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Objective: This study investigated drivers' move-over behavior when receiving an Emergency Vehicle Approaching (EVA) warning. Furthermore, the possible effects of false alarms, driver experience, and modality on move-over behavior were explored. Background: EVA warnings are one solution to encourage drivers to move over for emergency vehicles in a safe and timely manner. EVA warnings are distributed based on the predicted path of the emergency vehicle causing a risk of false alarms. Previous EVA studies have suggested a difference between inexperienced and experienced drivers' move-over behavior. Method: A driving simulator study was conducted with 110 participants, whereof 54 inexperienced and 56 experienced drivers. They were approached by an emergency vehicle three times. A control group received no EVA warnings, whereas the experimental groups received either true or false warnings, auditory or visual, 15 seconds before the emergency vehicle overtook them. Results: Drivers who received EVA warnings moved over more quickly for the emergency vehicle compared to the control group. Drivers moved over more quickly for each emergency vehicle interaction. False alarms impaired move-over behavior. No difference in driver behavior based on driver experience or modality was observed. Conclusion: EVA warnings positively affect drivers' move-over behavior. However, false alarms can decrease drivers' future willingness to comply with the warning. Application: The findings regarding measurements of delay can be used to optimize the design of future EVA systems. Moreover, this research should be used to further understand the effect of false alarms in in-car warnings. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Disasters often require large-scale evacuations, and damage key infrastructure (e.g., power, transportation). With growing electric vehicle (EV) adoption and electrification of transportation, governments and utilities may face significant power challenges during disasters, especially during the evacuation stage. Low state-of-charge, sporadic charging infrastructure, or power outages could significantly hamper safe and effective evacuations. Yet, EVs also offer possible resilience benefits to emergency response by more easily charging electronics or sending power back to the grid through vehicle-to-grid (V2G) technology. This paper focuses on the opportunities, benefits, and drawbacks of EVs in disasters and evacuations through a systematic review of current literature, reports, and sources. Overall, this review discovered EVs show promise as modes of transportation and mobile energy supply units. However, crucial challenges such as charging infrastructure locations, upfront cost of resilience technologies, and user behavior necessitate more dedicated research to overcome shortcomings and guide more realistic implementation of benefits. [ABSTRACT FROM AUTHOR]

Brain–computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator's electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to emergency vehicle classification that leverages a comprehensive set of informative audio features to distinguish between ambulance sirens, fire truck sirens, and traffic noise. A unique contribution lies in combining time domain features, including root mean square (RMS) and zero-crossing rate, to capture the temporal characteristics, like signal energy changes, with frequency domain features derived from short-time Fourier transform (STFT). These include spectral centroid, spectral bandwidth, and spectral roll-off, providing insights into the sound's frequency content for differentiating siren patterns from traffic noise. Additionally, Mel-frequency cepstral coefficients (MFCCs) are incorporated to capture the human-like auditory perception of the spectral information. This combination captures both temporal and spectral characteristics of the audio signals, enhancing the model's ability to discriminate between emergency vehicles and traffic noise compared to using features from a single domain. A significant contribution of this study is the integration of data augmentation techniques that replicate real-world conditions, including the Doppler effect and noise environment considerations. This study further investigates the effectiveness of different machine learning algorithms applied to the extracted features, performing a comparative analysis to determine the most effective classifier for this task. This analysis reveals that the support vector machine (SVM) achieves the highest accuracy of 99.5%, followed by random forest (RF) and k-nearest neighbors (KNNs) at 98.5%, while AdaBoost lags at 96.0% and long short-term memory (LSTM) has an accuracy of 93%. We also demonstrate the effectiveness of a stacked ensemble classifier, and utilizing these base learners achieves an accuracy of 99.5%. Furthermore, this study conducted leave-one-out cross-validation (LOOCV) to validate the results, with SVM and RF achieving accuracies of 98.5%, followed by KNN and AdaBoost, which are 97.0% and 90.5%. These findings indicate the superior performance of advanced ML techniques in emergency vehicle classification. [ABSTRACT FROM AUTHOR]

Scheduling decisions for concurrent emergency response (CER) across multiple disaster sites presents numerous difficulties. The main challenge is to minimize human casualties while taking into account the rationality of resource allocation across different disaster sites. This paper establishes a joint scheduling model for emergency vehicles on freeways in the context of CER. The model aims to minimize the transportation time, dispatch cost, and casualty risk, by using the resource site scheduling scheme as the decision variable, addressing multiple disaster and resource sites. Specifically, a casualty risk function based on the rescue waiting time was designed to balance the competing needs among various disaster sites, enhance equitable resource allocation, and reduce the probability of casualties. To achieve global convergence in a high-dimensional solution space, a tailored heuristic algorithm called adaptive dual evolutionary particle swarm optimization (ADEPSO) is proposed. The numerical results show that the scheduling scheme proposed by the ADEPSO algorithm satisfies all constraints and demonstrates significant advantages in large-sized instances. Compared to the two basic algorithms, ADEPSO provides a more cost-effective scheme and reduces the average rescue waiting time. Moreover, integrating the casualty risk function significantly decreases the average rescue waiting time at both high- and low-priority disaster sites, thereby directly lowering the casualty risk. [ABSTRACT FROM AUTHOR]

The rise in autonomous vehicles further impacts road networks and driving conditions over the road networks. Cameras and sensors allow these vehicles to gather the characteristics of their surrounding traffic. One crucial factor in this environment is the appearance of emergency vehicles, which require special rules and priorities. Machine learning and deep learning techniques are used to develop intelligent models for detecting emergency vehicles from images. Vehicles use this model to analyze regularly captured road environment photos, requiring swift actions for safety on road networks. In this work, we mainly developed a Generative Adversarial Network (GAN) model that generates new emergency vehicles. This is to introduce a comprehensive expanded dataset that assists emergency vehicles detection and classification processes. Then, using Convolutional Neural Networks (CNNs), we constructed a vehicle detection model demonstrating satisfactory performance in identifying emergency vehicles. The detection model yielded an accuracy of 90.9% using the newly generated dataset. To ensure the reliability of the dataset, we employed 10-fold cross-validation, achieving accuracy exceeding 87%. Our work highlights the significance of accurate datasets in developing intelligent models for emergency vehicle detection. Finally, we validated the accuracy of our model using an external dataset. We compared our proposed model's performance against four other online models, all evaluated using the same external dataset. Our proposed model achieved an accuracy of 85% on the external dataset. [ABSTRACT FROM AUTHOR]

The technical feasibility of solar photovoltaic (PV) direct current (DC) nanogrids is well established, but the components of nanogrids are primarily commercially focused on alternating current (AC)-based systems. Thus, DC converter-based designs at the system level require personnel with high degree of technical knowledge, which results in high costs. To enable a democratization of the technology by reducing the costs, this study provides a novel modular plug-and-play open-source DC nanogrid. The system can be customized according to consumer requirements, enabling the supply of various voltage levels to accommodate different device voltage needs. The step-by-step design process of the converter, controller, data logger, and assembly of the complete system is provided. A time-domain simulation and stability analysis of the designed system were conducted in MATLAB/Simulink (version 2024b) as well as experimental validation. The results show that transforming the nanogrid from a distribution network to a device makes it suitable for various user-specific applications, such as remotely supplying power to campsites, emergency vehicles like ambulances, and small houses lacking grid electricity. The modular DC nanogrid includes all the features available in a DC distribution network, as well as data logging, which enhances the user experience and promotes the use of solar-powered DC grid systems. [ABSTRACT FROM AUTHOR]

In the automotive industry, intelligent monitoring systems for advanced human-vehicle interaction aimed at enhancing the safety of drivers and passengers represent a rapidly growing area of research. Safe driving behavior relies on the driver's awareness of the road context, enabling them to make appropriate decisions and act consistently in anomalous circumstances. A potentially dangerous situation can arise when an emergency vehicle rapidly approaches with sirens blaring. In such cases, it is crucial for the driver to perform the correct maneuvers to prioritize the emergency vehicle. For this purpose, an Advanced Driver Assistance System (ADAS) can provide timely alerts to the driver about an approaching emergency vehicle. In this work, we present a driver-assistance prototype that leverages multimodal information from an integrated audio and video monitoring system. In the initial stage, sound analysis technologies based on computational audio processing are employed to recognize the proximity of an emergency vehicle based on the sound of its siren. When such an event occurs, an in-vehicle monitoring system is activated, analyzing the driver's facial patterns using deep-learning-based algorithms to assess their awareness. This work illustrates the design of such a prototype, presenting the hardware technologies, the software architecture, and the deep-learning algorithms for audio and video data analysis that make the driver-assistance prototype operational in a commercial car. At this initial experimental stage, the algorithms for analyzing the audio and video data have yielded promising results. The area under the precision-recall curve for siren identification stands at 0.92, while the accuracy in evaluating driver gaze orientation reaches 0.97. In conclusion, engaging in research within this field has the potential to significantly improve road safety by increasing driver awareness and facilitating timely and well-informed reactions to crucial situations. This could substantially reduce risks and ultimately protect lives on the road. [ABSTRACT FROM AUTHOR]

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology. [ABSTRACT FROM AUTHOR]

Emergency Vehicle Preemption (EVP) system plays an important role in reducing the response time of emergency vehicles by around 15–50%. Emergency Vehicle location estimation during Global Positioning System (GPS) outages is a challenging task in EVP. To address this issue, a location estimation scheme has been proposed by applying deep output kernel learning and extended Kalman filter using Inertial Measurement Unit (IMU) dead reckoning. The proposed scheme reduces the error in the estimation of velocity, attitude, and position by dynamically adapting the noise parameters of the extended Kalman filter. This system provides navigation solutions for emergency vehicles. Initially, four test routes were selected and the Position, velocity and attitude (pitch and roll angle) were measured and applied to the proposed scheme. Training and testing were conducted for the measured datasets. The performance of the proposed scheme was measured using five statistical methods during training and testing, and a comparison was made with other existing methods. The simulation results show that the proposed scheme performed well, in the four test routes. Finally, two different case studies were conducted using the proposed scheme and the performance was compared with three other methods. According to the results, the proposed scheme showed improvements in detection accuracy compared to the existing methods during GPS outages of 71.94% and 62.83% for Trajectory-1 and Trajectory-2, respectively. [ABSTRACT FROM AUTHOR]

The global rising level of climate change has caused significant disruptions in city traffic patterns due to intense heavy rains causing urban waterlogging disasters worldwide. These disasters have also resulted in massive economic losses and casualties. In consideration of these issues, the current challenge for urban disaster emergency response is to determine strategies to arrange for emergency vehicle scheduling as soon as urban waterlogging expands in a way that minimizes casualties and financial losses. This article proposes the Locally Constraint Evolutionary algorithm for Vehicle Evacuation Scheduling (LCEVES), which includes the following features to address the vehicle emergency evacuation problems in urban waterlogging zones specifically for regulated vehicles (e.g., buses, vehicles transporting hazardous materials, etc.). Firstly, a general technical route for vehicle emergency evacuation under urban waterlogging risk, secondly an optional routes search mechanism for vehicle evacuation in the waterlogging area and thirdly a locally constraint evolutionary algorithm for vehicle evacuation in the waterlogging area. The experiments show that LCEVES improves vehicle evacuation efficiency and reduces the risk of affected vehicles, providing an effective means for vehicle evacuation in the waterlogging zone. This research provides an effective method for solving vehicle evacuation scheduling challenges during urban waterlogging and also has significance for vehicle scheduling in other types of disasters. [ABSTRACT FROM AUTHOR]

How to free a road from vehicle traffic as efficiently as possible and in a given time, in order to allow for example the passage of emergency vehicles? We are interested in this question which we reformulate as an optimal control problem. We consider a macroscopic road traffic model on networks, semi-discretized in space and decide to give ourselves the possibility to control the flow at junctions. Our target is to smooth the traffic along a given path within a fixed time. A parsimony constraint is imposed on the controls, in order to ensure that the optimal strategies are feasible in practice. We perform an analysis of the resulting optimal control problem, proving the existence of an optimal control and deriving optimality conditions, which we rewrite as a single functional equation. We then use this formulation to derive a new mixed algorithm interpreting it as a mix between two methods: a descent method combined with a fixed point method allowing global perturbations. We verify with numerical experiments the efficiency of this method on examples of graphs, first simple, then more complex. We highlight the efficiency of our approach by comparing it to standard methods. We propose an open source code implementing this approach in the Julia language. [ABSTRACT FROM AUTHOR]

Electric vehicles are in vogue nowadays and it has been gaining some ground in India after Tesla entered into the Indian automobile market. Electric vehicles do not require conventional fuel sources such as fuel, oil, etc., so they do not pollute the environment. BLDC motors are used as the motor of electric vehicles, as they provide a higher speed as well as higher efficiency as compared to other motors. Since the vehicles are powered electrically, it is imperative for us to charge the vehicles just like cell phones. Just like fuel cars have fuel stations for filling up fuel for the operation of vehicles, there must also be battery charging or swapping stations for charging the battery of electric vehicles. This paper would determine how we must charge the electric vehicle using solar along with the traditional grid charging architecture. These two must work in tandem to provide the necessary electrical power to assuring the sufficiently charged battery at the swapping station for uninterrupted use of electric vehicles even in emergency cases. Here, the solar PV along with the traditional grid (Renewable and Conventional both respectively) is used to charge the battery at the swapping station, so it is named a hybrid battery swapping station (HBSS). Also, the concept of charging the battery packs in batches with scheduling is used which reduces the installation cost and the area requirement for the swapping stations compared to other existing battery swapping stations (BSS) or battery charging stations (BCS). [ABSTRACT FROM AUTHOR]

In urban areas, emergency vehicles (EVs) need efficient traffic signal preemption to ensure timely responses during peak hours. While emergency vehicle traffic signal preemption (EVTSP) has garnered significant attention in the literature, the issues of queue spillbacks and negative impacts on non‐priority traffic have been relatively underreported. These issues are particularly critical during peak hours, notably in densely populated urban areas. This study presents an EVTSP system that considers queue spillbacks on approaches along the routes of EVs and the negative impacts of signal preemption on non‐priority traffic. The proposed control system has four key components: (1) a queue length management algorithm to ensure that an EV will not be impeded by excessive queues, particularly on its initial approaches along its route; (2) a signal preemption algorithm to guarantee uninterrupted passage of an EV even for approaches experiencing queue spillbacks; (3) a traffic status recovery algorithm to alleviate the extra waiting time for non‐priority vehicles after an EV crosses each intersection; and (4) a signal plan recovery algorithm to smoothly transit traffic signals to normal operation. The experimental results confirm that the proposed system considerably improves the travel time of an EV and mitigates the negative impacts on non‐priority traffic. [ABSTRACT FROM AUTHOR]

Ambulance crews play an important role in responding quickly to emergencies and rescuing patients by providing appropriate treatment. Typically, fully equipped emergency vehicles are used to transport ambulance personnel to emergency locations. The ambulance crew cleans, sterilizes, and prepares equipment after each patient transfer with great care. Additionally, they check more than 70 pieces of equipment twice a day using a checklist, which is a tedious, time-consuming, and error-prone task. This study uses computer vision and deep learning techniques to replace the manual checklist process for medical equipment to assist the crew and make the equipment availability check faster and easier. To accomplish this, a dataset containing 2099 images of medical equipment in ambulances was collected and annotated with 3000 labeled instances. An experimental study compared the performance of YOLOv9-c, YOLOv8n, and YOLOv7-tiny. YOLOv8n demonstrated the best performance with a mAP50 of 99.2% and a speed of 3.3 ms total time per image. Therefore, YOLOv8 was selected for the proposed system due to its high accuracy and detection speed, which make it suitable for mobile applications. The presence of an application integrated with computer vision and deep learning technologies in paramedic devices can assist in reviewing the equipment checklist, reducing human errors, speeding up the review process, and alleviating the burden on paramedics in their work. [ABSTRACT FROM AUTHOR]

Emergency medical services (EMS) are an important part of the modern healthcare system that tries to provide timely medical care and transportation to patients to reduce morbidity and mortality. Performance evaluation of such EMS systems to determine measures such as mean service rates, dispatch probabilities, busy probabilities, and on-scene times is necessary to design effective and efficient systems. In this paper, we consider an urban EMS system that employs three types of emergency vehicles, including advanced life support (ALS), basic life support (BLS) and first responder vehicle (FRV). We consider two types of patients: type A requires ALS to be dispatched, while type B patients are expected to be served by BLS ambulances. We also consider co-located servers so ambulances of different types can be co-located at the same station. The presence of different types of servers (ambulances) and the patients with different dispatch policies, along with co-located servers, makes it applicable to a more realistic system. We first discuss a modification of the hypercube queueing model for the proposed system and then present an approximate approach for application in large EMS systems. These approaches are compared against a simulation-based model by computing server utilization, service times and on-scene time of ambulances. [ABSTRACT FROM AUTHOR]

This paper investigates dual-hop Reconfigurable Intelligent Surface (RIS) wireless communication systems with malicious jamming, where the destination node faces jamming from a malicious jammer with a RIS-Equipped Unmanned Aerial Vehicle (UAV) relay. We model the channel gains for Tx-RIS and Jammer-RIS links with a Rician distribution, while the RIS-Rx link follows a Nakagami-m distribution, and the jamming status is modeled as a Bernoulli-distributed random variable. We derived and provided closed-form expressions for the probability density functions (PDFs) of the legitimate channel and jamming channel in RIS-Equipped UAV wireless communication systems. Additionally, a new closed-form expression for the PDF of the received signal-to-jamming ratio (SJR) is derived. Using the Gauss–Laguerre Approximation method, we calculate the Average Bit Error Rate (ABER) under Binary Phase Shift Keying (BPSK) and Quadrature Amplitude Modulation (QAM) schemes. We analyze the effects of jamming probability, the location of the legitimate RIS, and different channel conditions on ABER performance through theoretical analyses and simulation results. Our theoretical analyses and simulation results indicate that an increase in the probability of malicious jamming significantly raises the ABER. For example, under favorable channel conditions, the ABER for BPSK modulation was observed to be as low as 10 − 5 , whereas under poor channel conditions, the ABER increased to 10 − 2 . Additionally, by reducing the distance between the transmitter and the RIS, the ABER can be improved. The legitimate RIS performs better when closer to the transmitter. These findings highlight the critical impact of channel conditions and the deployment of the RIS on the overall system's performance. Our results offer valuable insights into designing and evaluating the performance of RIS-Equipped UAV wireless communication systems in the presence of malicious jamming, aiding in the development of countermeasures to enhance system resilience and security. The derived expressions are validated through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Emergency vehicles (EVs) must reach their destination within the shortest amount of time considering the importance of their service in saving lives and reducing damage to property. The aim of this research was to reduce the delay experienced by these vehicles through the alteration of traffic control signals to give preferential treatment to EVs upon detection. Specifically, this study compared three algorithms with concepts of EV pre-emption, longest queue first and emergency vehicle signal priority (EVSP) based on average general and EV delays through a traffic simulation approach using the Vissim software program and its component object model interface. The performance evaluation was applied at Tuk Chai intersection in Bangkok, Thailand as a case study. The second algorithm of longest queue first with pre-emption was found to be the best approach for both peak hour traffic conditions and 0.8 (peak) traffic conditions (i.e. peak hour traffic volume reduced by 20%), performing better than the do-nothing condition by 69.70% for stochastic EV input and 79.14% for fixed EV input. The findings also show that the algorithm of preferential treatment using EVSP strategies produced a competitive and robust performance with EV delay values better than the do-nothing condition by 75.96, 77.27 and 45.67% in 0.8 (peak), peak hour and 1.2 (peak) traffic conditions, respectively (the last of these three being peak hour traffic volume increased by 20%). [ABSTRACT FROM AUTHOR]

Objectives: Little is known about how mass gatherings affect emergency response intervals. Previous research suggests that college football games increase ambulance transport intervals, but their impact on emergency response intervals is unexplored. This study examines how collegiate home football games in College Station, Texas (USA) affect emergency vehicle response intervals. Methods: The study determined the impact of collegiate football games on emergency response intervals using incident data provided by the College Station Fire Department (CSFD). Home games during the 2021-2023 Texas A&M University (TAMU) football seasons were the period of interest. Responses for a 72-hour period (Friday-Sunday) on home game weekends (HGWs) and non-home game weekends (NHGWs) were included (n = 5,095). Results: Response intervals on football HGWs were an average of 30 seconds faster than on NHGWs. Emergency vehicles were 16.5% less likely to respond from fire station locations on HGWs compared to NHGWs. There was also a 12.1% increase in the number of calls to campus locations and a 9.7% increase in calls to the local entertainment district on HGWs compared to NHGWs. Conclusions: Home collegiate football games do not delay response intervals for emergency response vehicles. Further research is needed to determine if these findings can be reproduced in other communities. [ABSTRACT FROM AUTHOR]

Road extraction from remote-sensing images is of great significance for vehicle navigation and emergency insurance. However, the road information extracted in the remote-sensing image is discontinuous because the road in the image is often obscured by the shadows of trees or buildings. Moreover, due to the scale difference of roads in remote-sensing images, it remains a computational challenge to extract small-size roads from remote-sensing images. To address those problems, we propose a road extraction method based on adaptive global feature fusion (AGF-Net). First, a dilated convolution strip attention (DCSA) module is designed from the encoder–decoder structure. It consists of the dilated convolution and the strip attention module, which adaptively emphasizes relevant features in vertical and horizontal directions. Then, multiple global feature fusion modules (GFFM) in the skip connection are designed to supplement the decoder with road detail features, and we design a multi-scale strip convolution module (MSCM) to implement the GFFM module to obtain multi-scale road information. We compare AGF-Net to state-of-the-art methods and report their performance using standard evaluation metrics, including Intersection over Union (IoU), F1-score, precision, and recall. Our proposed AGF-Net achieves higher accuracy compared to other existing methods on the Massachusetts Road Dataset, DeepGlobe Road Dataset, CHN6-CUG Road Dataset, and BJRoad Dataset. The IoU obtained on these datasets are 0.679, 0.673, 0.567, and 0.637, respectively. [ABSTRACT FROM AUTHOR]

Reducing the response time of emergency vehicle(EV) is accompanied with many health and traffic-related benefits. New technologies can be utilised in creating a cooperative lane-changing behaviour for surrounding traffic to reduce the response time. In this research, the influence of lane-changing behaviour on the delay of EV and surrounding vehicles was studied in microscopic traffic simulation. A driving behaviour and a pre-emption algorithm were developed to prioritise EVs on a signalised corridor. Two areas in front of EV were defined including stopping and lane-changing areas with different behaviours in a connected and autonomous traffic environment. It was concluded that using stopping and lane-changing areas together is more favourable regarding the delays of both EV and surrounding traffic. Also, the results verified the presence of an optimum lane-changing threshold after which the EV delay became constant. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to develop the target object mobile platform for vehicle Automatic Emergency Braking (AEB) system test, this article studied the configuration design, strength analysis, fatigue life estimation and path tracking of test scene. Through the analysis of the test scenarios stipulated in C-NCAP Management Regulation (2024 Edition), the moving path of the target was extracted, and the expected trajectory was obtained according to the boundary conditions, then the trajectory tracking results were obtained through dynamic simulation. The comparison shows that the simulation results are basically consistent with the specified requirements, indicating that the designed mobile platform can meet the needs of AEB testing. [ABSTRACT FROM AUTHOR]

Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents in modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly in adverse conditions. The objective of this research is to enhance emergency vehicle detection by leveraging the synergies between acoustic and visual information. By incorporating advanced deep learning techniques for both acoustic and visual data, our aim is to significantly improve the accuracy and response times. To achieve this goal, we developed an attention-based temporal spectrum network (ATSN) with an attention mechanism specifically designed for ambulance siren sound detection. In parallel, we enhanced visual detection tasks by implementing a Multi-Level Spatial Fusion YOLO (MLSF-YOLO) architecture. To combine the acoustic and visual information effectively, we employed a stacking ensemble learning technique, creating a robust framework for emergency vehicle detection. This approach capitalizes on the strengths of both modalities, allowing for a comprehensive analysis that surpasses existing methods. Through our research, we achieved remarkable results, including a misdetection rate of only 3.81% and an accuracy of 96.19% when applied to visual data containing emergency vehicles. These findings represent significant progress in real-world applications, demonstrating the effectiveness of our approach in improving emergency vehicle detection systems. [ABSTRACT FROM AUTHOR]

【Background】 In the event of a disaster or emergency, alerts, sirens, breaking news, awareness of the proximity of emergency vehicles, etc. are often aroused by auditory information. Therefore, people with hearing loss are more likely to be delayed in recognizing danger, and accordingly delayed in evacuation or avoidance. Visual information presentation has its limitations, making it difficult to obtain information about disasters and emergencies, especially for deaf people who do not wear hearing aids, when they are sleeping, or when they are not alert. In addition, people with hearing loss are often at a disadvantage when it comes to receiving and receiving information regarding rescue requests such as ambulances, so it is essential to develop countermeasures. 【Purpose/Method】 We investigated the status of primary input and information sharing during disasters and emergencies for people with hearing loss, and the status of utilization of information support equipment, including information and communication technology (ICT), conducted a survey on the people with hearing loss with the aim of extracting issues. The subjects were 223 people with hearing loss over the age of 18. The survey items were: 1. Background factors, 2. Primary input methods and challenges during disasters and emergencies, 3. Rescue request methods and challenges, and 4. Difficulties and ingenuity during disasters and emergencies. 【Results】 The investigation revealed the following. 1) More than 70% of people with hearing loss experienced difficulty in primary input during disasters, but the proportion using existing information support equipment was as low as 35%. They have challenges in obtaining information independently in real time. 2) Only half of the respondents had the experience of self-help when making an emergency call, and half of them had asked someone else to help them. Among people with hearing loss who made the call, some unilaterally provided information. 3) Many people with hearing loss are worried about communication when there is no support person or when there is a charging or communication failure of hearing aids or information support devices. 4) More than 70% of respondents desired a device that converts sound and vibration for emergency notifications in the event of a disaster or emergency, or a device that sends and receives information in the form of images or videos. The needs for their development were suggested. 【Conclusion】 In preparation for disasters and emergencies, it is desirable to expand systems for mutual aid and public assistance, as well as to secure means of self-help through the use of information support equipment, including ICT. [ABSTRACT FROM AUTHOR]

Smart sensors have revolutionized the world with their capability of capturing physical parameters. The sensors provide vital data to the monitoring station in the applications of environment, disaster management, transportation, etc. A framework is built up with an Internet of Things-based approach that detects vehicle accident with the sensors and the information is stored and monitored on the Thingspeak cloud. The alert is raised whenever an accident occurs to the vehicle with the help of an accelerometer, ultrasonic sensor, and GPS. The message of the real-time coordinates is transmitted with the communication module. The monitoring station takes the necessary action by recognizing the accident occurred and providing the emergency services to the hotspot immediately. The integrated approach will be beneficial as it will detect any mishap with the vehicle and facilitate the emergency help to the passengers in the vehicle. The proposed framework will be beneficial for saving the human life in any smart city. [ABSTRACT FROM AUTHOR]

This paper considers the vehicle control process in emergency lane change (ELC). The control method is divided into two parts: before crossing the obstacle and after crossing it. The first part is done by a feed-forward controller inspired by the driver's behavior pattern in the ELC maneuver. This controller is compensated by a neural network system and two proportional-integral (PI) and proportional-derivative (PD) controllers, which correct the output of the feed-forward controller to follow the path more accurately. The second part consists of two PD controllers responsible for controlling and steering the vehicle in the main path after crossing the obstacle. The main novelty of the proposed method is that the computational burden is low because of using a feed-forward controller, which is the main source of the calculation's burden. So as a result, the controller system can cover the ELC maneuver in under 2 seconds in various lateral displacements to bypass the obstacle and prevent collisions. The co-simulations are performed in CarSim software and MATLAB/Simulink, which shows that the control system can appropriately steer the vehicle and follow the desired at different speeds, in the presence of road friction and parameter uncertainty. [ABSTRACT FROM AUTHOR]