The Experience of DRIVERTIVE-DRIVERless cooperaTIve VEhicle-Team in the 2016 GCDC

Alonso, Ignacio Parra; Gonzalo, Rubén Izquierdo; Alonso, Javier; Garcia-Morcillo, Alvaro; Llorca, David Fernández; Sotelo, Miguel Ángel

doi:10.1109/tits.2017.2749963

Cited by 22 publications

(12 citation statements)

References 19 publications

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“…The experiments were performed using UAH's DRIVERTIVE vehicle [29]. DRIVERTIVE is a mechanically automated vehicle that carries multiple sensors for environment perception, such as an HDL-32E LiDAR, 3 RADARs, and multiple cameras.…”

Section: Overview Of Predictive Perception Systemsmentioning

confidence: 99%

Testing predictive automated driving systems: lessons learned and future recommendations

Gonzalo¹,

Salinas²,

Alonso³

et al. 2022

Preprint

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Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow to evaluate safety with real behaviors for critical and edge cases, nor to evaluate the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predictive systems to anticipate future actions and motions to be considered in the path planning layer. In this paper, we present and analyze the results of physical tests on proving grounds of several predictive systems in automated driving functions developed within the framework of the BRAVE project. Based on our experience in testing predictive automated driving functions, we identify the main limitations of current physical testing approaches when dealing with predictive systems, analyze the main challenges ahead, and provide a set of practical actions and recommendations to consider in future physical testing procedures for automated and autonomous driving functions.

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Section: Overview Of Predictive Perception Systemsmentioning

confidence: 99%

Testing predictive automated driving systems: lessons learned and future recommendations

Gonzalo¹,

Salinas²,

Alonso³

et al. 2022

Preprint

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“…In [9] provides a general overview of the different projects used to adapt a factory vehicle, without access to low-level control systems, into a fully automated cooperative vehicle suitable to compete in GCDC2016. Communication and data degradation have been combined and validated experimentally in real-world scenarios, together with other vehicles with different implementations.…”

Section: Related Workmentioning

confidence: 99%

Efficient Management of Road Intersections for Automated Vehicles—The FRFP System Applied to the Various Types of Intersections and Roundabouts

2019

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In the last decade, automatic driving systems for vehicles circulating on public roads have become increasingly closer to reality. There is always a strong interest in this topic among research centers and car manufacturers. One of the most critical aspects is the management of intersections, i.e., who will have to go first and in what ways? This is the question we want to answer through this research. Clearly, the goal is to manage the intersection safely, making it possible to reduce road congestion, travel time, emissions, and fuel consumption as much as possible. The research is conducted by comparing a new management system with the systems already known in the state of the art for different types of intersections. The new system proposed by us is called FRFP (first to reach the end of the intersection first to pass). In particular, vehicles will increase or decrease their speed in collaboration with each other by making the right decision. The vehicle that can potentially reach the intersection exit first.

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“…A survey by the American Automobile Association's Traffic Safety Foundation found that 16–21% of traffic accidents were caused by driver fatigue [ 1 ]. According to Ammour et al, the probability of a traffic accident caused by driver fatigue is 46 times that of normal driving [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN

Zhen

Zhou

Zhang

et al. 2020

Computational Intelligence and Neuroscience

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With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and the region of interest (ROI) is extracted using feature points. A convolutional neural network, named EM-CNN, is proposed to detect the states of the eyes and mouth from the ROI images. The percentage of eyelid closure over the pupil over time (PERCLOS) and mouth opening degree (POM) are two parameters used for fatigue detection. Experimental results demonstrate that the proposed EM-CNN can efficiently detect driver fatigue status using driving images. The proposed algorithm EM-CNN outperforms other CNN-based methods, i.e., AlexNet, VGG-16, GoogLeNet, and ResNet50, showing accuracy and sensitivity rates of 93.623% and 93.643%, respectively.

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The Experience of DRIVERTIVE-DRIVERless cooperaTIve VEhicle-Team in the 2016 GCDC

Cited by 22 publications

References 19 publications

Testing predictive automated driving systems: lessons learned and future recommendations

Testing predictive automated driving systems: lessons learned and future recommendations

Efficient Management of Road Intersections for Automated Vehicles—The FRFP System Applied to the Various Types of Intersections and Roundabouts

Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN

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