Decentralized Emergency Service Vehicle Pre-Emption System Using RF Communication and GNSS-Based Geo-Fencing

Rosayyan, Prakash; Subramaniam, Senthilkumar; Ilango, G. Saravana

doi:10.1109/tits.2020.3007671

Cited by 19 publications

(17 citation statements)

References 25 publications

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“…If an EV is given preemption when driving on a low-priority call, it can cause unnecessary delay for the surrounding traffic. The risks of negative effects on the surrounding traffic must therefore be considered when implementing EVP ( 52 – 54 ). One method to mitigate the negative impact on the surrounding traffic is to consider the traffic density for each direction of the intersection ( 55 ).…”

Section: Resultsmentioning

confidence: 99%

Potential of Cooperative Intelligent Transport System Services to Mitigate Risk Factors Associated With Emergency Vehicle Accidents

Weibull

Lidestam

Prytz

2022

Transportation Research Record: Journal of the Transportation R

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Emergency vehicles (EVs) are at high risk of accidents during emergency driving. To make use of countermeasures to mitigate these risks, it is important to understand under what circumstances EV-related accidents occur. The common risk factors for EV-related accidents were examined through a systematic literature review. A total of 22 articles were examined for risk factors associated with EV-related accidents. The most reported risk factors were, in order of frequency, intersections, daytime, dry roads, clear weather, urban roads, traffic signals, and angular collisions. The articles were also reviewed for suggested countermeasures to mitigate the risk factors. The most commonly suggested countermeasures were driver training for EV operators, educating the public, exercising caution at intersections, wearing a seatbelt, and intelligent vehicle technologies. Cooperative intelligent transport systems (C-ITSs) have the potential to mitigate the risks of EV-related accidents. Therefore, three C-ITS services were investigated: EV approaching, EV preemption, and geofencing. They could all be used to inform, warn, or control aspects of driving. Each suggested service has the potential to decrease risk factors for EV-related accidents. The current literature review provides guidance on under what circumstance and in what form C-ITSs could be beneficial to prevent EV-related accidents. Further research is needed to examine behavior when drivers are introduced to C-ITSs.

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Section: Resultsmentioning

confidence: 99%

Potential of Cooperative Intelligent Transport System Services to Mitigate Risk Factors Associated With Emergency Vehicle Accidents

Weibull

Lidestam

Prytz

2022

Transportation Research Record: Journal of the Transportation R

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“…By contrast, the existing traffic signal control studies for EVs are still limited to the rule-based methods [14]- [16]. Kang et al [14] propose an EV signal coordination approach to provide "green wave" for EVs.…”

Section: Road-centric Schedulingmentioning

confidence: 99%

“…Younes et al [15] design a real-time dynamic traffic signal control method which can handle the presence of one or more EVs over the road networks. Rosayyan et al [16] leverage a global navigation satellite system based on geo-fencing techniques to identify the entry of EVs and provide green signal automatically. However, these rule-based methods rarely consider the impact of scheduling strategy on OVs and cannot interact with the environment in real-time.…”

Section: Road-centric Schedulingmentioning

confidence: 99%

Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

Ding¹,

Zhang²,

Wang³

et al. 2022

Preprint

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The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines.

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“…The work in [11] proposes a unique decentralized approach to geofencing based on radio frequency and global navigation satellite system that allows emergency service vehicles to pass through intersections with the least amount of delays by giving them the right of way green signal. In the same context [12] proposed a cooperative vehicle-road scheduling method that includes a real-time route planning module and a group traffic signal management module.…”

Section: A Trajectory Prediction For Emergency Vehiclesmentioning

confidence: 99%

Smart System for Emergency Traffic Recommendations : Urban Ambulance Mobility

Charef¹,

Jarir²,

Quafafou³

2022

IJACSA

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With the increasing evolution of advanced technologies and techniques such as the Internet of Things, Artificial Intelligence and Big Data, the traffic management systems industry has acquired new methodologies for creating advanced and intelligent services and applications for traffic management and safety. The current contribution focuses on the implementation of a path recommendation service for paramedics in emergency situations, which is one of the most critical and complex issues in traffic management for the survival of individuals involved in emergency incidents. This work mainly focused on the response time to life-threatening incidents, which is an indicator for emergency ambulance services and for recommending a fastest ambulance route. To this end, we propose a hybrid approach consisting on a local approach using machine learning techniques to predict the congestion of different sections of a map from an origin to a destination, and a global approach to suggest the fastest path to ambulance drivers in real time as they move in OpenStreetMap.

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Decentralized Emergency Service Vehicle Pre-Emption System Using RF Communication and GNSS-Based Geo-Fencing

Cited by 19 publications

References 25 publications

Potential of Cooperative Intelligent Transport System Services to Mitigate Risk Factors Associated With Emergency Vehicle Accidents

Potential of Cooperative Intelligent Transport System Services to Mitigate Risk Factors Associated With Emergency Vehicle Accidents

Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

Smart System for Emergency Traffic Recommendations : Urban Ambulance Mobility

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