A software stack for next-generation automotive systems on many-core heterogeneous platforms

Burgio, Paolo; Bertogna, Marko; Capodieci, Nicola; Cavicchioli, Roberto; Sójka, Michał; Houdek, Premysl; Marongiu, Andrea; Gai, Paolo; Scordino, Claudio; Morelli, Bruno

doi:10.1016/j.micpro.2017.06.016

Microprocessors and Microsystems

2017

DOI: 10.1016/j.micpro.2017.06.016

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A software stack for next-generation automotive systems on many-core heterogeneous platforms

Paolo Burgio

Marko Bertogna

Nicola Capodieci

et al.

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Cited by 18 publications

References 14 publications

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Perception simplex: Verifiable collision avoidance in autonomous vehicles amidst obstacle detection faults

Bansal,

Kim,

et al. 2024

Software Testing Verif & Rel

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Advances in deep learning have revolutionized cyber‐physical applications, including the development of autonomous vehicles. However, real‐world collisions involving autonomous control of vehicles have raised significant safety concerns regarding the use of deep neural networks (DNNs) in safety‐critical tasks, particularly perception. The inherent unverifiability of DNNs poses a key challenge in ensuring their safe and reliable operation. In this work, we propose perception simplex (), a fault‐tolerant application architecture designed for obstacle detection and collision avoidance. We analyse an existing LiDAR‐based classical obstacle detection algorithm to establish strict bounds on its capabilities and limitations. Such analysis and verification have not been possible for deep learning‐based perception systems yet. By employing verifiable obstacle detection algorithms, identifies obstacle existence detection faults in the output of unverifiable DNN‐based object detectors. When faults with potential collision risks are detected, appropriate corrective actions are initiated. Through extensive analysis and software‐in‐the‐loop simulations, we demonstrate that provides deterministic fault tolerance against obstacle existence detection faults, establishing a robust safety guarantee.

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Perception simplex: Verifiable collision avoidance in autonomous vehicles amidst obstacle detection faults

Bansal,

Kim,

et al. 2024

Software Testing Verif & Rel

View full text Add to dashboard Cite

show abstract

Design of a living lab for autonomous driving: an investigation under the lens of the triple helix model

2023

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User-interactive innovation knowledge acquisition model based on social media

Zeng

Jing-hua

Zhang

et al. 2022

Information Processing & Management

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A software stack for next-generation automotive systems on many-core heterogeneous platforms

Cited by 18 publications

References 14 publications

Perception simplex: Verifiable collision avoidance in autonomous vehicles amidst obstacle detection faults

Perception simplex: Verifiable collision avoidance in autonomous vehicles amidst obstacle detection faults

Design of a living lab for autonomous driving: an investigation under the lens of the triple helix model

User-interactive innovation knowledge acquisition model based on social media

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