Active Warning System for Highway-Rail Grade Crossings Using Connected Vehicle Technologies

Wang, Xu; Li, Jiangchen; Zhang, Can; Qiu, Tony Z.

doi:10.1155/2019/3219387

Cited by 11 publications

(12 citation statements)

References 14 publications

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“…Then, the traffic controller computed collisions and estimated the train arrival time and sent advisory speeds or stop commands to the vehicles depending on the train distance to the intersection and the vehicles' positions. In [133], researchers employed an audio visual CW framework for rail-highway crossing by using V2T and V2I communication. In case a crash with the train was predicted, speed deceleration is triggered for the vehicles to decrease the collision probability.…”

Section: A Safetymentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

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Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.INDEX TERMS Vehicular ad-hoc networks (VANETs), intersection management (IM), vehicle-tovehicle (V2V), vehicle-to-infrastructure (V2I), trajectory planning (TP), spatio-temporal (ST), connected autonomous vehicles (CAVs), vulnerable road users (VRUs), connected vehicles (CVs), autonomous vehicle (AVs). II. INTERSECTION MANAGEMENT (IM) FUNDAMENTALS A. ARCHITECTURE ALTERNATIVESASHKAN GHOLAMHOSSEINIAN (Graduate Student Member, IEEE) received the bachelor's degree in computer engineering-software in Iran, the M.Sc. degree in computer network engineering from Halmstad University, Sweden, and the Post master's degree in communications for intelligent transport systems from Eurecom, France. He is currently pursuing the Ph.D. degree in electrical engineering and information technology with the Communication Networks Group, Technische Universität Ilmenau, Germany. His research interests include vehicular networks, intelligent transport systems, computer networks, and wireless and mobile networks and communications. JOCHEN...

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Section: A Safetymentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

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“…Connected-vehicle applications that will alert or warn drivers approaching an at-grade railroad crossing if they are on a crash-imminent trajectory to collide with a crossing or approaching train ( 33 – 36 ) can help prevent crashes at railroad grade crossings.…”

Section: System Descriptionmentioning

confidence: 99%

Quantifying the Extent to Which Connected and Autonomous Vehicles Reduce Accidents at Railroad Grade Crossings: A Machine Learning Approach

Mathew

Benekohal

2022

Transportation Research Record: Journal of the Transportation R

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This paper quantifies the safety benefits of a proposed near real-time traffic control system for highway–rail grade crossing (HRGC) utilizing emerging safety technologies in connected and autonomous vehicles (CAV). The connected-vehicle technologies that have applications at a railroad crossing include vehicle-based technologies (railroad crossing violation warning, automated or semi-automated braking system, drowsiness/distracted driver alert) and technologies that require cooperation from the railroad industry (advanced warnings to trains about an occupied crossing). This paper provides a methodology to quantify the reduction in crashes as safety technologies become prevalent. It first identifies crash characteristics that enable the classification of crashes as preventable crashes or not. This classification is used to train machine learning models to estimate the likelihood of a potential crash being preventable. The machine learning model is used along with the zero inflated negative binomial with empirical Bayes system (ZINEBS) model to estimate the expected accident count at a crossing when a percentage of vehicles in the traffic stream is CAV. This paper presents case studies for three crossings to show the reduction in crashes with the increase in the percentage of connected vehicles in the traffic stream. It also presents the general trend in the reduction expected by analyzing 50 crossings of each warning device type.

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“…The study presented a train-tracking solution that could detect train arrivals and produce safety messages for AVs. Wang et al [141] proposed a new active warning system that is based on the readily available CV technologies to improve safety at HRGCs. The system relied on a wireless communication via the DSRC between the onboard equipment and road-side units to activate the warning messages due to approaching trains.…”

Section: Figure 17 Approaching Vehicles and Passing At At An Hrgcmentioning

confidence: 99%

“…The AV perception sensors alone may not be sufficient to detect an approaching train at unprotected HRGCs, especially under inclement weather conditions. 64 Wang et al ( 2019) [141] Proposed a new active CV-based warning system.…”

Section: Autonomous Trains and Highway-rail Grade Crossings 55mentioning

confidence: 99%

Deployment of Autonomous Trains in Rail Transportation: Current Trends and Existing Challenges

et al. 2021

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Automation is expected to effectively address the growing demand for passenger and freight transportation, safety issues, human errors, and increasing congestion. The growth of autonomous vehicles using the state-of-the-art connected vehicle technologies has paved the way for the development of passenger and freight autonomous trains (ATs), also known as driverless trains. ATs are fully automated trains that are centrally controlled using advanced communication and internet technologies, such as highspeed internet (5G) technology, Internet of Things, dedicated short range communications, digital video detection cameras, and artificial intelligence-based methods. The current study focuses on a detailed up-todate review of the existing trends, technologies, advancements, and challenges in the deployment of ATs with a full automation level in rail transportation. The basic AT features along with the key technologies that are instrumental for the AT deployment and operations are discussed in detail. Furthermore, a comprehensive evaluation of the state-of-the-art research efforts is performed as well with a specific emphasis on the issues associated with the AT deployment, user perception and outlook for ATs, innovative concepts and models that could be used for the AT design, and the AT operations at highway-rail grade crossings. Based on the conducted review, this study determines the main advantages and challenges from the AT deployment. The identified challenges have to be collaboratively addressed by the relevant stakeholders, including railroad companies, researchers, and government representatives, to facilitate the AT development and deployment considering the perspectives of future users and without affecting the safety level.INDEX TERMS Autonomous trains, driverless trains, connected vehicles, autonomous vehicles, automation trends, automation challenges.

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Active Warning System for Highway-Rail Grade Crossings Using Connected Vehicle Technologies

Cited by 11 publications

References 14 publications

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Quantifying the Extent to Which Connected and Autonomous Vehicles Reduce Accidents at Railroad Grade Crossings: A Machine Learning Approach

Deployment of Autonomous Trains in Rail Transportation: Current Trends and Existing Challenges

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