| Project Id
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BRJP26100050
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| Project Detail
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| Project Title
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Cooperative Navigation in Mixed Traffic Using Partial V2V/V2I Connectivity
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| Senior Supervision Team (BITS)
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| Supervisor name and Title
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Prof. Maripini Himabindu
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School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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Email ID
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maripini.himabindu@pilani.bits-pilani.ac.in
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| URL for more info
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https://www.bits-pilani.ac.in/pilani/maripini-himabindu/
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| a) Are you currently supervising a BITS or RMIT HDR student?
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NO
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| Please comment how many you are supervising
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| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
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| If yes, please elaborate
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| Senior Supervision Team (RMIT)
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| Supervisor name and Title
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Peter Stasinopoulos
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School or Department (or company, if applicable)
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STEM
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Email ID
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peter.stasinopoulos@rmit.edu.au
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| URL for more info
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https://www.rmit.edu.au/profiles/s/peter-stasinopoulos
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| a) Are you currently supervising a BITS or RMIT HDR student?
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YES
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| Please comment how many you are supervising
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7
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| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
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| If yes, please elaborate
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| Other Supervisors (BITS)
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| Supervisor name and Title
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Dr. Prasanta Kumar Sahu, Associate Professor
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School or Department (or company, if applicable)
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BITS PILANI, HYDERABAD CAMPUS
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| Phone Number (Optional)
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+914066303603
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Email ID
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prasanta.sahu@hyderabad.bits-pilani.ac.in
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| URL for more info
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https://www.prasantsahu.com/
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| Other Supervisors (BITS)
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| Supervisor name and Title
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Nirajan Shiwakoti
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School or Department (or company, if applicable)
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STEM
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| Phone Number (Optional)
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+61399256193
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Email ID
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nirajan.shiwakoti@rmit.edu.au
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| URL for more info
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https://www.rmit.edu.au/profiles/s/nirajan-shiwakoti
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| Field of Research (For Codes)
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| 400512 | Transport engineering | 50.00 |
| 400608 | Wireless communication systems and technologies | 25.00 |
| 460202 | Autonomous agents and multiagent systems | 25.00 |
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| Project Description
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This project proposes a cost-effective cooperative navigation framework for mixed traffic environments where only a small fraction of vehicles are equipped with V2V/V2I communication, yet system-level improvements in safety, mobility, and sustainability are achieved. The study identifies the minimum penetration of connected vehicles needed to influence non-equipped vehicles through cooperative interactions, offering an infrastructure-light pathway toward safer urban mobility. Equipped vehicles act as information leaders, sharing real-time traffic conditions, conflicts, signal timing, and pedestrian movements via V2V and V2I communication. Roadside Units (RSUs) aggregate and rebroadcast this information, enabling a hybrid V2V–V2I ecosystem under limited C-ITS infrastructure and delivering benefits beyond equipped vehicles.
The research develops a two-level cooperative optimisation framework separating short-horizon tactical control from long-horizon strategic guidance. At each time step, connected vehicles exchange position, speed, acceleration, and lane occupancy via V2V communication, while signal phase and timing (SPaT) and intersection state information are obtained from road side units through V2I communication. This shared situational awareness supports informed navigation in heterogeneous traffic with partial connectivity. The framework is evaluated across urban scenarios, including conflict avoidance, pedestrian safety, and signalised intersection control using surrogate safety measures (TTC, PET), mobility indicators (delay, queue length), and sustainability metrics (fuel consumption and emissions).
The objectives are to: (i) develop a realistic mixed-traffic modeling framework reflecting heterogeneous Indian traffic under varying communication penetration; (ii) identify minimum V2V/V2I penetration thresholds for system-level benefits and analyze information propagation; (iii) design cooperative safety applications for vehicle–vehicle and vehicle–pedestrian conflict mitigation and assess V2I-enabled signalized intersection control for speed harmonization and stop avoidance; (iv) compare V2V-only, V2I-only, and hybrid communication architectures and develop deployment guidelines and policy insights for phased C-ITS implementation in developing-country contexts.
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| Project Deliverable/Outcomes
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1) A validated cooperative navigation framework that enhances safety and efficiency in mixed traffic with both connected and non-connected road users. The framework will integrate predictive trajectory modeling, decentralized decision-making, and fail-safe mechanisms to ensure safety and operational continuity under partial connectivity. It can serve as a scalable prototype for corridor-level C-ITS deployment in smart cities.
2) Clear identification of minimum connectivity levels needed to achieve system-wide improvements in safety, mobility and traffic stability, contributing to SDG 9 (Industry, Innovation and Infrastructure) and SDG 11.2 (Safe, affordable, and sustainable transport systems). The end product would be an investment decision-support tool that guides in phased infrastructure deployment.
3) Demonstrated reductions in vehicle-to-vehicle and vehicle-to-pedestrian conflicts at intersections and corridors through cooperative safety applications. The outcome can be translated into a conflict-monitoring dashboard for Traffic Management Centers to enable real-time risk evaluation and proactive intervention.
4) Improved signalized intersection performance via speed harmonization and stop-avoidance strategies, benefiting both connected and surrounding non-connected vehicles, directly supporting
SDG 11 (Sustainable urban mobility).
5) Quantified reductions in delay, stops, fuel consumption, and vehicular emissions, supporting energy-efficient low-emission urban transport and contributing to SDG 7 (Affordable and Clean Energy). An energy-efficient ITS deployment framework will be produced to support low-emission urban transport strategies. This can inform climate action planning and sustainable mobility policies.
6) Context-specific insights into the relative performance of V2V, V2I, and hybrid communication strategies under partial infrastructure deployment. This may lead to a deployable infrastructure decision matrix that can be used by the governments for city-level ITS investments.
7) Practical deployment guidelines and policy insights to support phased, cost-effective C-ITS implementation in Indian cities and similar developing contexts. This deliverable can support integration into Smart City initiatives and national ITS architecture planning frameworks.
Collectively, these outputs will establish methodological and applied advancements in cooperative mobility systems for developing countries.
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| Research Impact Themes
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| SUSTAINABLE DEVELOPMENT AND ENVIRONMENT
| SUSTAINABLE TECHNOLOGIES |
| AI/ML and Data Analytics / Data Science with a focus on applications/translation in | Transportation & Logistics |
| ENHANCED LIVABILITY AND URBAN FUTURES | URBAN ENVIRONMENTS AND SMART CITIES, WATER STEWARDSHIP AND EFFECTIVE WATER USE |
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| Which RMIT Sustainable Development Goal (SDG) does your project align to
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INDUSTRY, INNOVATION, AND INFRASTRUCTURE
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| Which RMIT Enabling Impact Platform (EIP) does your project align to
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URBAN FUTURES
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| Which RMIT Program code will this project sit under?
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DR216P23 PhD (Mech,Manu & Mech Eng)
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| Student Capabilities and Qualifications
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1. Knowledge and experience of working in projects related to Intelligent Transport Systems and V2X communication concepts; 2. Ability to implement mathematical models, optimization routines, or control logic for cooperative vehicle movement through programming languages is essential.
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1. Familiarity with tools such as SUMO, VISSIM, Aimsun, or equivalent platforms, particularly in modeling mixed traffic or signalized intersections; 2. Ability to analyze traffic performance indicators such as delay, stops, emissions, traffic stability, and conflict-based safety metrics.
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MTech
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| Preferred discipline of Student
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| Artificial Intelligence, Deep Learning, Information Extraction & Knowledge Extraction, Machine Learning, Natural Language Processing |
| Computing: Computer Science, Computer System Security, Software Engineering, Cyber Security & Cyber Physical Systems |
| Networks and Communications, Wireless Comms, Telecommunications |
| Robotics, Sensors, Signal Processing, Control Engineering |
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