Abstract

Image Processing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy and have long processing times. Image approximation has been proposed by recent works for an energy-efficient design of these applications. It also reduces the impact of long processing times. The challenge here is that the IP applications often work as a part of bigger closed-loop control systems, e.g. advanced driver assistance system (ADAS). The impact of image approximations that tolerate certain error on these image-based control (IBC) systems is very important. However, there is a lack of tool support to evaluate the performance of such closed-loop IBC systems when the IP is approximated. We propose a framework - for both software-in-the-loop (SiL) and hardware-in-the-loop (HiL) simulation - for performance evaluation of image approximation on a closed-loop automotive IBC system (IMACS). Both simulation setups model the 3D environment in 3ds Max, and simulate the system dynamics, camera position and environment in V-REP. Our SiL setup simulates the system software in C++ or Matlab. Here, V-REP runs as a server and the software as a client in synchronous mode. Our HiL simulation setup runs the system software in the NVIDIA Drive PX2 platform and communicates to V-REP using application programming interfaces (APIs) for synchronous execution. We show the effectiveness of our framework using a vision-based lateral control example.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1109/meco.2019.8760055 under the license cc-by-nd
http://dx.doi.org/10.1109/meco.2019.8760055
https://repository.tudelft.nl/view/tno/uuid:93822fa7-042a-497a-b735-e0e5d64c7f03,
https://research.tue.nl/nl/publications/imacs-a-framework-for-performance-evaluation-of-image-approximati,
https://tue-staging.elsevierpure.com/en/publications/imacs-a-framework-for-performance-evaluation-of-image-approximati,
https://academic.microsoft.com/#/detail/2961686991
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Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1109/meco.2019.8760055
Licence: Other

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