Autonomous Traffic System for Emergency Vehicles

Humayun, Mamoona; Almufareh, Maram Fahhad; Jhanjhi, N. Z.

doi:10.3390/electronics11040510

Cited by 36 publications

(14 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed solution was evaluated through mathematical modeling. The results show that the EVMS can reduce the travel times of EVs significantly without causing any performance degradation of normal vehicles (Humayun et al, 2022).…”

Section: Literaturementioning

confidence: 95%

“…We also benchmark various machine learning approaches that can determine the siren existence and sound source localization which includes direction and distance simultaneously within 50ms of latency. Authors in (Humayun et al, 2022) proposed system for Emergence Vehicles EV and minimally affects the travel times of other vehicles on the junction. In the presence of an EV in the communication range, the proposed system prioritizes the EV by creating space for it in the lane adjacent to the shoulder lane.…”

Section: Literaturementioning

confidence: 99%

See 1 more Smart Citation

A Transfer-Learning-Based Approach for Emergency Vehicle Detection

2022

EAJSE

View full text Add to dashboard Cite

The paper presents a computer-vision based approach for real-time detection of different types of emergency vehicles under heavy traffic conditions. This enables preferential path clearance for emergency vehicles by the traffic controller which has the potential of saving lives, properties and increasing the ability to prevent crimes and drastically reducing the total time required by an emergency vehicle to reach its target destination. The main challenge emergency vehicles faced in and around the cities is heavy traffic jams, which significantly hampers their operations resulting in a disastrous outcome. In most of the cities, emergency vehicles are equipped with unique colors and sound system which enable the traffic police to identify them. As our cities become smarter and transition into an era of artificial intelligence, the old system may not be sustainable due to that fact that it needs humans to constantly monitor emergency vehicle arrival at the intersections and also the sound produces by such vehicles may be nuisance and discomforting to the general public. This paper proposed a method of automatic detection of four different categories of emergency vehicle irrespective of the vehicle's shapes, models or manufacturers' using modified version of YOLOv5 object detection algorithm. YOLO is an acronym for (You Only Look Once) and it is an object detection algorithm that divides images into a grid system. Each cell within the grid is responsible for detecting objects within itself. YOLO is one of the most famous object detection algorithms due to its speed and accuracy. YOLO models are used for object detection with high performance which consists of 84 classes to detect and differentiate between 84 different objects. The proposed model developed here is based on 4 classes which are (Firetrucks, Ambulance, Police Car, and Normal Cars) classes. The top layers (fully connected layers) of the YOLO algorithm were re-designed and retrained to get new learned weights while freezing the bottom layers (convolutional layers) and retaining the pre-trained YOLOv5 weights. After retraining with the proposed modified YOLOv5, the model has shown promising results and quite impressive metrics in detecting and classifying emergency vehicles and normal vehicles. Using Mean Average Precision (mAP) metric, for police cars we achieved 98%, 96% for fire trucks, 89% for ambulances and 97% for normal cars.

show abstract

Section: Literaturementioning

confidence: 95%

Section: Literaturementioning

confidence: 99%

A Transfer-Learning-Based Approach for Emergency Vehicle Detection

2022

EAJSE

View full text Add to dashboard Cite

show abstract

“…In [ 24 ], they developed a side lane application so an emergency vehicle can pass without waiting at traffic lights. Normal vehicles are allowed to pass through this side lane only when an emergency vehicle arrives.…”

Section: Related Workmentioning

confidence: 99%

Development of a Smart Signalization for Emergency Vehicles

Ṣiddiqi

Alruwaili

Tarimer

et al. 2023

Sensors

View full text Add to dashboard Cite

As the population increases, the number of motorized vehicles on the roads also increases. As the number of vehicles increases, traffic congestion occurs. Traffic lights are used at road junctions, intersections, pedestrian crossings, and other places where traffic needs to be controlled to avoid traffic chaos. Due to traffic lights installed in the city, queues of vehicles are formed on the streets for most of the day, and many problems arise because of this. One of the most important problems is that emergency vehicles, such as ambulances, fire engines, police cars, etc., cannot arrive on time despite traffic priorities. Emergency vehicles such as hospitals and police departments need to reach the scene in a very short time. Time loss is a problem that needs to be addressed, especially for emergency vehicles traveling in traffic. In this study, ambulances, fire brigades, police, etc., respond to emergencies. A solution and a related application have been developed so privileged vehicles can reach their target destination as soon as possible. In this study, a route is determined between the current location of an emergency vehicle and its target location in an emergency. Communication between traffic lights is provided with a mobile application developed specifically for the vehicle driver. In this process, the person controlling the lights can turn on the traffic lights during the passage of vehicles. After the vehicles with priority to pass passed, traffic signaling was normalized via the mobile application. This process was repeated until the vehicle reached its destination.

show abstract

“…The networks have the potential to save lives as well as generally improve vehicle and road safety [5]. If the networks were widely adopted, the convenience and comfort of passengers and drivers might also be improved.…”

Section: Next Generation Networkmentioning

confidence: 99%

Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles

Brocklehurst

Radenkovic

2022

JSAN

View full text Add to dashboard Cite

The increased interest in autonomous vehicles has led to the development of novel networking protocols in VANETs In such a widespread safety-critical application, security is paramount to the implementation of the networks. We view new autonomous vehicle edge networks as opportunistic networks that bridge the gap between fully distributed vehicular networks based on short-range vehicle-to-vehicle communication and cellular-based infrastructure for centralized solutions. Experiments are conducted using opportunistic networking protocols to provide data to autonomous trams and buses in a smart city. Attacking vehicles enter the city aiming to disrupt the network to cause harm to the general public. In the experiments the number of vehicles and the attack length is altered to investigate the impact on the network and vehicles. Considering different measures of success as well as computation expense, measurements are taken from all nodes in the network across different lengths of attack. The data gathered from each node allow exploration into how different attacks impact metrics including the delivery probability of a message, the time taken to deliver and the computation expense to each node. The novel multidimensional analysis including geospatial elements provides evidence that the state-of-the-art MaxProp algorithm outperforms the benchmark as well as other, more complex routing protocols in most of the categories. Upon the introduction of attacking nodes however, PRoPHET provides the most reliable delivery probability when under attack. Two different attack methods (black and grey holes) are used to disrupt the flow of messages throughout the network and the more basic protocols show that they are less consistent. In some metrics, the PRoPHET algorithm performs better when under attack due to the benefit of reduced network traffic.

show abstract

Autonomous Traffic System for Emergency Vehicles

Cited by 36 publications

References 41 publications

A Transfer-Learning-Based Approach for Emergency Vehicle Detection

A Transfer-Learning-Based Approach for Emergency Vehicle Detection

Development of a Smart Signalization for Emergency Vehicles

Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles

Contact Info

Product

Resources

About