The correlation between vehicle vertical dynamics and deep learning-based visual target state estimation: A sensitivity study

Yannik Weber, Stratis Kanarachos

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Automated Vehicles will provide greater transport convenience and interconnectivity, increase mobility options to young and elderly people, and reduce traffic congestion and emissions. However, the largest obstacle towards the deployment of Automated Vehicles on public roads is their safety evaluation and validation. Undeniably, the role of cameras and Artificial Intelligence-based (AI) vision is vital in the perception of the driving environment and road safety. Although a significant number of studies on the detection and tracking of vehicles have been conducted, none of them focused on the role of vertical vehicle dynamics. For the first time, this paper analyzes and discusses the influence of road anomalies and vehicle suspension on the performance of detecting and tracking driving objects. To this end, we conducted an extensive road field study and validated a computational tool for performing the assessment using simulations. A parametric study revealed the cases where AI-based vision underperforms and may significantly degrade the safety performance of AVs.

Original languageEnglish
Article number4870
Number of pages28
JournalSensors (Switzerland)
Volume19
Issue number22
DOIs
Publication statusPublished - 8 Nov 2019

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Keywords

  • Automated Vehicles
  • Distance estimation
  • Object detection
  • Object tracking
  • Road anomalies
  • Road bumps

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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