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SHARPEN - Scalable Highly Automated vehicles with Robust PErceptionN

Reference number
Coordinator EMBEDL AB
Funding from Vinnova SEK 8 887 082
Project duration April 2019 - June 2022
Status Completed
Venture Traffic safety and automated vehicles -FFI
End-of-project report 2018-05001eng.pdf (pdf, 4013 kB)

Purpose and goal

The aim of the project was to improve today´s machine learning methods based on deep learning to become more robust in challenging environments, such as night, rain, snow and dirt on the sensors. To achieve this objective, both technologies to generate synthetic data, where one can control these variables, as well as the development of new methods, have been developed. The project has also focused on bringing these systems closer to production by compacting them to reduce their resource usage.

Expected results and effects

Our results show that you can improve today´s system with the use of synthetic data and also significantly reduce the cost of annotation of data. We have shown that 90% of the annotated data can be replaced with synthetic data. Furthermore, we have developed methods that can synthesize data from failed sensors in vehicles through machine learning and data from other sensors. We have also explored the best way of sensor fusion for object detection. We have also shown that with methods developed in the project we can reduce the latency of the same object detection system by over 60%.

Planned approach and implementation

We have designed an interface to the Carla Simulator with semi-automatic functionality to generate 3D worlds with challenging conditions. The vehicle sensor setup was based on the Kitty open database for the development of autonomous vehicles. Furthermore, we used Nvidias Jetson Xavier AGX as a hardware platform for accelerated deep learning. Organizationally, monthly meetings and additional technical meetings were held as needed. Coordinating partner at the start was Volvo GTT. This role was taken over by Embedl, who was added as a partner during the course of the project.

External links

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

Last updated 6 December 2022

Reference number 2018-05001

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