While end-to-end autonomous driving systems show promise, their application to micromobility vehicles is hindered by simulators failing to capture specific kinematics, such as differential drives and omni-wheels. This paper pro- poses a sim-to-real-aware, vehicle-specific end-to-end learning environment for the WHILL M...
This paper addresses the software portability gap between Model-Based Development (MBD) and advanced many-core execution for Cyber-Physical Systems (CPS). We present a workflow-preserving retargeting approach for Simulink-based CPS applications with candidate-wise data parallelism to OpenCL-based many-core processors....
3D object detection based on LiDAR point cloud data and deep neural networks (DNNs) plays a critical role in autonomous driving systems. Although state-of-the-art models achieve high accuracy, deploying them on edge devices remains challenging due to their computational complexity and latency. Furthermore, single-LiDAR...
Autonomous driving systems demand strict real-time properties and safety for practical realization, and development is progressing in heterogeneous environments where the industry standard AUTomotive Open System ARchitecture (AUTOSAR) Adaptive Platform (AUTOSAR AP) and Robot Operating System 2 (ROS 2) coexist; however,...
Ryudai Iwakami, Shunsuke Ito, Hiroyuki Hanyu et al.· IEEE Open Journal of the Ind...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
