arXiv subjects

Supriya Sarker

Publications and source records attributed to Supriya Sarker.

8 recordsLinked to original sources

arXiv2025

A Comprehensive Review on Traffic Datasets and Simulators for Autonomous Vehicles

Autonomous driving has rapidly evolved through synergistic developments in hardware and artificial intelligence. This comprehensive review investigates traffic datasets and simulators as dual pillars supporting autonomous vehicle (AV) development. Unlike prior surveys that examine these resources independently, we present an integrated analysis spanning the entire AV pipeline-perception, localization, prediction, planning, and control. We evaluate annotation practices and quality metrics while examining how geographic diversity and environmental conditions affect system reliability. Our analysis includes detailed characterizations of datasets organized by functional domains and an in-depth examination of traffic simulators categorized by their specialized contributions to research and development. The paper explores emerging trends, including novel architecture frameworks, multimodal AI integration, and advanced data generation techniques that address critical edge cases. By highlighting the interconnections between real-world data collection and simulation environments, this review offers researchers a roadmap for developing more robust and resilient autonomous systems equipped to handle the diverse challenges encountered in real-world driving environments.

cs.RO↗

arXiv2025

Beacon: A Naturalistic Driving Dataset During Blackouts for Benchmarking Traffic Reconstruction and Control

Extreme weather and infrastructure vulnerabilities pose significant challenges to urban mobility, particularly at intersections where signals become inoperative. To address this growing concern, we introduce Beacon, a naturalistic driving dataset capturing traffic dynamics during blackouts at two major intersections in Memphis, TN, USA. The dataset provides detailed traffic movements, including timesteps, origin, and destination lanes for each vehicle over four hours of peak periods. We analyze traffic demand, vehicle trajectories, and density across different scenarios, demonstrating high-fidelity reconstruction under unsignalized, signalized, and mixed traffic conditions. We find that integrating robot vehicles (RVs) into traffic flow can substantially reduce intersection delays, with wait time improvements of up to 82.6%. However, this enhanced traffic efficiency comes with varying environmental impacts, as decreased vehicle idling may lead to higher overall CO2 emissions. To the best of our knowledge, Beacon is the first publicly available traffic dataset for naturalistic driving behaviors during blackouts at intersections.

cs.RO↗

arXiv2023

Traffic Reconstruction and Analysis of Natural Driving Behaviors at Unsignalized Intersections

This paper explores the intricacies of traffic behavior at unsignalized intersections through the lens of a novel dataset, combining manual video data labeling and advanced traffic simulation in SUMO. This research involved recording traffic at various unsignalized intersections in Memphis, TN, during different times of the day. After manually labeling video data to capture specific variables, we reconstructed traffic scenarios in the SUMO simulation environment. The output data from these simulations offered a comprehensive analysis, including time-space diagrams for vehicle movement, travel time frequency distributions, and speed-position plots to identify bottleneck points. This approach enhances our understanding of traffic dynamics, providing crucial insights for effective traffic management and infrastructure improvements.

cs.CY↗

arXiv2023

Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections

In this report, we delve into two critical research inquiries. Firstly, we explore the extent to which Reinforcement Learning (RL) agents exhibit multimodal distributions in the context of stop-and-go traffic scenarios. Secondly, we investigate how RL-controlled Robot Vehicles (RVs) effectively navigate their direction and coordinate with other vehicles in complex traffic environments. Our analysis encompasses an examination of multimodality within queue length, outflow, and platoon size distributions for both Robot and Human-driven Vehicles (HVs). Additionally, we assess the Pearson coefficient correlation, shedding light on relationships between queue length and outflow, considering both identical and differing travel directions. Furthermore, we delve into causal inference models, shedding light on the factors influencing queue length across scenarios involving varying travel directions. Through these investigations, this report contributes valuable insights into the behaviors of mixed traffic (RVs and HVs) in traffic management and coordination.

cs.RO↗

arXiv2020

An IoT based Real-time Low Cost Smart Energy Meter Monitoring System using Android Application

Nowadays IoT based applications are becoming more popular because it provides efficient solutions for many real time problems. In this paper, an IoT based electric meter monitoring system using android application has been proposed that aims to reduce manual efforts for measuring the electricity units and make users concern about the excessive usage of electricity. Aurdino Uno and an optical sensor are used to fetch the pulse of the electric meter. In order to reduce human error and cost in energy consumption, a low cost wireless sensor network is implemented for digital energy meter and a mobile application that automatically capable of interpret the units meter.

eess.SP↗

arXiv2019

An assistive HCI system based on block scanning objects using eye blinks

Human-Computer Interaction (HCI) provides a new communication channel between human and the computer. We develop an assistive system based on block scanning techniques using eye blinks that presents a hands-free interface between human and computer for people with motor impairments. The developed system has been tested by 12 users who performed 10 common in computer tasks using eye blinks with scanning time 1.0 second. The performance of the proposed system has been evaluated by selection time, selection accuracy, false alarm rate and average success rate. The success rate has found 98.1%.

cs.HC↗

arXiv2019

An Approach Towards Intelligent Accident Detection, Location Tracking and Notification System

Advancement in transportation system has boosted speed of our lives. Meantime, road traffic accident is a major global health issue resulting huge loss of lives, properties and valuable time. It is considered as one of the reasons of highest rate of death nowadays. Accident creates catastrophic situation for victims, especially accident occurs in highways imposes great adverse impact on large numbers of victims. In this paper, we develop an intelligent accident detection, location tracking and notification system that detects an accident immediately when it takes place. Global Positioning System (GPS) device finds the exact location of accident. Global System for Mobile (GSM) module sends a notification message including the link of location in the google map to the nearest police control room and hospital so that they can visit the link, find out the shortest route of the accident spot and take initiatives to speed up the rescue process.

cs.OH↗

arXiv2018

Cyberbullying of High School Students in Bangladesh: An Exploratory Study

This study explores the cyberbullying experience of the high school students in Bangladesh. The motivation of the work is to identify the internet usage and online activities that may cause cyberbullying victimization of the students of the age between 13 and 18. The study also investigates cyberbullying prevalence and impacts both as victimization and perpetration perspectives, discusses their reporting practices to parents, school officials, other adults and suggest policies to teach cyber safety strategy and generate awareness among students.

cs.CY↗