Imagen de Portada

MP-RBFN: Learning-based Vehicle Motion Primitives using Radial Basis Function Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kaufeld, Marc, Piccinini, Mattia, Betz, Johannes
Formato:	Preprint
Publicado:	2025
Materias:	Robotics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles
por: Kaufeld, Marc, et al.
Publicado: (2024)

Precise and Efficient Collision Prediction under Uncertainty in Autonomous Driving
por: Kaufeld, Marc, et al.
Publicado: (2025)

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
por: Moller, Korbinian, et al.
Publicado: (2025)

DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving
por: Wang, Dingrui, et al.
Publicado: (2024)

Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
por: Langmann, Alexander, et al.
Publicado: (2025)

Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
por: Zhou, Bei, et al.
Publicado: (2025)

MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation
por: Kaufeld, Marc, et al.
Publicado: (2025)

A Quasi-Steady-State Black Box Simulation Approach for the Generation of g-g-g-v Diagrams
por: Werner, Frederik, et al.
Publicado: (2025)

Kineto-Dynamical Planning and Accurate Execution of Minimum-Time Maneuvers on Three-Dimensional Circuits
por: Piccinini, Mattia, et al.
Publicado: (2025)

Radial Basis Function Neural Networks for Formation Control of Unmanned Aerial Vehicles
por: Bui, Duy-Nam, et al.
Publicado: (2024)

Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing
por: Jank, Georg, et al.
Publicado: (2026)

From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios
por: Gao, Yuan, et al.
Publicado: (2025)

Results of the 2023 CommonRoad Motion Planning Competition for Autonomous Vehicles
por: Kochdumper, Niklas, et al.
Publicado: (2024)

Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform
por: Piccinini, Mattia, et al.
Publicado: (2026)

Informed Hybrid Zonotope-based Motion Planning Algorithm
por: Xie, Peng, et al.
Publicado: (2025)

A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control
por: Zarrouki, Baha, et al.
Publicado: (2024)

A Learning-based Planning and Control Framework for Inertia Drift Vehicles
por: Zhou, Bei, et al.
Publicado: (2025)

Motion Primitives Planning For Center-Articulated Vehicles
por: Hu, Jiangpeng, et al.
Publicado: (2024)

Model-Structured Neural Networks to Control the Steering Dynamics of Autonomous Race Cars
por: Piccinini, Mattia, et al.
Publicado: (2025)

StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving
por: Gao, Yuan, et al.
Publicado: (2026)

A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
por: Trauth, Rainer, et al.
Publicado: (2024)

Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning
por: Brusnicki, Roberto, et al.
Publicado: (2025)

EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
por: Schäfer, Finn Rasmus, et al.
Publicado: (2026)

Adaptive Learning-based Model Predictive Control Strategy for Drift Vehicles
por: Zhou, Bei, et al.
Publicado: (2025)

Three-Dimensional Vehicle Dynamics State Estimation for High-Speed Race Cars under varying Signal Quality
por: Goblirsch, Sven, et al.
Publicado: (2024)

iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization
por: Ortiz-Haro, Joaquim, et al.
Publicado: (2024)

Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware
por: Moller, Korbinian, et al.
Publicado: (2026)

Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios
por: Moller, Korbinian, et al.
Publicado: (2025)

MP-SfM: Monocular Surface Priors for Robust Structure-from-Motion
por: Pataki, Zador, et al.
Publicado: (2025)

FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving
por: Trauth, Rainer, et al.
Publicado: (2024)

PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation
por: Li, Yutai, et al.
Publicado: (2026)

Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms
por: Moller, Korbinian, et al.
Publicado: (2025)

Biasing the Driving Style of an Artificial Race Driver for Online Time-Optimal Maneuver Planning
por: Taddei, Sebastiano, et al.
Publicado: (2025)

How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study
por: Brusnicki, Roberto, et al.
Publicado: (2026)

PUMA: Deep Metric Imitation Learning for Stable Motion Primitives
por: Pérez-Dattari, Rodrigo, et al.
Publicado: (2023)

A Modular Framework for Motion Planning using Safe-by-Design Motion Primitives
por: Vukosavljev, Marijan, et al.
Publicado: (2019)

FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching
por: Nguyen, Khang, et al.
Publicado: (2025)

Terrain-Awared LiDAR-Inertial Odometry for Legged-Wheel Robots Based on Radial Basis Function Approximation
por: Liu, Yizhe, et al.
Publicado: (2025)

MIND-Stack: Modular, Interpretable, End-to-End Differentiability for Autonomous Navigation
por: Jahncke, Felix, et al.
Publicado: (2025)

MeshDMP: Motion Planning on Discrete Manifolds using Dynamic Movement Primitives
por: Vedove, Matteo Dalle, et al.
Publicado: (2024)