Energy
Context
Autonomous vehicles (AVs) are complex cyberphysical systems that require extensive validation to ensure safety and reliability. Since real-world testing is expensive and potentially unsafe, simulation-based testing using platforms like CARLA has become a key component of AV software validation. These simulators reproduce realistic traffic scenarios, sensors, and environmental conditions, but they are computationally intensive and consume significant amounts of energy. As large-scale simulation campaigns become common (e.g., thousands of tests in continuous integration pipelines), improving the energy efficiency of simulation-based testing becomes increasingly important for sustainable software engineering.
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.