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Model-Based Graph Reinforcement Learning for Inductive Traffic Signal Control IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-03-12 François-Xavier Devailly, Denis Larocque, Laurent Charlin
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Worst-Case Response Time of Mixed Vehicles at Complex Intersections IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-02-22 Radha Reddy, Luis Almeida, Harrison Kurunathan, Miguel Gutierrez Gaitan, Pedro M. Santos, Eduardo Tovar
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Safety Improvements for Personnel and Vehicles in Short-Term Construction Sites IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-02-19 Daniel Rau, Jonas Vogt, Philipp Schorr, Juri Golanov, Andreas Otte, Jens Staub, Horst Wieker
Despite all efforts to enhance safety, construction sites remain a major location for traffic accidents. Short-term construction sites, in particular, face limitations in implementing extensive safety measures due to their condensed timelines. This paper seeks to enhance safety in short-term construction sites by alerting maintenance personnel and approaching vehicles to potentially dangerous scenarios
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Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-02-15 Kunxiong Ling, Jan Thiele, Thomas Setzer
We propose a method to estimate information loss when conducting histogram binning and principal component analysis (PCA) sequentially, as usually done in practice for fleet analytics. Coarser-grained histogram binning results in less data volume, fewer dimensions, but more information loss. Considering fewer principal components (PCs) results in fewer data dimensions but increased information loss
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Addressing Rare Outages in C-V2X With Time-Controlled One-Shot Resource Scheduling IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-02-02 Md Saifuddin, Mahdi Zaman, Yaser P. Fallah, Jayanthi Rao
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An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-02-02 He Yuesheng, Wang Tao, Chen Long, Zhuang Hanyang, Yang Ming
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Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-01-18 Salah Zidi, Bechir Alaya, Tarek Moulahi, Amal Al-Shargabi, Salim El Khediri
The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks
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2024 Editorial IEEE Open Journal of Intelligent Transportation Systems IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-01-05 Jiaqi Ma
Dear Authors and Readers, Welcome to the 2024 Volume of the IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS). This marks my second year serving as the Editor-in-Chief (EiC) of OJ-ITS. First and foremost, I would like to express my gratitude to all the active associate editors and reviewers who have devoted their valuable time to OJ-ITS and enabled the journal’s rapid growth. I also
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Analyzing Shared Bike Usage Through Graph-Based Spatio-Temporal Modeling IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-01-05 Dinh Viet Cuong, Vuong M. Ngo, Paolo Cappellari, Mark Roantree
Bike sharing schemes can be used both to improve mobility around busy city routes but also to contribute to the fight against climate change. Optimization of the network in terms of station locations and routes is a focus for researchers, where usage can highlight the precise times at which bike availability is high in some areas and low in others. Locations for new stations are important for the expansion
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Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts? IEEE Open J. Intell. Transp. Syst. Pub Date : 2024-01-04 Olger Siebinga, Arkady Zgonnikov, David A. Abbink
Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on merging use naturalistic data. Although this provides insight into human gap acceptance and traffic flow effects, it obscures the operational inputs of interacting drivers. Besides that, researchers have no control over the
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How Will the Railway Look Like in 2050? A Survey of Experts on Technologies, Challenges and Opportunities for the Railway System IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-12-25 Michael Nold, Francesco Corman
The railway system can fulfil society’s current and future transportation goals; compared to other transport modes, it does that with high energy, space and resource efficiency. It can deliver high-quality transport services, superior speed, safety and comfort to most competing modes. Nevertheless, its share of the total traffic is often relatively small. This study examines new technologies, their
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On Modelling and Investigating User Acceptance of Highly Automated Passenger Vehicles IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-12-25 Ilias E. Panagiotopoulos, George J. Dimitrakopoulos, Gabriele Keraite
Highly automated passenger vehicles hold great potential to alleviate traffic congestion, enhance road safety, and revolutionize the travel journey. However, while much attention has been given to the technical aspects of this technology, the investigation of public acceptance remains crucial for successful implementation in the global market. To address this gap, this paper introduces innovative research
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Theoretical Trade-Off Between Fairness and Efficiency in the Cooperative Driving Problem for CAVs at On-Ramps IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-12-19 Zimin He, Huaxin Pei, Yuqing Guo, Danya Yao, Li Li
Cooperative driving is crucial for improving traffic efficiency and safety for connected and automated vehicles (CAVs), especially in traffic bottlenecks. However, most of the state-of-the-art cooperative driving strategies neglect the issue of fairness. Fairness is essential to properly allocate road resources and improve the travel experience. In this paper, we focus on the fairness concerns in the
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Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-12-13 Lisa Kessler, Klaus Bogenberger
This paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. Congestion events can be categorized into multiple types, ranging from short traffic disruptions (referred to as Jam Wave) to Stop and Go patterns and severe congestion scenarios like Wide Jam. We analyze multiple traffic data sets, including speed data from
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Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-12-12 Jorge Ugan, Mohamed Abdel-Aty, Zubayer Islam
Speeding remains a key factor in traffic fatalities, prompting transportation agencies to propose speed management solutions. While studies have examined speeding percentages above limits, few address its impact on individual journeys. Most studies rely on detector speed data, lacking route insights. This research employs connected vehicle trajectory data to analyze driver paths and variables, predicting
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Optimal Conflict Resolution for Vehicles With Intersecting and Overlapping Paths IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-30 Johan Karlsson, Nikolce Murgovski, Jonas Sjöberg
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
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Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-29 Roya Alizadeh, Yvon Savaria, Chahé Nerguizian
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
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Nonlocal Calculus-Based Macroscopic Traffic Model: Development, Analysis, and Validation IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-28 Pushkin Kachroo, Shaurya Agarwal, Animesh Biswas, Archie J. Huang
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
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A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-28 Durga Prasad Bavirisetti, Herman Ryen Martinsen, Gabriel Hanssen Kiss, Frank Lindseth
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
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Toward Safer Autonomous Vehicles: Occlusion-Aware Trajectory Planning to Minimize Risky Behavior IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-24 Rainer Trauth, Korbinian Moller, Johannes Betz
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
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Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-23 Younghoon Seo, Jihyeok Park, Gyungtaek Oh, Hyungjoo Kim, Jia Hu, Jaehyun So
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
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Robust Optimal Braking Policy for Avoiding Collision With Front Bicycle IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-22 Xun Shen, Yan Zhang, Xingguo Zhang, Pongsathorn Raksincharoensak, Kazumune Hashimoto
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
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A Systematic Literature Review on Machine Learning in Shared Mobility IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-21 Julian Teusch, Jan Niklas Gremmel, Christian Koetsier, Fatema Tuj Johora, Monika Sester, David M. Woisetschläger, Jörg P. Müller
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
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Risky Traffic Situation Detection and Classification Using Smartphones IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-15 Akira Uchiyama, Akihito Hiromori, Ryota Akikawa, Hirozumi Yamaguchi, Teruo Higashino, Masaki Suzuki, Yasuhiko Hiehata, Takeshi Kitahara
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
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Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-13 Tianyu Shi, François-Xavier Devailly, Denis Larocque, Laurent Charlin
A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. Compared to traditional approaches, RL approaches can learn from higher-dimensionality input road and vehicle sensors and better adapt to varying traffic conditions resulting in reduced travel times (in simulation). However, these RL methods require training from massive traffic sensor data. To offset this relative
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Automotive Radar Sub-Sampling via Object Detection Networks: Leveraging Prior Signal Information IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-13 Madhumitha Sakthi, Marius Arvinte, Haris Vikalo
In recent years, automotive radar has attracted considerable attention due to the growing interest in autonomous driving technologies. Acquiring situational awareness using multimodal data collected at high sampling rates by various sensing devices including cameras, LiDAR, and radar requires considerable power, memory and compute resources which are often limited at an edge device. In this paper,
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Radar Translation Network Between Sunny and Rainy Domains by Combination of KP-Convolution and CycleGAN IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-09 Jinho Lee, Geonkyu Bang, Toshiaki Nishimori, Kenta Nakao, Shunsuke Kamijo
Recently, research on autonomous driving has focused on the advent of various deep learning algorithms. The main sensors for autonomous driving include cameras, LiDAR, and radar, but these algorithms primarily focus on image and LiDAR data. This is because radar data is limited compared to image and LiDAR data. To address the lack of data problem, GAN-based translation methods have been proposed. However
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A Survey on the Use of Container Technologies in Autonomous Driving and the Case of BeIntelli IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-09 Benjamin Acar, Marc Guerreiro Augusto, Marius Sterling, Fikret Sivrikaya, Sahin Albayrak
The application of containerization technology has seen a significant increase in popularity in recent years, both in the business and scientific sectors. In particular, the ability to create portable applications that can be deployed on different machines has become a valuable asset. Autonomous driving has embraced this technology, as it offers a wide range of potential applications, including the
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Network-Wide Public Transport Occupancy Prediction Framework With Multiple Line Interactions IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-11-09 Federico Gallo, Nicola Sacco, Francesco Corman
This paper addresses the problem of predicting the occupancy of urban public transport vehicles with a network-wide framework where the effects of the interactions between multiple lines are jointly considered. In particular, we propose and compare several occupancy predictors, each of them differing in the amount of information used and in the prediction model adopted. We consider two prediction models:
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Identifying and Planning for Group Travellers in On-Demand Mobility Models IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-10-30 Grace O. Kagho, Milos Balac
Understanding group travel is vital for transportation planners and policymakers, especially when modelling emerging on-demand mobility such as ridesharing and shared autonomous vehicles. Existing agent-based simulations of ridesharing services hardly consider group travel, even though these services mainly occur during the weekend and for leisure trips where people are more likely to travel in groups
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Bandwidth-Based Traffic Signal Coordination Models for Split or Mixed Phasing Schemes in Various Types of Networks IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-10-18 Binbin Jing, Quan Shi, Cong Huang, Peng Ping, Yongjie Lin
Bandwidth-based traffic signal coordination has long been an effective technique to make traffic flows within a network more efficient, smoother, and safer. Existing network bandwidth optimization models mainly focus on maximizing the bandwidth under NEMA phasing. The network bandwidth maximization under other typical phasing schemes, namely the split or mixed phasing, has not been intensively studied
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Comfort and Safety in Conditional Automated Driving in Dependence on Personal Driving Behavior IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-10-13 Laurin Vasile, Naramsen Dinkha, Barbara Seitz, Christoph Däsch, Dieter Schramm
When changing from active driving to conditional automated driving (CAD), the question arises whether users still prefer their own driving behavior while being a passenger. The aim of this paper is to analyze driving behavior preferences in CAD based on the perception of comfort and safety, taking the personal driving behavior into account. Furthermore, it is investigated if users are able to manually
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A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-10-02 Serio Agriesti, Vladimir Kuzmanovski, Jaakko Hollmén, Claudio Roncoli, Bat-Hen Nahmias-Biran
Addressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity and computational needs. For example, hundreds of parameters are involved in the calibration of Activity-Based
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Extrinsic and Temporal Calibration of Automotive Radar and 3-D LiDAR in Factory and On-Road Calibration Settings IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-09-08 Chia-Le Lee, Chun-Yu Hou, Chieh-Chih Wang, Wen-Chieh Lin
While automotive radars are widely used in ADAS and autonomous driving, extrinsic and temporal calibration of automotive radars with other sensors is still daunting due to the sparsity, uncertainty, and missing elevation angles of automotive radar measurements. We propose a target-based calibration approach of 3D automotive radar and 3D LiDAR that performs extrinsic and temporal calibration in both
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A Multifaceted Equity Metric System for Transportation Electrification IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-09-04 Takahiro Tsukiji, Ning Zhang, Qinhua Jiang, Brian Yueshuai He, Jiaqi Ma
Transportation electrification offers societal benefits like reduced emissions and decreased dependence on fossil fuels. Understanding the deployment of electric vehicles (EVs) and electric vehicle supply equipment (EVSE) has been a popular focus, however, achieving their equitable distribution in the transportation system remains a challenge for successful electrification. To address this issue, this
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Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-08-24 Tânia Fontes, Francisco Murços, Eduardo Carneiro, Joel Ribeiro, Rosaldo J. F. Rossetti
This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle large-scale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP
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Mobility Analytics of Fans During the 2021 FIFA Arab Cup™ Football Tournament in Qatar IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-08-09 Jassuer Abidi, Fethi Filali
The FIFA Arab Cup, a test event for the 2022 FIFA World Cup, took place in Qatar from November to December 2021. The event showcased 32 matches across six venues that will also be utilized in the World Cup. This paper presents a groundbreaking spatiotemporal analysis of traffic mobility during the event, using data collected from WaveTraf road sensors. The sensors detect and track Bluetooth and WiFi-enabled
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A Hierarchical Framework for Multi-Lane Autonomous Driving Based on Reinforcement Learning IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-08-01 Xiaohui Zhang, Jie Sun, Yunpeng Wang, Jian Sun
This paper proposes a hierarchical framework integrating deep reinforcement learning (DRL) and rule-based methods for multi-lane autonomous driving. We define an instantaneous desired speed (IDS) to mimic the common motivation for higher speed in different traffic situations as an intermediate action. High-level DRL is utilized to generate IDS directly, while the low-level rule-based policies including
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Collective Perception: A Delay Evaluation With a Short Discussion on Channel Load IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-08-01 Christoph Pilz, Peter Sammer, Esa Piri, Udo Grossschedl, Gerald Steinbauer-Wagner, Lukas Kuschnig, Alina Steinberger, Markus Schratter
Automated vehicles and vehicle-to-everything (V2X) communication open the window for sharing of sensor data. This paper aims to provide a systematic view of the delay chain involved. We implemented collective perception (CP) into two street legal automated driving demonstrators (ADDs) to provide insight into the components’ delay. The implementation allowed us to gather highly accurate Quality of Service
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Three-Dimensional Urban Path Planning for Aerial Vehicles Regarding Many Objectives IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-07-27 Nikolas Hohmann, Sebastian Brulin, Jürgen Adamy, Markus Olhofer
Planning flight paths for unmanned aerial vehicles in urban areas requires consideration of safety, legal, and economic aspects as well as attention to social factors for gaining public acceptance. To solve this many-objective path planning problem in the three-dimensional space, we propose a hybrid framework combining an exact Dijkstra search and a metaheuristic evolutionary optimization. Given a
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Real-Time Traffic State Measurement Using Autonomous Vehicles Open Data IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-07-26 Zhaohan Wang, Profita Keo, Meead Saberi
Autonomous vehicle (AV) technologies are expected to disrupt the existing urban transportation systems. AVs’ multi-sensor system can generate large amount of data, often used for localization and safety purposes. This study proposes and demonstrates a practical framework for real-time measurement of local traffic states using LiDAR data from AVs. Fundamental traffic flow variables including volume
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Maximum Acceptable Risk as Criterion for Decision-Making in Autonomous Vehicle Trajectory Planning IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-07-26 Maximilian Geisslinger, Rainer Trauth, Gemb Kaljavesi, Markus Lienkamp
Autonomous vehicles are being developed to make road traffic safer in the future. The time when autonomous vehicles are actually safe enough to be used in real traffic is a current subject of discussion between industry, science, and society. In our work, we propose a new approach to the risk assessment of autonomous vehicles based on risk-benefit analysis, as it is already established in other areas
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Robust Perception and Visual Understanding of Traffic Signs in the Wild IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-07-25 Rodolfo Valiente, Darren Chan, Alan Perry, Joshua Lampkins, Sasha Strelnikoff, Jiejun Xu, Alireza Esna Ashari
As autonomous vehicles (AVs) become increasingly prevalent on the roads, their ability to accurately interpret and understand traffic signs is crucial for ensuring reliable navigation. While most previous research has focused on addressing specific aspects of the problem, such as sign detection and text extraction, the development of a comprehensive visual processing method for traffic sign understanding
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Fairness-Enhancing Deep Learning for Ride-Hailing Demand Prediction IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-07-21 Yunhan Zheng, Qingyi Wang, Dingyi Zhuang, Shenhao Wang, Jinhua Zhao
Short-term demand forecasting for on-demand ride-hailing services is a fundamental issue in intelligent transportation systems. However, previous research predominantly focused on improving prediction accuracy, ignoring fairness issues such as systematic underestimations of travel demand in disadvantaged neighborhoods. This study investigates how to measure, evaluate, and enhance prediction fairness
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High-Definition Maps: Comprehensive Survey, Challenges, and Future Perspectives IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-07-14 Gamal Elghazaly, Raphaël Frank, Scott Harvey, Stefan Safko
In cooperative, connected, and automated mobility (CCAM), the more automated vehicles can perceive, model, and analyze the surrounding environment, the more they become aware and capable of understanding, making decisions, as well as safely and efficiently executing complex driving scenarios. High-definition (HD) maps represent the road environment with unprecedented centimetre-level precision with
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DNN-Based Map Deviation Detection in LiDAR Point Clouds IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-07-11 Christopher Plachetka, Benjamin Sertolli, Jenny Fricke, Marvin Klingner, Tim Fingscheidt
In this work we present a novel deep learning-based approach to detect and specify map deviations in erroneous or outdated high-definition (HD) maps using both sensor and map data as input to a deep neural network (DNN). We first present our proposed reference method for map deviation detection (MDD) utilizing a sensor-only DNN detecting traffic signs, traffic lights, and pole-like objects in LiDAR
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An Optimized Car-Following Behavior in Response to a Lane-Changing Vehicle: A Bézier Curve-Based Approach IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-06-30 Gihyeob An, Jun Han Bae, Alireza Talebpour
Sudden lane-changing maneuvers can disrupt the traffic flow. In this paper, we introduce an approach to optimize car-following behavior in response to a lane-changing vehicle in a connected driving environment. Our approach utilizes a quadratic Bézier curve in the time-space diagram to represent the car-following behavior. The algorithm adapts to sudden interruptions from the leading vehicle (i.e.
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Resilient Adaptive Finite-Time Fault-Tolerant Control for Heterogeneous Uncertain and Nonlinear Autonomous Connected Vehicles Platoons IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-06-29 Bianca Caiazzo, Dario Giuseppe Lui, Alberto Petrillo, Stefania Santini
This paper addresses the control problem of heterogeneous uncertain nonlinear autonomous vehicle platoons in the presence of adversarial threats arising in Vehicular Ad-hoc NETworks (VANET) during the information sharing process. As unpredictable faults and/or malicious attacks may affect the trustworthiness of the messages shared among vehicles, a suitable resilient control law, able to enhance the
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Signalling Design in Sensor-Assisted mmWave Communications for Cooperative Driving IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-06-21 Giovanni Ciaramitaro, Mattia Brambilla, Monica Nicoli, Umberto Spagnolini
Millimeter-Wave (mmWave) Vehicle-To-Vehicle (V2V) communications are a key enabler for connected and automated vehicles, as they support the low-latency exchange of control signals and high-resolution imaging data for maneuvering coordination. The employment of mmWave V2V communications calls for Beam Alignment and Tracking (BAT) procedures to ensure that the antenna beams are properly steered during
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Mitigation of ADS-B Spoofing Attack Impact on Departure Sequencing Through Modulated Synchronous Taxiing Approach IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-06-16 Mohd Ruzeiny Kamaruzzaman, Md Delwar Hossain, Yuzo Taenaka, Youki Kadobayashi
Apart from delay to flight arrivals, occurrence of ghost aircraft from ADS-B message injection attack will also cause delay to the departure operations. Moreover, if attacks are designed meticulously, departure operations can suffer substantially with extensive flight delays and cancellations. To mitigate this incident, we propose a custom method for taxiing-out which encompasses three key components
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3-D Vehicle Detection Enhancement Using Tracking Feedback in Sparse Point Clouds Environments IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-06-08 Yeqiang Qian, Xiaoliang Wang, Hanyang Zhuang, Chunxiang Wang, Ming Yang
In recent years, vehicle detection in intelligent transportation systems using 3D LIDAR point clouds based on deep neural networks has made substantial progress. However, when the point clouds are very sparse, the detection model cannot generate proposals efficiently, resulting in false negative results. Considering that the object tracking technology accurately predicts vehicles based on historical
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HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-06-02 Saumya Gupta, Mohamed H. Zaki, Adan Vela
The paper proposes a generative pedestrian trajectory modeling framework named HISS - Human Interactions in Shared Space. The trajectory modeling framework is based on a receding horizon optimization approach utilizing pedestrian behavior and interactions that seeks to capture pedestrian trajectory planning and execution. The benefit of the proposed dynamic optimization trajectory generation approach
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Learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-05-29 Haotian Zheng, Chaoyi Chen, Shuai Li, Sifa Zheng, Shengbo Eben Li, Qing Xu, Jianqiang Wang
Energy-function-based safety certificates can provide demonstrable safety for complex automatic control systems used in safety control tasks. However, recent studies on learning-based energy function synthesis have only focused on feasibility, which can lead to over-conservatism and reduce controller efficiency. In this study, we propose using magnitude regularization techniques to enhance the efficiency
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Optimal Control and Station Relocation of Vehicle-Sharing Systems With Distributed Dynamic Pricing IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-05-24 Kazunori Sakurama
This paper addresses an optimal control problem for one-way car-sharing systems under dynamic pricing. One-way car-sharing service allows customers to return vehicles to any available station. Although this service provides great convenience to customers, it has a serious drawback such that vehicles can be unevenly parked and some stations can be unavailable. To solve this problem, dynamic pricing
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Mixed Traffic Flow State Detection: A Connected Vehicles-Assisted Roadside Radar and Video Data Fusion Scheme IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-05-23 Rui Chen, Jiameng Ning, Yu Lei, Yilong Hui, Nan Cheng
An increasing number of connected vehicles (CVs) driving together with regular vehicles (RVs) on the road is an inevitable stage of future traffic development. As accurate traffic flow state detection is essential for ensuring safe and efficient traffic, the level of road intelligence is being enhanced by the mass deployment of roadside perception devices, which is capable of sensing the mixed traffic
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Engaging the Crowd in Sensing for Smart Mobility: A Discrete Choice Experiment IEEE Open J. Intell. Transp. Syst. Pub Date : 2023-05-17 Ria Johanna van Den Boogert, Aaron Yi Ding
With rising numbers of people living in cities leading to increasing congestion and pollution, mobile crowdsensing applications form a potential solution to make transport systems smarter and more efficient. However, sharing data comes with the risk of private information being disclosed. Therefore, a clear incentive is necessary to motivate smart device users to share data about their activities and