Energy
Context
Modern public transportation vehicles, such as trams, buses, trolleybuses and trains, increasingly rely on on-board computing units to process and securely transfer large volumes of data generated by sensors and surveillance cameras. These systems often operate on limited battery power during night-time parking, when vehicles are disconnected from external energy sources. During this time window, the on-board computer must complete several computationally intensive tasks—such as software updates, video decoding, compression, encryption, and data upload—before service resumes.
In collaboration with Supercomputing Systems AG (SCS) and a public transportation company in Romandie, this project addresses the challenge of executing these tasks reliably under strict energy and time constraints. Understanding how to configure the embedded system and how to select optimal communication protocols for data transfer in order to remain both energy-efficient and predictable is essential for dependable fleet operations.
Context
Autonomous vehicles (AVs) are complex cyberphysical systems that require extensive testing to ensure safety. Since field testing is costly and unsafe, simulation-based testing using platforms like CARLA and BeamNG.tech has become a cornerstone in AV software validation. These simulators rely heavily on GPU performance for rendering, physics, and sensor emulation, and are therefore both resource-intensive and energy-demanding. As the scale of simulation campaigns grows (thousands of tests per day in CI pipelines), understanding and optimizing GPU cost becomes critical for cost-effective and sustainable testing.