Loose Animal-Vehicle Accidents Mitigation: Vision and Challenges

Saad, Waleed; Alsayyari, Abdulaziz

doi:10.1109/itce.2019.8646591

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…However, the paper also highlighted the harm and disruption to the ecosystem caused by fencing, and proposed automated gates that open based on proximity of radio signals sent by collars on the camels. In [11], the authors proposed a road-based system consisting of wireless IR sensors connected to a sink node. The sensors are arranged in clusters along the sides of the road and each node is equipped with a thermal camera and ultrasonic sensor.…”

Section: Related Workmentioning

confidence: 99%

Spot-the-Camel: Computer Vision for Safer Roads

AlNujaidi¹,

AlHabib²,

Alodhieb³

2023

IJAIA

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As the population grows and more land is being used for urbanization, ecosystems are disrupted by our roads and cars. This expansion of infrastructure cuts through wildlife territories, leading to many instances of Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue that is having a global socio-economic impact, resulting in billions of dollars in property damage and, at times, fatalitiesfor vehicle occupants. In Saudi Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC) being particularly deadly due to the large size of camels, which results in a 25% fatality rate [1].The focus of this work is to test different object detection models on the task of detecting camels on theroad. The Deep Learning (DL) object detection models used in the experiments are: CenterNet, Efficient Det, Faster R-CNN, SSD, and YOLOv8. Results of the experiments show that YOLOv8 performed the best in terms of accuracy and was the most efficient in training. In the future, the plan is to expand on this work by developing a system to make countryside roads safer.

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Section: Related Workmentioning

confidence: 99%

Spot-the-Camel: Computer Vision for Safer Roads

AlNujaidi¹,

AlHabib²,

Alodhieb³

2023

IJAIA

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“…Therefore, the active take-over approach is an issue that must be studied in the development of automated driving technology. As the number of vehicles increases, the number of vehicle-animal accidents on the highway also increases [37]. Due to the constraints of existing technology, it is difficult for fast moving vehicles on motorways to accurately identify animals and alert drivers [38].…”

Section: Take-over Modementioning

confidence: 99%

The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over

et al. 2023

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This paper investigates the effects of non-driving related tasks, take-over request time, and take-over mode interactions on take-over performance in human–machine cooperative driving in a highway environment. Based on the driving simulation platform, a human–machine collaborative driving simulation experiment was designed with various take-over quality influencing factors. The non-driving related tasks included no task, listening to the radio, watching videos, playing games, and listening to the radio and playing games; the take-over request time was set to 6, 5, 4, and 3 s, and the take-over methods include passive and active take-over. Take-over test data were collected from 65 drivers. The results showed that different take-over request times had significant effects on driver take-over performance and vehicle take-over steady state (p < 0.05). Driver reaction time and minimum TTC decreased with decreasing take-over request time, maximum synthetic acceleration increased with decreasing take-over request time, accident rate increased significantly at 3 s take-over request time, and take-over safety was basically ensured at 4 s request time. Different non-driving related tasks have a significant effect on driver take-over performance (p < 0.05). Compared with no task, non-driving related tasks significantly increase driver reaction time, but they only have a small effect on vehicle take-over steady state. Vehicle take-over mode has a significant effect on human–machine cooperative driving take-over quality; compared with passive take-over mode, the take-over quality under active take-over mode is significantly lower.

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“…There are special categories of vehicle detection systems available in the market, which only identifies the vehicles based on the specifications loaded into their database [23]. These systems cannot detect animals.…”

Section: A Road-based Technologiesmentioning

confidence: 99%

Implementing a Safe Travelling Technique to Avoid the Collision of Animals and Vehicles in Saudi Arabia

Jadi¹

2019

IJACSA

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In this work, a safe travelling technique was proposed and implemented a LoRa based application to avoid the collision of animals with vehicles on the highways of Saudi Arabia. For the last few decades, it has been a great challenge for the authorities to secure the life of animals and human being on the roads due to the sudden passage of animals on the highways. In such situations, drivers are not aware of the animal movement, and serious damage is observed with the life of both humans and animals. A LoRaWAN based architecture with a variety of advantages towards low cost and high accuracy of finding the movement of animals is possible with the proposed method and could deliver good results as well. The accuracy of this method was improved to a maximum extent as compared to the existing system due to the usage of LoRa sensors implanted in the animal's skin to trace with the nodes and base stations easily.

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Loose Animal-Vehicle Accidents Mitigation: Vision and Challenges

Cited by 9 publications

References 14 publications

Spot-the-Camel: Computer Vision for Safer Roads

Spot-the-Camel: Computer Vision for Safer Roads

The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over

Implementing a Safe Travelling Technique to Avoid the Collision of Animals and Vehicles in Saudi Arabia

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