Autonomous vehicle pavement response refers to how self-driving vehicles interact with road surfaces, including factors like tire wear, vibration, and load distribution. These vehicles use sensors to assess pavement conditions, enabling real-time adj
Autonomous vehicle design for air mobility focuses on creating self-piloting aerial vehicles, such as drones or air taxis, that can navigate without human intervention. These vehicles integrate advanced technologies like AI, machine learning, and sen
Supporting Ultra-Reliable Low-Latency Communications (URLLC) in 5G and beyond networks focuses on providing high reliability and minimal delay for critical applications like autonomous vehicles and remote surgery. Techniques like network slicing, edg
Measurement-based joint estimation of battery states for Electric Vehicle (EV) applications involves using real-time data to accurately estimate parameters like state of charge (SOC), state of health (SOH), and state of energy (SOE). This approach im
Federated learning for autonomous driving enables decentralized model training across multiple vehicles without sharing raw data. It allows vehicles to collaboratively improve driving algorithms while maintaining data privacy. This approach enhances
The safety impact assessment of in-vehicle driver assistance systems (ADAS) and autonomous vehicle (AV) integration in Indian road conditions evaluates their effectiveness in improving road safety. It considers factors like traffic congestion, road i
Evaluating the effectiveness and acceptance of Advanced Driver Assistance Systems (ADAS) and Driver Monitoring Systems (DMS) in India through Field Operational Tests (FOT) involves assessing their impact on driving performance, safety, and user exper
Decentralized Multi-Agent intelligent traffic light controller based on a deep reinforcement learning model is developed and analysed with fixed and adaptive traffic light controllers. We integrate this model with MultiVeStA to check properties of th
Automated driving incorporating Naturalistic Driving Study (NDS) focuses on understanding real-world driving behaviors and conditions to enhance autonomous vehicle performance. By analyzing natural driving data, the approach improves vehicle algorith
Machine learning in wireless communication optimizes network performance by enabling smarter decision-making for tasks like resource allocation, interference management, and signal processing. It improves spectrum utilization, enhances data transmiss
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