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Big Automotive Data Analytics: SEnsor Modeling and Performance Analysis (BADA-SEMPA)

Reference number
Coordinator Volvo Personvagnar Aktiebolag - Active Safety & Chassis, PVV 1:2
Funding from Vinnova SEK 7 000 000
Project duration March 2016 - May 2018
Status Completed
End-of-project report 2015-04787eng.pdf (pdf, 567 kB)

Purpose and goal

The analysis of sensor data plays a crucial role to build highly automated and autonomous vehicles. Such analysis makes it possible to develop better sensor verification and accurate computer-aided engineering (CAE) simulations, and to better implement the active safety functions. The main research questions addressed by the project were related to improving ground truth, developing sensor models, and semi-, un-supervised data analysis. During the project, we have achieved the main goals and developed different analysis methods to improve the verification of AD.

Expected results and effects

Findings are presented and discussed in different conferences, e.g.: 1- J. Florbäck, L. Tornberg, N. Mohammadiha, “Offline Object Matching and Evaluation Process for Verification of Autonomous Driving”, ITSC, 2016. 2- J. Martinsson, N. Mohammadiha, A. Schliep, “Clustering Vehicle Motion Trajectories Using Finite Mixtures of Hidden Markov Models”, submitted. 3. E. L. Zec, N. Mohammadiha, A. Schliep, “Modelling Autonomous Driving Sensors Using Hidden Markov Models”, submitted. 4. E. Karlsson, N. Mohammadiha, “A Statistical GPS Error Model for Autonomous Driving”, IV 2018.

Planned approach and implementation

During the recent years, many companies have increased their efforts in developing autonomous driving. One important part of this is related to verification and validation. Machine learning ans statistical signal processing play an important role in this aspect and this project has been able to answer some of the open questions on which models are more suitable for such analysis but it has also shown some new challenges that were not clear from beginning. New projects and activities have to be planned to fully overcome c´such challenges.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 11 February 2020

Reference number 2015-04787

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