IET Intelligent Transport Systems最新文献

This paper proposes a longitudinal controller for the mixed vehicle platoon in the presence of driver response time-delay and communication time-delay based on the pinning consensus of heterogeneous multi-agent system. Firstly, an adaptive pinning consensus control protocol and derive sufficient conditions for the heterogeneous non-linear multi-agent system are designed to achieve pinning consistency. Then, the heterogeneous characteristics of the vehicle are described based on the car-following model, then the mixed vehicle platoon system model is constructed and a mixed vehicle platoon controller based on heterogeneous multi-agent pinning consensus is proposed. Besides, the driver response time-delay and communication time-delay are introduced into the model, and a controller based on time-delay heterogeneous multi-agent pinning consensus is designed. Finally, the effectiveness of the proposed controller is verified by numerical simulations, and the effect of number of connected autonomous vehicle, different types of vehicle order, driver response time-delay, communication time-delay and the parameter of the controller on stability of mixed vehicle platoon is also quantitatively demonstrated.

The rapid and uncontrolled urbanization of cities in developing countries has engendered a plethora of urban mobility issues, including traffic congestion, accidents, and air pollution. To address these challenges, contemporary urban mobility trends are incorporating innovative technologies and sustainable governance practices. This article investigates how urban managers can leverage the opportunities presented by cost-effective technologies and the management of urban data to enhance urban mobility in developing countries. Within this discourse, an RSU-based approach is proposed that employs motorbike taxis for inter-vehicular communication, given their status as the most widely used form of public transportation. This approach substantially diminishes investment costs and reinforces the sustainability of urban mobility. Through the implementation of this solution, a noteworthy reduction is anticipated in the emission of gases such as CO2, and NOx, known contributors to climate change and various respiratory diseases. To validate the efficacy of the proposed solution, four distinct scenarios are scrutinized in a case study centered on the city of Douala in Cameroon, utilizing tools such as OMNET++, SUMO, Veins, and INET. The proposed framework offers significant benefits in terms of environmental sustainability and operational efficiency. It enables a 10% reduction in CO2 emissions, a 15% reduction in NOx emissions, an 11% drop in fuel consumption, and a 15% reduction in waiting time in traffic jams. The envisaged solution aims to aid urban managers in their decision-making processes, specifically in advancing sustainable urban mobility. Through the adoption of this approach, cities in developing countries can mitigate challenges associated with urban mobility and enhance the overall well-being of their residents.

Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation-assisted machine learning model with two concepts: general (adding all predictors at once) and step-wise (adding predictors as they become available in sub-yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10-fold cross-validation. The model's performance on three data sets—a real-world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective $�R^2$ of 0.90, 0.87, and 0.70. Step-wise inclusion of the predictors results differently for the real-world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real-world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions.

Multispectral pedestrian detection with visible light and infrared images is robust to changes in lighting conditions and therefore is of great importance to numerous applications that require all-day environmental perception. This paper proposes a novel method named FCE-RCNN, which integrates saliency detection as a sub-task and utilizes global information for enhanced feature representation. The approach enhances thermal inputs by incorporating gradients at the raw-data level before feature extraction. Utilizing a dual-stream backbone, a global semantic information extraction module is introduced that combines pooling with horizontal–vertical attention mechanisms, capturing high-quality global semantic information for lower-level feature enrichment and guidance. Additionally, the pedestrian locality enhancement module is designed to enhance spatial locality information of pedestrians through saliency detection. Furthermore, to alleviate the challenges posed by positional shifts between cross-spectral features, deformable convolution is innovatively employed. Experimental results on the KAIST dataset demonstrate that FCE-RCNN significantly improves nighttime detection, achieving a log-average miss rate of 6.92%, outperforming the new method ICAFusion by 0.93%. These results underscore the effectiveness of FCE-RCNN, and the method also maintains competitive inference speed, making it suitable for real-time applications.

Advances in real-time basic safety message (BSM) data from sensor-equipped vehicles have created new opportunities for driving risk assessments. This paper presents a machine learning approach using BSM data to provide fine-grained risk assessments, focusing on safety-critical events (SCEs) related to driving profiles, vehicle states, and road conditions. This approach formulates a bi-level risk indicator: one level measures the observable frequency of SCEs, while the other estimates their likelihood. The coarse level calculates risk scores by classifying driving profiles as normal or risky based on SCE frequency. The fine level refines these scores by comparing normal and risky profiles using key features from a feature learning model. This combined system accounts for recent driving behaviours and road/weather conditions within a vehicle-to-everything (V2X) environment, addressing high data dimensionality and imbalance. A comprehensive case study using 1 year of data from pilot V2X infrastructure in Tampa, Florida, demonstrates the efficacy of this approach, showing practical applications of the SCE-based risk indicator and combinatorial feature learning while also highlighting the real-world utility of the assessment method in providing a detailed and actionable view of driving risk based on V2X information.

The analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents’ activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra-similarities, inter-differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned.

Intelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision-making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high-level road plan. To this end, this research compares cutting-edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real-time results have been obtained for fast KNN algorithms, leading to a robust tree index-based methodology for decision making in self-driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control.

The anomaly detection and predictive replacement of the degradation decay state coefficient (D_esc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first-order, and second-order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z-score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.

High-definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer-grade sensors in mass-produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high-definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing-based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high-precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud-based map.

The study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self-supervised vessel trajectory segmentation method (SS-VTS), which segments VTs based on their inherent spatio-temporal semantics. SS-VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi-dimensional feature sequence of the VTs using self-supervised learning. Finally, spatio-temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS-VTS achieves state-of-the-art segmentation results compared to seven baseline methods.