Simulation-based validation of ADAS in complex scenarios

Tematiche Principali:

Validazione basata sulla simulazione (Simulation-Based Validation)

Ottimizzazione

ADAS (Advanced Driver Assistance Systems)

Requisiti e Profilo del Candidato:

Corso di Laurea: Ingegneria dell'Automazione, Informatica o corsi di studio affini.

Ottimizzazione: Familiarità con la modellazione e la risoluzione di problemi di ottimizzazione multivariabile.

Competenze Software: Familiarità con Python ed ambiente MathWorks (es. Global Optimization Toolbox).

Sede:

Napoli, Italia

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The deployment of Highly Automated Vehicles (HAVs), enabled by complex hardware/software interactions and AI algorithms, has introduced new challenges for safety assessment. To tackle these issues, simulation-based falsification, which conducts an iterative search to identify safety-critical combinations of factors influencing driving scenarios, has emerged as a leading knowledge-driven validation paradigm. Specifically, by adopting an X-in-the-Loop (XiL) testing architecture, this approach enables the identification of safety-boundary conditions, which are valuable both for system refinement during development phases and for online monitoring to facilitate real-time risk mitigation. This thesis aims to explore and implement solutions for managing the high-dimensional parametric spaces of driving scenarios during the identification of safety-critical conditions. To achieve this, the work involves designing and modeling both driving scenarios and search strategies using Python and the MathWorks environment.

Main Topics:

Simulation-Based Validation

Optimization

ADAS

Prerequisites & Candidate Profile:

Degree Course: Automation Engineering, Computer Science, or related fields.

Optimization: Familiarity with modeling and solving multivariable optimization problems.

Software Skills: Familiarity with Python and the MathWorks environment (e.g., Global Optimization

Toolbox).

Location:

Naples, Italy