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.

/pdf/emergency-vehicle-driving-assistance-system-using-recurrent-1adco9hc.pdf

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

Application Research and Improvement of Weighted Information Fusion Algorithm and Kalman Filtering Fusion Algorithm in Multi-sensor Data Fusion Technology

Digital construction relies on effective sensing to enhance the safety, productivity, and quality of its activities. However, current sensing devices (e.g., camera, LiDAR, infrared sensors) have significant limitations in different aspects. In light of the substantial advantages offered by emerging 4D mmw technology, it is believed that this technology can overcome these limitations and serve as an excellent complement to current construction sensing methods due to its robust imaging capabilities, spatial sensing abilities, velocity measurement accuracy, penetrability features, and weather resistance properties. To support this argument, a scientometric review of 4D mmw-based sensing is conducted in this study. A total of 213 articles published after the initial invention of 4D mmw technology in 2019 were retrieved from the Scopus database, and six kinds of metadata were extracted from them, including the title, abstract, keywords, author(s), publisher, and year. Since some papers lack keywords, the GPT-4 model was used to extract them from the titles and abstracts of these publications. The preprocessed metadata were then integrated using Python and fed into the Citespace 6.2.R3 for further statistical, clustering, and co-occurrence analyses. The result revealed that the primary applications of 4D mmw are autonomous driving, human activity recognition, and robotics. Subsequently, the potential applications of this technology in the construction industry are explored, including construction site monitoring, environment understanding, and worker health monitoring. Finally, the challenges of adopting this emerging technology in the construction industry are also discussed.

/pdf/four-dimensional-4d-millimeter-wave-based-sensing-and-its-3rzzegjk.pdf

Four-Dimensional (4D) Millimeter Wave-Based Sensing and Its Potential Applications in Digital Construction: A Review

Traffic monitoring systems featuring robust, multi-sensor fusion capabilities are rapidly growing in demand to observe traffic flow, reduce congestion and to detect and report traffic accidents. However, monitoring outdoor environments using cameras remains challenging due to complex weather conditions, including fog, rain, snow and variable lighting conditions. The presence of these weather conditions can significantly reduce vehicle detection and classification performance using machine learning methods. Unfortunately, openly available datasets for multi-sensor traffic monitoring development and testing remain limited, especially those featuring infrastructure-based cameras and millimeter wave (mmWave) radar. To address these challenges, we evaluate open camera and mmWave radar data using vehicle classification models for cars, trucks, vans and buses on embedded hardware. We also provide an open multi-sensor traffic monitoring dataset with more than 8,000 manually annotated frames as well as mmWave radar point clouds recorded in an urban environment under sunny, partially cloudy, cloudy, rainy and night conditions.

Open Urban mmWave Radar and Camera Vehicle Classification Dataset for Traffic Monitoring

In this paper, a millimeter wave radar ranging algorithm based on improved robust Kalman filter is proposed. In order to solve the problems of multi-point return of radar, unstable output and susceptible to environmental influences in the real-time monitoring of transmission line anti-mechanical collision line. Firstly, the effective target distance minimum extraction algorithm based on time domain is used to filter out the outliers. Next, the continuous output data is subjected to robust Kalman filter to realize data smoothing, so as to improve the anti-coarse interference capability of the system. Finally, static and dynamic experiments were conducted to simulate different mobile states of construction vehicles. The experimental results show that accuracy of distance measurement of our algorithm is about ±0.1m, and the ranging error is 2%, which can meet the overall demand for real-time monitoring of transmission line anti-mechanical collision line.

Transmission line distance measurement with millimeter wave radar based on improved robust Kalman filter

The Advanced Driving Assistance System has been widely applied on various kinds of vehicles, since it can provide timely protection and burden relief to the passengers and drivers. Multiple different sensors are adopted to realize specific functions, while millimeter-wave radar and vision sensor are one of the most common sensor combinations. The scheme of sensor data fusion for this combination becomes necessary to contribute with more accurate and complementary detection results. A data fusion model designed for the pair of radar and vision is proposed with a three-level hierarchical structure, which includes screening of original sensor data, sensor data correlation, and target track forecast. The concrete fusion scheme applied on each layer is presented in details. The results of simulation and real circumstances test validate and prove the presented sensor data fusion model is efficient.

A Data Fusion Model for Millimeter-Wave Radar and Vision Sensor in Advanced Driving Assistance System

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A Data Fusion Model for Millimeter-Wave Radar and Vision Sensor in Advanced Driving Assistance System