Evaluation of level 2 automated driving artificial intelligence readiness in simulated scenarios

David Tena Gago; Qi Wang; Jose M. Alcaraz Calero

doi:10.1145/3568160.3570232

Evaluation of level 2 automated driving artificial intelligence readiness in simulated scenarios

David Tena Gago^*
, Qi Wang
, Jose M. Alcaraz Calero

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

Original language	English
Title of host publication	ACM Computer Science in Cars Symposium (CSCS22)
Editors	Björn Brücher, Christoph Krauß, Mario Fritz, Hans-Joachim Hof, Oliver Wasenmüller
Publisher	Association for Computing Machinery
Pages	1-8
Number of pages	8
ISBN (Print)	9781450397865
DOIs	https://doi.org/10.1145/3568160.3570232
Publication status	Published - 8 Dec 2022
Event	6th ACM Computer Science in Cars Symposium: Artificial Intelligence and Security for Autonomous Vehicles - Technische Hochschule Ingolstadt, Ingolstadt, Germany Duration: 8 Dec 2022 → … https://acm-cscs.org/

Conference

Conference	6th ACM Computer Science in Cars Symposium
Abbreviated title	CSCS 2022
Country/Territory	Germany
City	Ingolstadt
Period	8/12/22 → …
Internet address	https://acm-cscs.org/

Keywords

perception
AI
camera-based detection
ADAS

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10.1145/3568160.3570232

2022 10 29 Tena et al Evaluation acceptedAccepted author manuscript, 5.86 MB

Cite this

Tena Gago, D., Wang, Q., & Alcaraz Calero, J. M. (2022). Evaluation of level 2 automated driving artificial intelligence readiness in simulated scenarios. In B. Brücher, C. Krauß, M. Fritz, H.-J. Hof, & O. Wasenmüller (Eds.), ACM Computer Science in Cars Symposium (CSCS22) (pp. 1-8). Article 3 Association for Computing Machinery. https://doi.org/10.1145/3568160.3570232

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title = "Evaluation of level 2 automated driving artificial intelligence readiness in simulated scenarios",

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.",

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publisher = "Association for Computing Machinery",

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note = "6th ACM Computer Science in Cars Symposium : Artificial Intelligence and Security for Autonomous Vehicles, CSCS 2022 ; Conference date: 08-12-2022",

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Tena Gago, D, Wang, Q & Alcaraz Calero, JM 2022, Evaluation of level 2 automated driving artificial intelligence readiness in simulated scenarios. in B Brücher, C Krauß, M Fritz, H-J Hof & O Wasenmüller (eds), ACM Computer Science in Cars Symposium (CSCS22)., 3, Association for Computing Machinery, pp. 1-8, 6th ACM Computer Science in Cars Symposium, Ingolstadt, Germany, 8/12/22. https://doi.org/10.1145/3568160.3570232

Evaluation of level 2 automated driving artificial intelligence readiness in simulated scenarios. / Tena Gago, David; Wang, Qi; Alcaraz Calero, Jose M.
ACM Computer Science in Cars Symposium (CSCS22). ed. / Björn Brücher; Christoph Krauß; Mario Fritz; Hans-Joachim Hof; Oliver Wasenmüller. Association for Computing Machinery, 2022. p. 1-8 3.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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Evaluation of level 2 automated driving artificial intelligence readiness in simulated scenarios

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