8 papers
How the Fusion of Onboard Sensors and V2X Data can Improve (or not) the Cooperative Perception of Connected Automated Vehicles
Amir Mohammadisarab, Miguel Sepulcre, Luca Lusvarghi +1
Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adverse weather conditions or whe…
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…
Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data
Shahab Salehi, Luca Lusvarghi, Miguel Sepulcre +1
Trajectory prediction allows autonomous vehicles to anticipate the future behavior of surrounding objects (or agents) and, accordingly, maximize the safety and efficiency of their…
Self-Supervised Relevance Modelling in Autonomous Driving via Counterfactual Analysis
Luca Lusvarghi, Javier Gozalvez, Pablo Urbano Hidalgo
Autonomous driving relies on computationally intensive perception pipelines to continuously detect and track objects in the surrounding environment. While some objects are key to p…
Semantic and Task-Oriented V2X Communications: Pushing the Limits of V2X Networks Scalability
Luca Lusvarghi, Javier Gozalvez, Mohammad Irfan Khan +3
Scalable Vehicle-to-Everything (V2X) networks are key to support the large-scale deployment of connected and automated mobility. However, the scalability of V2X networks is current…
Deterministic Task Scheduling in In-Vehicle Networks for Software-Defined Vehicles
Keyvan Aghababaiyan, Baldomero Coll-Perales, Luca Lusvarghi +1
Modern vehicles are embedding increasing levels of automation, connectivity, and intelligence, which require advanced in-vehicle networks and computational platforms to support the…