kusari
Context bias refers to the association between the foreground objects and background during the object detection training process. Various methods have been proposed to minimize th…
MatPredict: a dataset and benchmark for learning material properties of diverse indoor objects
Yuzhen Chen, Hojun Son, Arpan Kusari
Determining material properties from camera images can expand the ability to identify complex objects in indoor environments, which is valuable for consumer robotics applications.…
Car-STAGE: Automated framework for large-scale high-dimensional simulated time-series data generation based on user-defined criteria
Asma A. Almutairi, David J. LeBlanc, Arpan Kusari
Generating large-scale sensing datasets through photo-realistic simulation is an important aspect of many robotics applications such as autonomous driving. In this paper, we consid…
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes
Yang Chen, Chih-Li Sung, Arpan Kusari +2
Deep neural networks (DNNs) are often constructed under the closed-world assumption, which may fail to generalize to the out-of-distribution (OOD) data. This leads to DNNs producin…
Quantifying Context Bias in Domain Adaptation for Object Detection
Hojun Son, Asma Almutairi, Arpan Kusari
Domain adaptation for object detection (DAOD) has become essential to counter performance degradation caused by distribution shifts between training and deployment domains. However…
Fractured Glass, Failing Cameras: Simulating Physics-Based Adversarial Samples for Autonomous Driving Systems
Manav Prabhakar, Jwalandhar Girnar, Arpan Kusari
While much research has recently focused on generating physics-based adversarial samples, a critical yet often overlooked category originates from physical failures within on-board…
Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making
Hanxi Wan, Pei Li, Arpan Kusari
With the advent of universal function approximators in the domain of reinforcement learning, the number of practical applications leveraging deep reinforcement learning (DRL) has e…
Object level footprint uncertainty quantification in infrastructure based sensing
Arpan Kusari, Asma Almutairi, Mark E. Gilbert +1
We examine the problem of estimating footprint uncertainty of objects imaged using the infrastructure based camera sensing. A closed form relationship is established between the gr…
Operational requirements for localization in autonomous vehicles
Arpan Kusari, Satabdi Saha
Autonomous vehicles (AVs) need to determine their position and orientation accurately with respect to global coordinate system or local features under different scene geometries, t…
Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology
Arpan Kusari, Wenbo Sun
Subgraph isomorphism, also known as subgraph matching, is typically regarded as an NP-complete problem. This complexity is further compounded in practical applications where edge w…
A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections
Pei Li, Huizhong Guo, Shan Bao +1
Pedestrian safety has become an important research topic among various studies due to the increased number of pedestrian-involved crashes. To evaluate pedestrian safety proactively…
A Novel Traffic Simulation Framework for Testing Autonomous Vehicles Using SUMO and CARLA
Pei Li, Arpan Kusari, David J. LeBlanc
Traffic simulation is an efficient and cost-effective way to test Autonomous Vehicles (AVs) in a complex and dynamic environment. Numerous studies have been conducted for AV evalua…
Enhancing SUMO simulator for simulation based testing and validation of autonomous vehicles
Arpan Kusari, Pei Li, Hanzhi Yang +4
Current autonomous vehicle (AV) simulators are built to provide large-scale testing required to prove capabilities under varied conditions in controlled, repeatable fashion. Howeve…
Assessing and Accelerating Coverage in Deep Reinforcement Learning
Arpan Kusari
Current deep reinforcement learning (DRL) algorithms utilize randomness in simulation environments to assume complete coverage in the state space. However, particularly in high dim…
CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem
Arpan Kusari
Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP). A novel approach using variational…
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
Arpan Kusari, Jonathan P. How
A common approach for defining a reward function for Multi-objective Reinforcement Learning (MORL) problems is the weighted sum of the multiple objectives. The weights are then tre…