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Mattia Piccinini

7 accepted papers

2026

Differentiable Weights-Varying Nonlinear MPC via Gradient-Based Policy Learning: An Autonomous Vehicle Guidance Example

RA-L 2026

Tuning Model Predictive Control (MPC) cost weights for multiple, competing objectives is labor-intensive. Derivative-free automated methods, such as Bayesian Optimization, reduce manual effort but remain slow, while Differentiable MPC (Diff-MPC) exploits solver sensitivities for faster gradient-base

Cited by 0SourceScholar
2026

NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving

ICRA 2026poster

Understanding risk in autonomous driving requires not only perception and prediction, but also high-level reasoning about agent behavior and context. Current Vision Language Model (VLM)-based methods primarily ground agents in static images and provide qualitative judgments, lacking the spatio–tempo…

2026

Real-Time Velocity Profile Optimization for Time-Optimal Maneuvering With Generic Acceleration Constraints

RA-L 2026

The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with particular relevance to autonomous racing. However, the existing methods either support arbitrary acceleration constraints at

Cited by 0SourcecodeScholar
2026

Real-Time Velocity Profile Optimization for Time-Optimal Maneuvering with Generic Acceleration Constraints

ICRA 2026poster

The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with particular relevance to autonomous racing. However, the existing methods either support arbitrary acceleration constraints at…

2025

Kineto-Dynamical Planning and Accurate Execution of Minimum-Time Maneuvers on Three-Dimensional Circuits

ICRA 2025

Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates

Cited by 3SourceScholar
2025

Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example

IROS 2025

Autonomous drifting is a complex and crucial maneuver for safety-critical scenarios like slippery roads and emergency collision avoidance, requiring precise motion planning and control. Traditional motion planning methods often struggle with the high instability and unpredictability of drifting, par

Cited by 0SourceScholar
2020

Real-time optimal control of an autonomous RC car with minimum-time maneuvers and a novel kineto-dynamical model

IROS 2020poster

In this paper, we present a real-time non-linear model-predictive control (NMPC) framework to perform minimum-time motion planning for autonomous racing cars. We introduce an innovative kineto-dynamical vehicle model, able to accurately predict non-linear longitudinal and lateral vehicle dynamics. T…

Cited by 39SourceScholar