Spiking Neural Network Vs. Transformers in Object Detection in Urban Scenes: A Race for Energy Efficiency

Object detection in city environments is critical for applications such as autonomous driving, intelligent surveillance, and urban planning. As the complexity and scale of these applications grow, so does the demand for computational resources, leading to increased energy consumption. Traditional Transformer-based models, while powerful, are often energy-intensive. On the other hand, Spiking Neural Networks (SNNs), inspired by biological neural processes, promise significant energy savings due to their event-driven nature. This project explores the potential of SNNs as a more energy-efficient alternative to Transformers. The aim is to analyze the comparative performance of Spiking Neural Networks (SNNs) and Transformer-based architectures in object detection within urban scenes. The energy efficiency analysis focuses on determining which neural network paradigm offers superior performance from both (accuracy and liability) while minimizing power consumption. By leveraging neuromorphic hardware and optimizing Transformer models, the research seeks to contribute to developing sustainable and efficient AI systems for real-time urban environment analysis.

Street-Level Proof-of-Location: Turning E-Scooters into Mobile Witnesses in an Urban Mesh

Location data is the backbone of today’s shared mobility services—but GPS alone is vulnerable to spoofing, tampering, and errors. How can we guarantee that an e-scooter was really at a parking bay, at the time a ride started or ended? This project transforms everyday e-scooters into mobile witnesses in a secure urban mesh. Using Raspberry Pi pods, UWB ranging, and BLE Direction Finding, scooters and curbside anchors will generate cryptographically verifiable proofs of location (PoL)—without touching scooter firmware. What You’ll Build? Rugged Raspberry Pi–powered PoL pods mounted on scooters, anchor stations with GNSS time sync and multi-antenna BLE/UWB arrays, a PoL protocol implementation with quorum attestations and transparency logging, and a live field pilot: ride start/end proofs, parking compliance, and anti-theft tracking. This is a hands-on thesis project: you will design hardware, implement secure protocols, and test your system on the streets. Perfect for those passionate about IoT security, smart mobility, and applied cryptography.

Real-Time Temporal Dynamics Modeling with Large Language Models for Predicting Dangerous Situations in Urban Road Scenes

Autonomous vehicles (AVs) rely heavily on perception systems to ensure safe navigation in complex urban environments. While recent advances in multimodal large language models (MLLMs) have demonstrated strong capabilities in scene understanding, reasoning, and providing natural-language explanations, their application to real-time prediction of dangerous or risk-critical situations remains underexplored. Most existing approaches either (i) analyze static frames or short clips offline, (ii) generate risky scenarios for testing, or (iii) provide risk recognition without strict latency guarantees. However, urban driving demands continuous, low-latency anticipation of hazards ? such as a pedestrian suddenly entering the road or a vehicle running a red light ? where milliseconds of delay may determine safety outcomes. This thesis aims to close this gap by integrating temporal dynamics modeling into multimodal LLM frameworks, enabling predictive risk recognition in real time.

Hybrid Graph Neural Network and Agent-Based Modeling for Urban Traffic Flow Estimation Using Intersection Camera Counts

Accurate estimation of traffic flows is fundamental for intelligent transportation systems, urban mobility management, and sustainable planning. Traditionally, such estimations rely on extensive sensor infrastructures, including inductive loops, GPS trajectories, or mobile phone data. However, in many cities?particularly small and medium-sized ones?camera counts at intersections remain the most available and cost-effective data source. These counts provide only partial and noisy observations of the underlying traffic dynamics, making the problem of network-wide flow estimation challenging. Recent advances in Graph Neural Networks (GNNs) offer powerful tools for modeling spatiotemporal processes on road networks, learning flow patterns directly from graph-structured data. At the same time, agent-based models (ABMs), though computationally demanding, provide interpretable simulations that capture the microscopic decision-making of vehicles and drivers, such as route choice and turning movements. This project proposes a hybrid approach that integrates GNN learning with ABM insights to leverage the strengths of both paradigms.

Station-level demand forecasting for Tartu Smart Bike using machine learning

Accurate forecasting allows operators to strategically reposition bikes, ensuring availability where and when it's most needed. This directly contributes to a better user experience by reducing the frustration of finding empty docks or unavailable bikes. Furthermore, effective demand prediction is crucial for the equitable and sustainable expansion of bike-sharing systems, helping to mitigate urban congestion and promoting environmentally friendly transportation. What You'll Build? Delve into the world of mobility modelling and machine learning to decide which is the best model for this task. Implement and validate a machine learning model to forecast demand in existing bikeshare stations using historic data and other information, such as weather and time of day.