Refereed journal article or data article (A1)

Development and experimental validation of high performance embedded intelligence and fail-operational urban surround perception solutions of the PRYSTINE project




List of Authors: Novickis Rihards, Levinskis Aleksandrs, Fescenko Vitalijs, Kadikis Roberts, Ozols Kaspars, Ryabokon Anna, Schorn Rupert, Koszescha Jochen, Solmaz Selim, Stettinger Georg, Adu-Kyere Akwasi, Halla-Aho Lauri, Nigussie Ethiopia, Isoaho Jouni

Publisher: MDPI

Publication year: 2022

Journal: Applied Sciences

Journal name in source: Applied Sciences (Switzerland)

Volume number: 12

Issue number: 1

ISSN: 2076-3417

DOI: http://dx.doi.org/10.3390/app12010168

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/68506318


Abstract

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.


Downloadable publication

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Last updated on 2022-30-03 at 09:36