Jim Cherian

Jim Cherian
Nanyang Technological University | ntu · School of Computer Engineering

About

10
Publications
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Introduction
Jim Cherian currently works at CETRAN, the Center for Testing and Research on Autonomous Vehicles at Nanyang Technological University, Singapore. He received his PhD from School of Computer Science and Engineering, Nanyang Technological University. Jim currently focus on developing methods to validate the safety of autonomous vehicles through virtual methods (simulations) complemented by physical tests. His research interests include autonomous systems, mobile computing and sensing applications for intelligent vehicular mobility.

Publications

Publications (10)
Preprint
Vehicles with driving automation are increasingly being developed for deployment across the world. However, the onboard sensing and perception capabilities of such automated or autonomous vehicles (AV) may not be sufficient to ensure safety under all scenarios and contexts. Infrastructure-augmented environment perception using roadside infrastructu...
Preprint
In this paper, we present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV), developed as part of the 2021 IEEE Autonomous Test Driving AI Test Challenge. Scenario-based virtual testing aims to construct specific challenges posed for the AV to overcome, albeit in virtual test environments that may not necessarily res...
Conference Paper
Sensing and Perception (S&P) is a crucial component of an autonomous system (such as a robot), especially when deployed in highly dynamic environments where it is required to react to unexpected situations. This is particularly true in case of Autonomous Vehicles (AVs) driving on public roads. However, the current evaluation metrics for perception...
Preprint
Full-text available
Sensing and Perception (S&P) is a crucial component of an autonomous system (such as a robot), especially when deployed in highly dynamic environments where it is required to react to unexpected situations. This is particularly true in case of Autonomous Vehicles (AVs) driving on public roads. However, the current evaluation metrics for perception...
Article
Full-text available
Locating a vehicle indoors (e.g., underground parking garages) has been a difficult problem to tackle, due to the unavailability of GPS and/or WiFi signals. Current GPS-free indoor localization efforts often rely on infrastructure supports such as WiFi or BLE beacons, whereas the smartphone-only proposals mostly require significant data collection...
Conference Paper
Full-text available
Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking due to a common belief that parking garages are capable of delivering real-time occupancy information, we specifically target at (indoor) parking garages as this belief is far from true...
Conference Paper
Full-text available
Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking, we target at (indoor) parking garages where the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are unavailable and counting vehicles by crowdsensing is dif...
Conference Paper
Full-text available
One of the major challenges during the process of extracting information from multiple spatio-temporal data sources of diverse data types is the matching and fusion of extracted knowledge (e.g. interesting nearby events detected from text, estimated density or flow from a set of geo-coded images). In this demonstration, we present PETRINA ("PErsona...

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Projects

Project (1)
Project
Locating human user or objects as well as tracking their motions have been made possible to embedded sensing technologies around us. This project aims to take the advantage of these pervasive sensing technologies so as to implement system prototypes for lightweight localization and tracking purposes.