Effects of Adjacent Vehicles on Judgments of a Lead Car During Car Following

Levulis, Samuel J.; DeLucia, Patricia R.; Oberfeld, Daniel

doi:10.1177/0018720816652270

Cited by 6 publications

(1 citation statement)

References 65 publications

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“…With the rapid development of autonomous driving technology [1][2][3], accurate and timely risk detection has become a crucial factor in ensuring road safety. Driving environments are highly dynamic and complex, presenting significant challenges [4][5][6][7] to traditional risk assessment methods, especially in real-time data processing and understanding complex scenarios. The YOLOv8 series has made significant strides in object detection, improving both detection speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Visual Risk Warning System for Autonomous Driving

Qiu,

Tang,

et al. 2024

Preprint

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In autonomous driving, the identification and tracking of multiple vehicles on the road are critical tasks. This paper aims to develop a risk warning system using deep learning algorithms to address the heterogeneous, high-dynamic, and complex driving environments. To enhance the generalization capability and detection accuracy of small objects in road perception, we propose a novel VBFNet-YOLOv8 algorithm for real-time vehicle identification, tracking, distance measurement, and speed estimation. Specifically, we replace the Backbone of the original YOLOv8 network with the VanillaNet structure and upgrade the traditional PANet in the neck part to Bi-FPN. By integrating the optimized YOLOv8n algorithm with Deepsort and TTC algorithms, we achieve a comprehensive road risk assessment. The algorithm continuously tracks the targets, and the TTC algorithm intuitively assesses the risk. Finally, the system provides layered warnings by changing the color of the bounding boxes, offering drivers an integrated and real-time risk alert. Comparative experimental results show that the optimized algorithm improves Precision by 0.61%, mAP@0.5 by 0.63%, and mAP@0.5:0.95 by 0.70%. In the road tests on sections A and B, the detection frame rate of the risk warning system maintained a minimum of 37.1fps and a maximum of 56.4fps. The detection Confidence of various objects remained above 0.67, reaching up to 0.97.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-based Visual Risk Warning System for Autonomous Driving

Qiu,

Tang,

et al. 2024

Preprint

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Does automated driving affect time-to-collision judgments?

Lodinger

DeLucia

2019

Transportation Research Part F: Traffic Psychology and Behaviou

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Analysis of visual risk perception model for braking control behaviour of human drivers: A literature review

Liu

Wang

Nacpil³

et al. 2022

IET Intelligent Trans Sys

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In the interest of driving safety, it is essential to understand driver risk assessment to develop human-centred automated driving systems. Visual perception plays a core role in observing, analysing, and predicting driving crash risks. Furthermore, comprehensive progress of visual perception has been achieved in the development of risk perception metrics. However, it is still of interest to understand driver control behaviours, which depend on visual perception. Therefore, the objective of this literature review is to assess the potential of coupling time-remaining cues for risk perception and braking control behaviour. First, conventional risk perception metrics are classified based on trajectory, contact, and state principles, along with new analysis metrics that have been derived from conventional ones. Then, the analysis metrics are discussed based on visual perception information. Next, driver risk assessment is summarised according to the environment, driver, and object characteristics. Moreover, braking behaviour models are compared in relation to visual perception variables, action affordances, and behaviour dynamics. The current review further discusses the possibility of applying driver behaviour modelling to perception, navigation, and spatial awareness in autonomous vehicles. This human-centred approach has the potential to improve interaction between drivers and automation.

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Effects of Adjacent Vehicles on Judgments of a Lead Car During Car Following

Abstract: Results suggest that nearby traffic can affect a driver's ability to accurately judge a lead car's motion in situations that pose risk for rear-end collisions.

Cited by 6 publications

References 65 publications

Deep Learning-based Visual Risk Warning System for Autonomous Driving

Deep Learning-based Visual Risk Warning System for Autonomous Driving

Does automated driving affect time-to-collision judgments?

Analysis of visual risk perception model for braking control behaviour of human drivers: A literature review

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