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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationACM Computer Science in Cars Symposium (CSCS22)
Publication statusAccepted/In press - 29 Oct 2022
Event6th 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

Conference6th ACM Computer Science in Cars Symposium
Abbreviated titleCSCS 2022
Country/TerritoryGermany
CityIngolstadt
Period8/12/22 → …
Internet address

Keywords

  • perception
  • AI
  • camera-based detection
  • ADAS

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