10 papers
Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
Alexander Langmann, Yevhenii Tokarev, Mattia Piccinini +2
Sampling-based trajectory planners are widely used for agile autonomous driving due to their ability to generate fast, smooth, and kinodynamically feasible trajectories. However, t…
Enhancing Physical Consistency in Lightweight World Models
Dingrui Wang, Zhexiao Sun, Zhouheng Li +8
A major challenge in deploying world models is the trade-off between size and performance. Large world models can capture rich physical dynamics but require massive computing resou…
Real-time Velocity Profile Optimization for Time-Optimal Maneuvering with Generic Acceleration Constraints
Mattia Piazza, Mattia Piccinini, Sebastiano Taddei +2
The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with parti…
Model-Structured Neural Networks to Control the Steering Dynamics of Autonomous Race Cars
Mattia Piccinini, Aniello Mungiello, Georg Jank +3
Autonomous racing has gained increasing attention in recent years, as a safe environment to accelerate the development of motion planning and control methods for autonomous driving…
MP-RBFN: Learning-based Vehicle Motion Primitives using Radial Basis Function Networks
Marc Kaufeld, Mattia Piccinini, Johannes Betz
This research introduces MP-RBFN, a novel formulation leveraging Radial Basis Function Networks for efficiently learning Motion Primitives derived from optimal control problems for…
Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
Bei Zhou, Baha Zarrouki, Mattia Piccinini +3
Autonomous drifting is a complex and crucial maneuver for safety-critical scenarios like slippery roads and emergency collision avoidance, requiring precise motion planning and con…