The train_fmu_gym toolchain represents a significant advancement in applying reinforcement learning to multi-physical systems by bridging existi...
The DLR project SAFER² (Sensor and AI Fusion for Enhanced PeRformance and Reliability) focused on integrating sensor data with advanced models...
The AI For Mobility (AFM) concept has been designed as a universal platform for testing of vehicle dynamics control methods. A classification of...
Data-driven or derived through physics? This is a key question when modelling dynamical systems, where recent developments in machine learning...
In automotive applications, the goal of vertical dynamics design is to optimize the vehicle chassis suspension so that certain objectives, such...
Within the AICloud project, the DLR institute SR researched together with EFS TechHub GmbH on innovative AI-based sensor algorithms using a clou...
Many applications of machine learning and reinforcement learning approaches require a transfer of the trained neural network to the embedded sof...
The eFMI standard (Functional Mock-Up Interface for embedded systems) enables a standardized workflow for the application of physical models, li...
The AFM’s AI-based path detection module, which is responsible for identifying drivable paths, was tested on public roads with an intrinsicall...
The TMeasy is a tire model suitable for vehicle handling analyses and enables easy parametrization. Recently, a convenient interface to Modelica...
The semi-active suspension of the testing platform AI For Mobility (AFM) enables the real-time control of the vertical dynamics of the vehicle....
The intelligent control algorithms we develop at SR base i.a. on artificial intelligence (AI) and machine learning (ML) approaches which need a...
In order to enable the testing platform AI For Mobility (AFM) to perceive its environment, it is equipped with multiple cameras. The camera imag...
The hybrid research vehicle AFM (AI For Mobility) is equipped with multiple camera sensors to sense the vehicles environment. A real time path...