IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Transformer-Conformer Ensemble for Crash Prediction Using Connected Vehicle Trajectory Data","authors":"Zubayer Islam;Mohamed Abdel-Aty;B M Tazbiul Hassan Anik","doi":"10.1109/OJITS.2023.3339016","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3339016","url":null,"abstract":"Crash prediction is one of the important elements of real time traffic management strategies. Previous studies have demonstrated the use of infrastructure-based detector data and UAV video to predict a crash in the near future. The main limitation of such data is limited coverage. In this work, we have used connected vehicle trajectory data that can have wide coverage as well as provide insight into the trajectory that might lead to a crash. The trajectory data was provided by Wejo which collects data from the manufacturer and was spaced at 3 seconds. GPS locations and their associated time series features such as speed, acceleration and yaw rate were used to feed into an ensembled Transformer and Conformer model. A voting classifier was used to obtain the output of the final model which achieved a recall of 76% and the false alarm rate of 30%. This study showed how connected vehicle trajectory data can aid in getting insight into crashes. While most previous studies focus on using aggregated data to estimate crashes, the proposed work shows that trajectory data mining can also provide competitive results.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"979-988"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10339651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Conflict Resolution for Vehicles With Intersecting and Overlapping Paths","authors":"Johan Karlsson;Nikolce Murgovski;Jonas Sjöberg","doi":"10.1109/OJITS.2023.3336533","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3336533","url":null,"abstract":"A collaborative centralized model predictive controller solving the problem of autonomous vehicles safely crossing an intersection is presented. The solution gives optimal speed trajectories for each vehicle while considering collision avoidance constraints between vehicles traveling on the same path before, after and/or within the intersection. This extends earlier results, where collision avoidance was only considered for vehicles with intersecting paths, with the possibility of vehicles on the same path and by this, the controller is not only one step closer to handling complex traffic intersections but can now be used for merging and splitting of roads, roundabouts and intersection networks. The proficiency of the extended controller is demonstrated by applying it to a four-way intersection. It is shown that the controller provides smooth, collision free trajectories in scenarios with and without vehicles traveling in the same lane. Further, it is evaluated how the solutions differ when using various cost functions and how the controller handles disturbances in the form of a sudden lane blockage. Lastly, it is discussed how the presented controller could also be extended to handle mixed-traffic scenarios and how soft constraints can be used to avoid infeasibility in the case of missing or noisy traffic data.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"146-159"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335957","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System","authors":"Roya Alizadeh;Yvon Savaria;Chahé Nerguizian","doi":"10.1109/OJITS.2023.3336795","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3336795","url":null,"abstract":"Robust methods are needed to detect how people are moving in smart public transportation systems. This paper proposes and characterizes effective means to accurately detect passengers. We analyze a public WiFi-based activity recognition (WiAR) dataset to extract human activity features from Channel State Information (CSI) data. To do so, CSI power changes caused by nearby human activity are analyzed. Our method first extracts multi-dimensional features using a Short-Time Fourier Transform (STFT) of CSI data to capture the relevant signal features. Since the environment of a transportation system changes dynamically and non-deterministically, we propose analyzing these changes with a heuristic algorithm that leverages a decision tree to automate a decision-making solution for feature selection. Principal Component Analysis (PCA) is performed before the decision tree algorithm. Reported results are compared with those obtained from the existing methods. Based on these results, we explore the effectiveness of various features such as the chirp rate, delta band power, spectral flux, and frequency of movement. This allows identifying and recommending the most effective features for the explored detection task according to observed variability, information gain, and correlation between features. The reported classification results show that using only the chirp rate estimated from CSI information as a feature, we achieve precision = 83%, True Positive \u0000<inline-formula> <tex-math>$(TP)=94%$ </tex-math></inline-formula>\u0000, True Negative \u0000<inline-formula> <tex-math>$(TN)= 91%$ </tex-math></inline-formula>\u0000 and F1-score = 87%. Considering delta band power as an additional feature adds more information and allows getting higher performance with precision = 100%, \u0000<inline-formula> <tex-math>$TP=97%$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$TN = 95%$ </tex-math></inline-formula>\u0000 and F1-score = 95%.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"55-69"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10332939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles","authors":"Durga Prasad Bavirisetti;Herman Ryen Martinsen;Gabriel Hanssen Kiss;Frank Lindseth","doi":"10.1109/OJITS.2023.3335648","DOIUrl":"10.1109/OJITS.2023.3335648","url":null,"abstract":"In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting. Specifically, we explore the use of Vision Transformers for semantic segmentation and monocular depth estimation using only a single image as input. We present state-of-the-art Vision Transformers for these tasks and combine them into a multitask model. Through multiple experiments on four different street image datasets, we demonstrate that the multitask approach significantly reduces inference time while maintaining high accuracy for both tasks. Additionally, we show that changing the size of the Transformer-based backbone can be used as a trade-off between inference speed and accuracy. Furthermore, we investigate the use of synthetic data for pre-training and show that it effectively increases the accuracy of the model when real-world data is limited.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"909-928"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10330677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138576854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonlocal Calculus-Based Macroscopic Traffic Model: Development, Analysis, and Validation","authors":"Pushkin Kachroo;Shaurya Agarwal;Animesh Biswas;Archie J. Huang","doi":"10.1109/OJITS.2023.3335303","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3335303","url":null,"abstract":"Nonlocal calculus-based macroscopic traffic models overcome the limitations of classical local models in accurately capturing traffic flow dynamics. These models incorporate “nonlocal” elements by considering the speed as a weighted mean of downstream traffic density, aligning it more closely with realistic driving behaviors. The primary contributions of this research are manifold. Firstly, we choose a nonlocal LWR model and Greenshields fundamental diagram and prove that this traffic flow model satisfies the well-posed conditions. Furthermore, we prove that the chosen model maintains bounded states, laying the groundwork for developing numerically stable schemes. Subsequently, the efficacy of the proposed nonlocal model is evaluated through extensive field validation using real traffic data from the NGSIM dataset and developing a stable numerical scheme. These validation results highlight the superiority of the nonlocal model in capturing traffic characteristics compared to its local counterpart and establish its enhanced accuracy in reproducing complex traffic behavior. Therefore, this research expands both the theoretical constructs within the field and substantiates its practical applicability.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"900-908"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10330738","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Safer Autonomous Vehicles: Occlusion-Aware Trajectory Planning to Minimize Risky Behavior","authors":"Rainer Trauth;Korbinian Moller;Johannes Betz","doi":"10.1109/OJITS.2023.3336464","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3336464","url":null,"abstract":"Autonomous vehicles face numerous challenges to ensure safe operation in unpredictable and hazardous conditions. The autonomous driving environment is characterized by high uncertainty, especially in occluded areas with limited information about the surrounding obstacles. This work aims to provide a trajectory planner to solve these unsafe environments. The work proposes an approach combining a visibility model, contextual environmental information, and behavioral planning algorithms to predict the likelihood of occlusions and collision probabilities. Ultimately, this allows us to estimate the potential harm from collisions with pedestrians in occluded situations. The primary goal of our proposed approach is to minimize the risk of hitting pedestrians and to establish a predefined, adjustable maximum level of harm. We show several practical applications for informing a sampling-based trajectory planner about occluded areas to increase safety. In addition, to respond to possible high-risk situations, we introduce an adjustable threshold that governs the vehicle’s speed when encountering uncertain situations and strategies to maximize the vehicle’s visible area. In implementing our novel methodology, we analyzed several real-world scenarios in a simulation environment. Our results indicate that combining occlusion-aware trajectory planning algorithms and harm estimation significantly influences vehicle driving behavior, especially in risky situations. The code used in this research is publicly available as open-source software and can be accessed at the following link: \u0000<uri>https://github.com/TUM-AVS/Frenetix-Motion-Planner</uri>\u0000.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"929-942"},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10328654","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data","authors":"Younghoon Seo;Jihyeok Park;Gyungtaek Oh;Hyungjoo Kim;Jia Hu;Jaehyun So","doi":"10.1109/OJITS.2023.3335817","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3335817","url":null,"abstract":"The unstructured-textual crash descriptions recorded by police officers is rarely utilized, despite containing detailed information on traffic situations. This lack of utilization is mainly due to the difficulty in analyzing text data, as there is currently no innovative methodology for extracting meaningful information from it. Given limitations and challenges in analyzing traffic crash descriptions, this study developed a methodology to classify significant words in unstructured data that describe traffic crash scenarios into standardized data. Ultimately, a natural language processing technique, specifically a bidirectional encoder representation from transformer (BERT), was used to extract meaningful information from crash descriptions. This BERT-based model effectively extracts information on the exact collision point and the pre-crash vehicle maneuver from crash descriptions. Its practical approach allows for the interpretation of traffic crash descriptions and outperforms other natural language processing models. Importantly, this method of extracting crash scene information from traffic crash descriptions can aid in better comprehending the unique characteristics of traffic crashes. This comprehension can ultimately aid in the development of appropriate countermeasures, leading to the prevention of future traffic crashes.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"955-965"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10327780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Optimal Braking Policy for Avoiding Collision With Front Bicycle","authors":"Xun Shen;Yan Zhang;Xingguo Zhang;Pongsathorn Raksincharoensak;Kazumune Hashimoto","doi":"10.1109/OJITS.2023.3335397","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3335397","url":null,"abstract":"Bicycles are frequently involved in traffic collisions with vehicles, particularly when sudden changes in direction occur. This paper presents a robust risk-predictive braking policy to ensure collision avoidance in all possible crossing behaviors of a bicycle. The policy controls the vehicle to follow an upper limit of the safe speed before the bicycle changes direction, ensuring that the vehicle can stop in time by the advanced emergency braking system before a collision occurs in any situation. The upper limit of the safe speed is the solution of an intractable robust optimization problem. Therefore, a scenario approach is adapted to develop a tractable approximate problem for the original robust optimization problem. The feasibility and optimality of the problem reduction are theoretically proved. A bisection method-based fast algorithm is designed to solve the approximate problem of the original robust optimization problem, making it applicable in practical scenarios. The convergence of the algorithm is also proven. The effectiveness of the proposed method is validated through hardware-in-the-loop simulations using CarMaker.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"943-954"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10325675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Literature Review on Machine Learning in Shared Mobility","authors":"Julian Teusch;Jan Niklas Gremmel;Christian Koetsier;Fatema Tuj Johora;Monika Sester;David M. Woisetschläger;Jörg P. Müller","doi":"10.1109/OJITS.2023.3334393","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3334393","url":null,"abstract":"Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"870-899"},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10325362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risky Traffic Situation Detection and Classification Using Smartphones","authors":"Akira Uchiyama;Akihito Hiromori;Ryota Akikawa;Hirozumi Yamaguchi;Teruo Higashino;Masaki Suzuki;Yasuhiko Hiehata;Takeshi Kitahara","doi":"10.1109/OJITS.2023.3333263","DOIUrl":"10.1109/OJITS.2023.3333263","url":null,"abstract":"Behind many traffic accidents, there are more frequent minor incidents (risky traffic situations) that may lead to severe accidents. Analyzing such minor incidents effectively reduces accidents, but the challenge is to design a method to collect and analyze such incident information. In this paper, we propose a novel platform that aggregates behavioral data from pedestrians and drivers using their smartphones and recognizes risky traffic situations from the aggregated data. We design a two-stage approach where the smartphones of pedestrians and vehicles act as local anomaly detectors for triggering the event detector and classifier in the post-stage at the cloud server to suppress the processing and communication overhead. We also introduce an unsupervised learning system to cope with unseen risky situations enabled by joint utilization of the autoencoder-based anomaly detector and the risky situation classifier. The evaluation is conducted through both simulation and real experiments. The simulation result shows the risky situation detector achieves an F-measure of 0.89. We also collected real data at a car driving course to evaluate the risky situation classifier. From the results, we have confirmed that the proposed method succeeded in classifying three risky traffic situations involving pedestrians and/or vehicles with an accuracy of 89.3%.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"846-857"},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10318156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135709423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}