Sensor Test Bench for Autonomous Vehicle Engineering Education

Autonomous Vehicles (AVs) represent a paradigm shift in automotive transportation, promising advancements in safety, accessibility, and convenience. However, unlocking their full potential requires extensive exploration in perception, planning, and motion control domains, tightly integrated with hardware electronics, mechanical systems, control software, and communication technologies. This work addresses the entry barriers encountered by emerging researchers venturing into the AV field. We introduce a sensor-centric laboratory bench aimed at facilitating education and experimentation within the Robot Operating System (ROS) framework, enabling students to explore AV subsystems systematically. The platform’s design emphasizes modularity and adaptability. By combining diverse sensor data streams, including LiDAR, computer vision, inertial measurements, and Real-Time Locating Systems (RTLS), the test bench provides a rich perceptive dataset for the end user. A comprehensive software repository complemented by detailed documentation encourages ongoing contributions to the open-source platform, with a container-based architecture ensuring reproducibility and cross-compatibility across ROS distributions. The proposed laboratory bench serves as a pragmatic, replicable solution for AV education.