@article{cemag_2019,
title = {Central vehicle computer design: Software taking over},
author = {Milan Z. Bjelica and Željko Lukač},
doi = {10.1109/MCE.2019.2929813},
issn = {2162-2248},
year = {2019},
date = {2019-11-01},
journal = {IEEE Consumer Electronics Magazine},
volume = {8},
number = {6},
abstract = {To prevent each vehicle manufacturer from “reinventing the wheel” and spending vast amounts of time and engineering, a paradigm shift is needed. Transferring the vehicle design to standardized hardware would enable software to take the key role and allow the horizontal approach in design, where each feature may be added as a module. This sets the stage for a central vehicle computer-a brain for next generation vehicles which is everything but easy to design. This article discusses one such design and identifies the required building blocks for this rising market.},
howpublished = {M22},
keywords = {ADAS, automotive, ieeexplore, infotainment, market research, software framework},
pubstate = {published},
tppubtype = {article}
}

@conference{2019_iwssip,
title = {Deep Learning vs. Safety - Practical Approach and Platform Design Perspective},
author = {Milan Z. Bjelica},
url = {http://www.milanbjelica.info/wp-content/uploads/2019/06/program-web.pdf
https://www.youtube.com/watch?v=tJrDJsBiDqg},
year = {2019},
date = {2019-06-06},
booktitle = {Proceedings of 2019 International Conference on Systems, Signals and Image Processing (IWSSIP)},
publisher = {EURASIP},
address = {Osijek, Croatia},
abstract = {Deep Learning is a promising field, allowing an increase in artificial intelligence applications across many fields, ranging from data science, medical, weather, and aerospace to automotive. Applications of computer vision-based deep learning are vastly assisted by modern System-on-Chip architectures, which provide the required parallelism, heterogeneity and interfacing. However, the application of deep learning to safety-critical contexts where human lives might be at stake, such as in self-driving cars, still has many pitfalls. Ongoing academic research tackles transparent AI, in which the correctness of AI is attempted to be reached by design; however, the outcome of this research is still far-fetched. In this talk, we will discuss a practical approach when integrating deep learning vision-based solutions into a safety-critical context, which can be achieved today. We outline an approach which introduces a software/hardware platform design which fosters diversity, with the goal of minimizing risk of critical failures which are induced by AI in decision making.},
howpublished = {M32},
note = {Keynote},
keywords = {ADAS, automotive, deep learning, keynote, market research},
pubstate = {published},
tppubtype = {conference}
}

@conference{ceskeynote1,
title = {Smart Devices, Homes, Cars: Is the Modern Consumer Overwhelmed?},
author = {Milan Z. Bjelica},
url = {http://www.icce.org/conference/icce-2017-special-sessions/
https://www.youtube.com/watch?v=KK7CwKT1qNU},
year = {2017},
date = {2017-01-09},
urldate = {2017-01-09},
booktitle = {IEEE International Conference on Consumer Electronics},
abstract = {A potpourri of modern day technologies, which assume connectivity and ubiquitous access, accelerate the pace of our everyday lives. Although useful at the first glance, the galore of choices keep users entangled and hard on new technology adoption. This is particularly true with the adoption of recent devices and applications, including, for example, smart watches, internet of things and home automation technologies. It may be a daunting task for the consumer electronics industry to devise a specific, targeted and clear use cases which would promote the technology. In this talk we would give an outline of current challenges in this regard, via the examples of current products and R&D endeavors within the RT-RK research institute. },
howpublished = {M32},
note = {Keynote},
keywords = {business, internet of things, keynote, market research, smart homes},
pubstate = {published},
tppubtype = {conference}
}