6 citations · 7 across the 6 of their papers we have counts for
6 papers
PRIME: Perception Feedback with Situational Memory Embeddings in VLA Models
Erik Deinzer, Naya Baslan, Luca Paparusso +3
Current Vision-Language-Action (VLA) models for autonomous driving operate primarily through feedforward inference across the perception--reasoning--planning hierarchy. While moder…
Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction
Daniel Jost, Luca Paparusso, Martin Stoll +3
In highly interactive driving scenes, trajectory prediction is conditioned on information from surrounding traffic participants such as cars and pedestrians. Our main contribution…
Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
Feng Tao, Luca Paparusso, Chenyi Gu +6
Real-time path planning in constrained environments remains a fundamental challenge for autonomous systems. Traditional classical planners, while effective under perfect perception…
ZAPP! Zonotope Agreement of Prediction and Planning for Continuous-Time Collision Avoidance with Discrete-Time Dynamics
Luca Paparusso, Shreyas Kousik, Edward Schmerling +2
The past few years have seen immense progress on two fronts that are critical to safe, widespread mobile robot deployment: predicting uncertain motion of multiple agents, and plann…
Competitors-Aware Stochastic Lap Strategy Optimisation for Race Hybrid Vehicles
Francesco Braghin, Luca Paparusso, Manuel Riani +1
World Endurance Championship (WEC) racing events are characterised by a relevant performance gap among competitors. The fastest vehicles category, consisting in hybrid vehicles, ha…
Real-Time Forecasting of Driver-Vehicle Dynamics on 3D Roads: a Deep-Learning Framework Leveraging Bayesian Optimisation
Luca Paparusso, Stefano Melzi, Francesco Braghin
Most state-of-the-art works in trajectory forecasting for automotive target predicting the pose and orientation of the agents in the scene. This represents a particularly useful pr…