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Validation of Automotive ADAS Systems in Hardware-in-the-Loop Test Environments for European General Safety Regulation Homologation

ADAS technologies play a crucial role in enhancing road safety, and their compliance with safety regulations is of utmost importance. Hardware-in-the- Loop (HIL) testing offers an efficient and repeatable validation approach, ensuring Advanced Driver Assistance Systems (ADAS) meet the stringent requirements of the European General Safety Regulation.

This Master's thesis focuses on the validation of automotive ADAS within HIL test environments, specifically for tests related to homologation according to the European General Safety Regulation. The study presents the HIL test bench setup, defines specific ADAS test scenarios, and analyzes validation results compared to real-world testing.

Challenges and recommendations for future improvements are identified. The research aims to enhance ADAS regulatory compliance and contribute to safer mobility on European roads.

Contact:     Ing. Giuseppe Mercurio - mercurio_g@fev.com

Tutor:         

 

Requirements: 

  • Educational background in Electrical or Mechanical engineering.
  • Good knowledge of MATLAB / Simulink and Python programming.
  • Good English language skills in speaking and writing.
  • High motivation and commitment.

 

Speed Profile Prediction for energy management tailored to the driver-style

Predicting the driver behavior is an enabler for the prediction and therefore the reduction of vehicle emissions. Given real-time data on route and traffic, it is possible to design an adaptive speed profile prediction, which matches the driver style. This master thesis focuses on completing the porting of the Speed Profile Prediction (SPP) function from a prototype Hybrid Control Unit (HCU)  Simulink model, to an portable Python implementation. The SPP shall be tested on several platforms, including an Infotainment System and finally as service in the Cloud. To validate the SPP and adapt the driver style parameters, synthetic driving data will be generated using dSpace ASM Traffic simulator. Finally, traffic participants are introduced in ASM to reproduce the driver behavior, given navigation data from map service provider.

Contact:     Ing. Giuseppe Mercurio - mercurio_g@fev.com

Tutor:         Prof.ssa Barbara Masini

                    Ing. Alessandro Bazzi

Requirements: 

  • Educational background in Electrical or Mechanical engineering.
  • Good knowledge of MATLAB / Simulink and Python programming.
  • Good English language skills in speaking and writing.
  • High motivation and commitment.

 

 

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Contacts

Green Mobility Research Lab

+ 30 051 0031834

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Nicolò Cavina

Full Professor at the University of Bologna, Co-founder of the Green Mobility Research Lab

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Giuseppe Mercurio

Functional Safety & Cybersecurity Manager at FEV Italy

+39 342 5472575

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Stefano Longhi

Virtual Development Team Leader at FEV Italy Srl

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Alessandro Bazzi

Senior assistant professor at the University of Bologna

+39 051 20 9 3880

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