European Study Group with Industry (ESGI)

This challenge invites participants to develop a novel Advanced Driver Assistance System (ADAS) aimed at enabling safer and more efficient overtaking maneuvers — a functionality that, while highly relevant, is not yet commercially available in existing systems. Participants will work with the CARLA simulator and related open-source tools to design, implement, and test their proposed ADAS solution. The core challenges lie in understanding and modeling complex sensor behavior based on current automotive standards, defining an innovative and logically consistent system functionality, and implementing it in a simulated environment using realistic virtual sensors. Teams must also create a diverse and technically robust set of driving scenarios, accounting for highway conditions, varying traffic density, environmental visibility, pedestrian and vehicle behavior, and degraded infrastructure. Additionally, the challenge requires rigorous performance analysis through quantitative metrics such as accuracy, reaction time, and false positive/negative rates. One of the key difficulties will be achieving a system that performs reliably under uncertainty and edge cases, while balancing implementation complexity with simulation constraints.

This challenge focuses on optimizing the use of multiple water sources to meet specific flow and quality requirements, using a simplified model based on mass balances (hydraulic and quality), process efficiencies, and operational constraints. Pre-existing models and equations will be provided to support the work. The complexity of the challenge lies in the heterogeneity of available water sources—such as groundwater, surface water, reclaimed, desalinated, or network water—each differing in flow variability, quality, and treatment cost. The system must not only deliver a target flow rate but also ensure that the final water blend meets strict quality standards depending on its end use, such as irrigation, industrial processes, or drinking water. Operational infrastructure comes with its own limitations, including flow capacity, pressure, retention time, and energy efficiency, along with regulatory and environmental constraints. Further complexity arises from the fact that treatment process efficiencies depend on input water quality, introducing non-linearities that must be carefully modeled. A key challenge will be to strike the right balance between a model that is simple and tractable, yet realistic and robust enough to produce useful, reliable results.

Handling volumetric risk for renewable energy sources presents a significant challenge primarily because production quantity is inherently weather-dependent and thus highly variable and unpredictable. In competitive electricity markets, this uncertain volume, coupled with fluctuating prices, leads to simultaneous fluctuations in both price and volume, causing significant uncertainty in revenues and cash flows. While established tools exist to mitigate price risk, such as Power Purchase Agreements (PPAs) which can lock in the selling price for extended periods, effectively neutralizing this specific dimension of risk, the challenge of managing the volumetric risk itself is less mature. Even when price risk is neutralized through contracts like PPAs, the income uncertainty from variable wind generation remains a concern for asset owners. The market for managing the specific risks of renewable energy sources, especially the challenge of simultaneously hedging both price and volume fluctuations for wind power producers, is still very much in development. Finding and implementing the best tools and strategies for this dual risk is described as an ongoing process of research and experimentation.