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Safety analysis and verification/validation of machine learning based systems

To increase the level of autonomy, future vehicles will depend on functions based on Deep machine learning (DML) whose correct behavior cannot be guaranteed by traditional methods. To enable DML-based functions to take decisions in autonomous vehicles, SMILE II focuses on methods that allow such functions to be included.

Aim and goal

Increase knowledge around methods that can be used for verification and validation of deep learning-based system within safety critical applications. The following research questions are studied within the project:

How can model performance be monitored when pre-trained with different data sets?

How can new data be used to update models while maintaining model performance and security?

Challenge

Critical part of the functional safety standard ISO26262 are not defined for autonomous systems and their process demands and recommendations are not applicable for the development of machinelearning-based systems specification, design and test.

Solution

The project studies the possibility to design a safety cage to monitor input signals to a DML-based model that is used in a vehicle to recognize its surroundings.

Effect

To realise autonomous vehicles a perception system is required that can interpret the surrounding of the vehicle. To perceive objects in a dynamic environment such as traffic, the systems need to incorporate machine learning, that have the capability to learn from historical data. This project develops technologies that makes it possible to trust the perception system, that it perceives the surrounding in a correct way.


Summary

Project name

SMILE II

Status

Active

Area

Artificial intelligence, Mobility, Sensation and perception, Automated vehicles, Transport systems

Region

Västra Götalandsregionen

RISE role in project

Projektledare

Project start

Duration

2017-10-01 – 2019-09-30

Total budget

9 455 000

Partner

Volvo Cars, QRTECH, Semcon

Funders

Vinnova FFI, Machine Learning

The UN sustainable development goals

10. Reduced inequalities
11. Sustainable cities and communities

Cristofer Englund

Contact person

Cristofer Englund

Research Manager

+46 10 228 40 26
cristofer.englund@ri.se

Read more about Cristofer