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The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study

Research Institute Future Transport and Cities, Coventry University, Priory Street, Coventry CV1 5FB, UK
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Sensors 2019, 19(22), 4870; https://doi.org/10.3390/s19224870
Received: 8 October 2019 / Revised: 2 November 2019 / Accepted: 4 November 2019 / Published: 8 November 2019
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: automated vehicles; object detection; object tracking; distance estimation; road anomalies; road bumps automated vehicles; object detection; object tracking; distance estimation; road anomalies; road bumps
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Weber, Y.; Kanarachos, S. The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study. Sensors 2019, 19, 4870.

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