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Project Coordinator


 

Dr Georg Stettinger

 
Infineon Technologies AG
81726 München
Germany

Risk Monitoring and Mitigation for Automated Vehicles: A Model Predictive Control Perspective

Kilin Tong, Fengwei Guo, Selim Solmaz, Martin Steinberger, Martin Horn

Abstract: Despite recent advances in algorithms and technology, self-driving vehicles are still susceptible to errors that can have severe consequences. As a result, effective risk monitoring and mitigation measures for autonomous driving systems are in high demand. To overcome this issue, several specifications and standards have been developed. However, a theoretical framework for dealing with autonomous vehicle hazards has rarely been presented. This study suggests a risk modeling method inspired by ideas from control theory and introduces a Model Predictive Control (MPC) Framework to deal with risks in general. Two application examples are presented. The first example shows how MPC parameters may affect the aggressiveness of the response. In the second example, our proposed risk monitoring and mitigation module is integrated into a visionbased Adaptive Cruise Control (ACC) system. Simulation results indicate a significant improvement in collision avoidance rate (from 0% to 47% in edge scenarios) during the Euro NCAP ACC Car-to-Car tests with a stationary target, which demonstrates the utility of our approach for addressing various types of hazards faced by autonomous vehicles. Index Terms—automated vehicles, model predictive control, risk monitoring, risk mitigation, functional safety


Risk Monitoring and Mitigation for Automated Vehicles: A Model Predictive Control Perspective


 

Acknowledgement

ArchitectECA2030 has been accepted for funding within (ECSEL JU) in collaboration with the European Union’s H2020 Framework Programs under grant agreement No 877539.

The project will receive an ECSEL JU funding up to 4 M€ completed with national budgets from national funding authorities in Germany, Netherlands, Czech Republic, Austria and Norway.  

Project Facts

Short Name: ArchitectECA2030

Full Name: Trustable architectures with acceptable residual risk for the electric, connected and automated cars

Duration:  01/07/2020- 30/06/2023

Total Costs: ~ € 13,6 Mio.

Consortium: 20 partners from 8 countries

Coordinator: Infineon Technologies AG

Funding

 

Horizon 2020
Horizon 2020

 

    

National Funding

National Funding

 


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