35 citations · 37 across the 12 of their papers we have counts for
13 papers
Fusion or Confusion? Potential and Challenges in Fusion of Onboard Sensors and V2X Data in Cooperative Perception
Amir Mohammadisarab, Miguel Sepulcre, Luca Lusvarghi +5
Connected Automated Vehicles (CAVs) utilize their onboard sensors to perceive the environment. The perception range and accuracy can be affected by adverse weather or non-line-of-s…
Importance of Intent-Sharing for V2X-based Maneuver Coordination
Rafael Molina-Masegosa, Sergei S. Avedisov, Miguel Sepulcre +3
This paper examines the critical role of intent-sharing in enabling effective maneuver coordination for connected and automated vehicles (CAVs). Successful maneuver coordinations r…
FORESEE: A Cooperative Lane Change Model for Connected and Automated Driving
Rafael Molina-Masegosa, Sergei S. Avedisov, Miguel Sepulcre +3
This paper presents FORESEE, a novel cooperative lane change model for connected and automated driving. FORESEE leverages Vehicle-to-Everything (V2X) data to anticipate traffic con…
Multi-Target Maneuver Coordinations: Unlocking Coordination Opportunities in Connected Automated Driving
Rafael Molina-Masegosa, Sergei S. Avedisov, Miguel Sepulcre +3
Maneuver coordination is a key enabler of connected and automated driving, allowing vehicles to negotiate and execute maneuvers that would otherwise be difficult, inefficient or un…
When Cooperation Should End: Maneuver Coordination Cancellation for Connected Automated Driving
Rafael Molina-Masegosa, Sergei S. Avedisov, Miguel Sepulcre +2
Maneuver coordination is essential for cooperative connected automated driving, enabling vehicles to negotiate maneuvers and interactions through V2X communication. While prior wor…
5G Network Architecture and Configuration Choices to Support Teleoperated Driving at Scale
M. C. Lucas-Estañ, B. Coll-Perales, M. I. Khan +4
Teleoperated driving (ToD) enables the remote driving or control of vehicles. For this purpose, vehicles must transmit video feeds to the ToD control center so that the remote oper…
Impact of ADAS and V2X Penetration Rates on Cooperative Active Safety
M. C. Lucas-Estañ, B. Coll-Perales, M. I. Khan +2
A major driver of connected and automated driving is cooperative active safety. The effectiveness of cooperative safety applications depends on the ability of vehicles to detect tr…
CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
Deyuan Qu, Qi Chen, Takayuki Shimizu +1
Autonomous vehicles equipped with robust onboard perception, localization, and planning still face limitations in occlusion and non-line-of-sight (NLOS) scenarios, where delayed re…
PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search
Xiaolong Tu, Dawei Chen, Kyungtae Han +2
Hardware-Aware Neural Architecture Search (HW-NAS) has emerged as a powerful tool for designing efficient deep neural networks (DNNs) tailored to edge devices. However, existing me…
The Search for Relevance: A Context-Aware Paradigm Shift in Semantic and Task-Oriented V2X Communications
Luca Lusvarghi, Javier Gozalvez, Baldomero Coll-Perales +4
The design of communication systems has traditionally prioritized the reliable and timely delivery of data. However, the scalability challenges faced by the evolution towards a dat…
Scene-Aware Conversational ADAS with Generative AI for Real-Time Driver Assistance
Kyungtae Han, Yitao Chen, Rohit Gupta +1
While autonomous driving technologies continue to advance, current Advanced Driver Assistance Systems (ADAS) remain limited in their ability to interpret scene context or engage wi…
GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices
Xiaolong Tu, Dawei Chen, Kyungtae Han +2
We present GreenAuto, an end-to-end automated platform designed for sustainable AI model exploration, generation, deployment, and evaluation. GreenAuto employs a Pareto front-based…
Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices
Xiaolong Tu, Anik Mallik, Dawei Chen +4
Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often…