AI based emergency vehicle priority system

Patel, Rutwik; Mange, Suraj; Mulik, Simran; Mehendale, Ninad

doi:10.1007/s42486-022-00093-7

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…In Stage 2, EMVPS commonly employ light/infrared-based [80], sound-based [81], fixed sensors-based [82], radio-based [76,83], GPS-based [84,85], and VANET-based [71,86] solutions to detect the EMV-TPC demand within the control area, as shown in Fig- ure 5. Upon detection of the entry of an EMV or receiving a request from an EMV, the traffic signal controller (TSC) can be quickly activated to remain or turn green upon confirmation of the request.…”

Section: Stage 2: Detectionmentioning

confidence: 99%

“…However, in current realistic scenarios, there are often a number of constraints, including obstructed dissemination of warning L&S and the inability of SV drivers to be informed of the exact yielding requirements (i.e., when to give way and which lane to leave) in a timely manner. As a result, several studies have focused on the early clearance of specific lanes to ensure that the expected speed of EMVs on certain road links can be maintained [71,81,100].…”

Section: Setting Up Emv Lanesmentioning

confidence: 99%

“…The proposed strategy was simulated in the VISSIM. Patel et al [81] proposed a framework that identified ambulances based on a neural network and helped set up a makeshift EMV-dedicated lane on road links along the rescue route. Su et al [71] calculated the emergency capacity of the road link by the segment's shoulder width, lane width, and other factors in order to determine whether an EMV lane could be set up or not.…”

Section: Setting Up Emv Lanesmentioning

confidence: 99%

See 2 more Smart Citations

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Hao,

Wang,

Yang

2024

Sustainability

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Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment.

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Section: Stage 2: Detectionmentioning

confidence: 99%

Section: Setting Up Emv Lanesmentioning

confidence: 99%

Section: Setting Up Emv Lanesmentioning

confidence: 99%

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Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Hao,

Wang,

Yang

2024

Sustainability

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“…Patel et al [27] introduced an Artificial Intelligence (AI) based system for prioritising emergency vehicles. The proposed system aims to improve emergency services by using a visual indicator to identify traffic congestion and provide alternate vehicle routes.…”

Section: Related Workmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

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Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

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AI based emergency vehicle priority system

Cited by 2 publications

References 15 publications

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

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