25 citations · 55 across the 18 of their papers we have counts for
18 papers
Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks
Tong Duy Son, Marc Brughmans, Andrey Hense +4
Mode shape recognition is a fundamental task in automotive NVH development, yet it remains dependent on manual visual inspection by experienced engineers. Existing approaches based…
Automotive Engineering-Centric Agentic AI Workflow Framework
Tong Duy Son, Zhihao Liu, Piero Brigida +4
Engineering workflows such as design optimization, simulation-based diagnosis, control tuning, and model-based systems engineering (MBSE) are iterative, constraint-driven, and shap…
Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
Tong Duy Son, Kohta Sugiura, Marc Brughmans +7
Automotive engineering development increasingly relies on heterogeneous 3D data, including finite element (FE) models, body-in-white (BiW) representations, CAD geometry, and CFD me…
ExAMPC: the Data-Driven Explainable and Approximate NMPC with Physical Insights
Jean Pierre Allamaa, Panagiotis Patrinos, Tong Duy Son
Amidst the surge in the use of Artificial Intelligence (AI) for control purposes, classical and model-based control methods maintain their popularity due to their transparency and…
Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach
Renzi Wang, Flavia Sofia Acerbo, Tong Duy Son +1
This paper presents a novel approach to imitation learning from observations, where an autoregressive mixture of experts model is deployed to fit the underlying policy. The paramet…
AV-Occupant Perceived Risk Model for Cut-In Scenarios with Empirical Evaluation
Sarah Barendswaard, Tong Duy Son
Advancements in autonomous vehicle (AV) technologies necessitate precise estimation of perceived risk to enhance user comfort, acceptance and trust. This paper introduces a novel A…
Driving from Vision through Differentiable Optimal Control
Flavia Sofia Acerbo, Jan Swevers, Tinne Tuytelaars +1
This paper proposes DriViDOC: a framework for Driving from Vision through Differentiable Optimal Control, and its application to learn autonomous driving controllers from human dem…
Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin
Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer +1
In this work, we focus on the challenge of transferring an autonomous driving controller from simulation to the real world (i.e. Sim2Real). We propose a data-efficient method for o…
Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation
Jean Pierre Allamaa, Panagiotis Patrinos, Toshiyuki Ohtsuka +1
The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling su…
Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin
Kevin Voogd, Jean Pierre Allamaa, Javier Alonso-Mora +1
Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, u…
Safety Envelope for Orthogonal Collocation Methods in Embedded Optimal Control
Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer +1
Orthogonal collocation methods are direct approaches for solving optimal control problems (OCP). A high solution accuracy is achieved with few optimization variables, making it mor…
Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving
Flavia Sofia Acerbo, Jan Swevers, Tinne Tuytelaars +1
This work evaluates and analyzes the combination of imitation learning (IL) and differentiable model predictive control (MPC) for the application of human-like autonomous driving.…
Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field
Yurui Du, Flavia Sofia Acerbo, Jens Kober +1
In recent years, imitation learning (IL) has been widely used in industry as the core of autonomous vehicle (AV) planning modules. However, previous IL works show sample inefficien…
Virtual Reality Assisted Human Perception in ADAS Development: a Munich 3D Model Study
Felix Bognar, Markus Oster, Herman Van der Auweraer +1
As the development of autonomous driving (AD) and advanced driver assistance systems (ADAS) progresses, the relevance of the comfort of users is gaining increasing interest. It bec…
Sim2real for Autonomous Vehicle Control using Executable Digital Twin
Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer +1
In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digita…
MPC-based Imitation Learning for Safe and Human-like Autonomous Driving
Flavia Sofia Acerbo, Jan Swevers, Tinne Tuytelaars +1
To ensure user acceptance of autonomous vehicles (AVs), control systems are being developed to mimic human drivers from demonstrations of desired driving behaviors. Imitation learn…
Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving
Flavia Sofia Acerbo, Mohsen Alirezaei, Herman Van der Auweraer +1
This paper presents a safe imitation learning approach for autonomous vehicle driving, with attention on real-life human driving data and experimental validation. In order to incre…
Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving
Jean Pierre Allamaa, Petr Listov, Herman Van der Auweraer +2
In this paper, we present the development and deployment of an embedded optimal control strategy for autonomous driving applications on a Ford Focus road vehicle. Non-linear model…