Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3568160.3570232acmconferencesArticle/Chapter ViewAbstractPublication PagescscsConference Proceedingsconference-collections
research-article

Evaluation of Level 2 Automated Driving Artificial Intelligence Readiness in Simulated Scenarios

Published: 08 December 2022 Publication History

Abstract

Recent advances in state-of-the-art camera-based AI mechanisms in the automated driving field have leveraged great progress in the installation and widespread use of this technology along the recent years. However, vehicles with automated driving capabilities are usually equipped with a wide range of sensors that complement the perception capacity of camera-based AI algorithms. For this reason, this paper tries to reveal the degree of readiness of one of the most used open-source AI models for Level 2 automated driving. To this end, a set of simulated common driving scenarios were used to evaluate the predictions. The results obtained clearly indicate that the current capacity of this camera-based DNN model is not sufficient to be the only source of information in the process of environment perception of a Level 2 automated vehicle, and therefore, further progress in the context awareness needs to be achieved to consider its sole use in the perception stage.

References

[1]
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014).
[2]
Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin, Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider, and Nemanja Djuric. 2019. Multimodal trajectory predictions for autonomous driving using deep convolutional networks. In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2090–2096.
[3]
Roger Lanctot 2017. Accelerating the future: The economic impact of the emerging passenger economy. Strategy analytics 5(2017).
[4]
Justin Norden 2019. Efficient black-box assessment of autonomous vehicle safety. arXiv preprint arXiv:1912.03618(2019).
[5]
Abu Hasnat Mohammad Rubaiyat 2018. Experimental resilience assessment of an open-source driving agent. In 2018 IEEE PRDC. 54–63.
[6]
SAE SAE. 2021. J3016 standard: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles.
[7]
Takami Sato 2020. Hold tight and never let go: Security of deep learning based automated lane centering under physical-world attack. arXiv preprint arXiv:2009.06701(2020).
[8]
Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. 6105–6114.
[9]
Alessandro Toschi 2019. Characterizing perception module performance and robustness in production-scale autonomous driving system. In IFIP ICNPC. 235–247.
[10]
Ziyuan Zhong 2021. Detecting Safety Problems of Multi-Sensor Fusion in Autonomous Driving. arXiv preprint arXiv:2109.06404(2021).
[11]
Xugui Zhou, Anna Schmedding, Haotian Ren, Lishan Yang, Philip Schowitz, Evgenia Smirni, and Homa Alemzadeh. 2022. Strategic safety-critical attacks against an advanced driver assistance system. arXiv preprint arXiv:2204.06768(2022).

Index Terms

  1. Evaluation of Level 2 Automated Driving Artificial Intelligence Readiness in Simulated Scenarios

        Recommendations

        Comments

        Please enable JavaScript to view thecomments powered by Disqus.

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        CSCS '22: Proceedings of the 6th ACM Computer Science in Cars Symposium
        December 2022
        127 pages
        ISBN:9781450397865
        DOI:10.1145/3568160
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Sponsors

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 08 December 2022

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. ADAS
        2. AI
        3. Camera-based detection
        4. Perception

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Funding Sources

        • Horizon 2020

        Conference

        CSCS '22
        CSCS '22: Computer Science in Cars Symposium
        December 8, 2022
        Ingolstadt, Germany

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 70
          Total Downloads
        • Downloads (Last 12 months)19
        • Downloads (Last 6 weeks)1
        Reflects downloads up to 16 Nov 2024

        Other Metrics

        Citations

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media