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IEEE Open Journal of Intelligent Transportation Systems
Volume 4: 2023
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IEEE Open Journal of the Intelligent Transportation Systems Society Publication Information
Publication Year: 2023,Page(s):C2 - C2
IEEE Open Journal of the Intelligent Transportation Systems Society Publication Information
Year: 2023 | Volume: 4
2023 Editorial IEEE Open Journal of Intelligent Transportation Systems
Publication Year: 2023,Page(s):1 - 1
Dear Authors and Readers, Welcome to the 2023 Volume of the IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS). It is my great pleasure and honor to serve as the second Editor-in-Chief (EiC) of OJ-ITS, starting from January 2023, taking over the position from the founding EiC Prof. Bart van Arem. I am really looking forward to working closely with Prof. Bilge Küçük Atasoy (newly appo...Show More
Lane Transformer: A High-Efficiency Trajectory Prediction Model
Zhibo Wang;Jiayu Guo;Zhengming Hu;Haiqiang Zhang;Junping Zhang;Jian Pu
Publication Year: 2023,Page(s):2 - 13
Cited by: Papers (26)
Trajectory prediction is a crucial step in the pipeline for autonomous driving because it not only improves the planning of future routes, but also ensures vehicle safety. On the basis of deep neural networks, numerous trajectory prediction models have been proposed and have already achieved high performance on public datasets due to the well-designed model structure and complex optimization proce...Show More
Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach
Publication Year: 2023,Page(s):14 - 29
Cited by: Papers (2)
While traffic volume data from loop detectors have been the common data source for link flow estimation, the detectors only cover a subset of links. These days, other data sources such as vehicle trajectory data collected from vehicle tracking sensors are also incorporated. However, trajectory data are often sparse in that the observed trajectories only represent a small subset of the whole popula...Show More
Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features
Syrus Gomari;Rohith Domakuntla;Christoph Knoth;Constantinos Antoniou
Publication Year: 2023,Page(s):30 - 47
Cited by: Papers (6)
On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill t...Show More
Anytime Tree-Based Trajectory Planning for Urban Driving
Publication Year: 2023,Page(s):48 - 57
Cited by: Papers (10)
The personal mobility of the future will be changed significantly by autonomous driving. To realize this vision, the complex task of trajectory planning needs to be solved. In this article, a novel planning concept, CarPre trajectory planning, based on Monte-Carlo tree search, is presented. Using a speed-dependent steering angle transformation, the state space of a kinematic single track model is ...Show More
Insufficiency-Driven DNN Error Detection in the Context of SOTIF on Traffic Sign Recognition Use Case
Publication Year: 2023,Page(s):58 - 70
Cited by: Papers (11)
Deep Neural Networks (DNNs) are used in various domains and industry fields with great success due to their ability to learn complex tasks from high-dimensional data. However, the data-driven approach within deep learning results in various DNN-specific insufficiencies (e.g., robustness limitations, overconfidence, lack of interpretability), which makes the usage in safety-critical applications, l...Show More
Boids Flocking Algorithm for Situation Assessment of Driver Assistance Systems
Christopher Knievel;Aldin Pejic;Lars Krüger;Christoph Ziegler;Jürgen Adamy
Publication Year: 2023,Page(s):71 - 82
Cited by: Papers (1)
Driver assistance systems are increasingly becoming part of the standard equipment of vehicles and thus contribute to road safety. However, as they become more widespread, the requirements for cost efficiency are also increasing, and so few and inexpensive sensors are used in these systems. Especially in challenging situations, this leads to the fact that target discrimination cannot be ensured wh...Show More
Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions
Publication Year: 2023,Page(s):83 - 100
Cited by: Papers (22)
This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safe...Show More
Characterization of Transient Communication Outages Into States to Enable Autonomous Fault Tolerance in Vehicle Platooning
Publication Year: 2023,Page(s):101 - 129
Cited by: Papers (12)
The benefits of platooning, e.g., fuel efficiency, road throughput enhancement, driver offload, etc., have sparked an interest in a more connected, intelligent, and sustainable transportation ecosystem. However, efficient platooning is realized through wireless communications, characterized by transient connectivity, which is caused by occasional packet losses. Being a safety-critical system of sy...Show More
Learning Policies for Automated Racing Using Vehicle Model Gradients
Nathan A. Spielberg;Maximilian Templer;John Subosits;J. Christian Gerdes
Publication Year: 2023,Page(s):130 - 142
Cited by: Papers (9)
Safe autonomous driving approaches should be capable of quickly and efficiently learning as professional drivers do, while also using all of the available road-tire friction for safety. Inspired by how skilled drivers learn, we demonstrate improvement from an initial optimization-generated racing trajectory using model-based reinforcement learning. By using a simple physics-based dynamics model an...Show More
Research on Global Ship Path Planning Method Based on Improved Ant Colony Algorithm
Publication Year: 2023,Page(s):143 - 152
Cited by: Papers (8)
To solve the global path planning problem of the ship in the static and dynamic environment, we propose an improved ant colony algorithm to plan the ship’s navigation path. We use the artificial potential field method to compute the force direction of the ship at the initial iteration stage. The attraction potential field function is modified to improve the iteration efficiency of the hybrid ant c...Show More
Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data
Josef Hoppe;Felix Schwinger;Henrik Haeger;Jonas Wernz;Matthias Jarke
Publication Year: 2023,Page(s):153 - 174
Cited by: Papers (12)
Passengers of public transportation nowadays expect reliable and accurate travel information. The need for occupancy information is becoming more prevalent in intelligent public transport systems as people started avoiding overcrowded vehicles during the COVID-19 pandemic. Furthermore, public transportation companies require accurate occupancy forecasts to improve service quality. We present a nov...Show More
An Architecture for Experiments in Connected and Automated Vehicles
Publication Year: 2023,Page(s):175 - 186
Cited by: Papers (14)
Rapid prototyping of Connected and Automated Vehicles (CAV) is challenging because of the physical distribution of vehicles. Furthermore, experiments with CAV may be subject to external influences which prevent reproducibility. This article presents an architecture for the experimental testing of CAVs, focusing on decision-making. Our architecture for experiments of CAV is strictly modular and hie...Show More
Constrained Control Allocation Improving Fault Tolerance of a Four Wheel Independently Driven Articulated Vehicle
Publication Year: 2023,Page(s):187 - 203
Cited by: Papers (4)
In articulated vehicles steering is accomplished by adjusting the articulation angle. Commonly a steering actuator is used to pivot the two vehicle sections. This actuator can be dispensed with if the pivoting is controlled by selective distribution of the drive torques to the individually driven wheels instead. Since steering is a safety-critical function, it must be ensured even if one of the dr...Show More
On the Characterization of Eco-Friendly Paths for Regional Networks
Publication Year: 2023,Page(s):204 - 215
Cited by: Papers (2)
Macroscopic traffic models represent a promising tool to design strategies for ecological routing. To benefit from this tool, we must first characterize the relationship between path emissions and distance traveled or travel time on aggregated networks, i.e., a regional network. This paper investigates this relationship between two toy networks and a real urban network representing the city of Inn...Show More
Driver Visual Attention Estimation Using Head Pose and Eye Appearance Information
Publication Year: 2023,Page(s):216 - 231
Cited by: Papers (9)
In autonomous, as well as manually operated vehicles, monitoring the driver visual attention provides useful information about the behavior, intent and vigilance level of the driver. The gaze of the driver can be formulated in terms of a probabilistic visual map representing the region around which the driver’s attention is focused. The area of the estimated region changes based on the level of co...Show More
Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control
Mahdi Razzaghpour;Rodolfo Valiente;Mahdi Zaman;Yaser P. Fallah
Publication Year: 2023,Page(s):232 - 243
Cited by: Papers (13)
Utilizing Vehicle-to-everything (V2X) communication technologies, vehicle platooning systems are expected to realize a new paradigm of cooperative driving with higher levels of traffic safety and efficiency. Connected and Autonomous Vehicles (CAVs) need to have proper awareness of the traffic context. The cooperative platoon’s performance will be influenced by the communication strategy. In partic...Show More
RSG-GCN: Predicting Semantic Relationships in Urban Traffic Scene With Map Geometric Prior
Publication Year: 2023,Page(s):244 - 260
Cited by: Papers (5)
Automated identification of the relationships between traffic actors and surrounding objects, in order to describe their behavior and predict their intentions, has become the focus of increasing attention in the field of autonomous driving. Therefore, in this work, we propose a Road Scene Graphs-Graph Convolutional Network (RSG-GCN) as a novel, graph-based model for predicting the topological grap...Show More
Simulation Evaluation of Vehicle Movement Model Using Spatio-Temporal Grid Reservation for Automated Valet Parking
Publication Year: 2023,Page(s):261 - 266
Cited by: Papers (4)
Automated valet parking (AVP) systems aim to use automated driving technology to park a vehicle from the passenger’s boarding/exiting location to a parking space in a parking lot and recall the vehicle from the parking space to the boarding/exiting location. For multiple vehicles to move efficiently through a parking lot, travel must be mediated between each vehicle. We have proposed a driving con...Show More
Fuzzy-Based Variable Speed Limits System Under Connected Vehicle Environment: A Simulation-Based Case Study in the City of Naples
Angelo Coppola;Luca Di Costanzo;Luigi Pariota;Gennaro Nicola Bifulco
Publication Year: 2023,Page(s):267 - 278
Cited by: Papers (9)
This paper handles the problem of controlling speed limits on freeways in a connected traffic environment to reduce traffic congestion and improve both the operational and environmental performance of the road network. In order to achieve this objective, we present a Variable Speed Limit (VSL) system that utilizes fuzzy logic, which adjusts the speed limits that connected vehicles must comply with...Show More
On the Limitations of Physics-Informed Deep Learning: Illustrations Using First-Order Hyperbolic Conservation Law-Based Traffic Flow Models
Publication Year: 2023,Page(s):279 - 293
Cited by: Papers (11)
Since its introduction in 2017, physics-informed deep learning (PIDL) has garnered growing popularity in understanding the systems governed by physical laws in terms of partial differential equations (PDEs). However, empirical evidence points to the limitations of PIDL for learning certain types of PDEs. In this paper, we (a) present the challenges in training PIDL architecture, (b) contrast the p...Show More
Implementation of a Model-Oriented Approach for Supporting Safe Integration of GNSS-Based Virtual Balises in ERTMS/ETCS Level 3
Publication Year: 2023,Page(s):294 - 310
Cited by: Papers (6)
Moving Block is a railway signaling concept that paves the way for increasing line capacity while reducing maintenance and operating costs. Its implementation relies on autonomous solutions for train localization, mainly based on GNSS technology. However, the introduction of such technological innovations leads to the emergence of new risks. These risks need to be investigated meticulously, and so...Show More
Infrastructure-Based Digital Twins for Cooperative, Connected, Automated Driving and Smart Road Services
Elvira Thonhofer;Simon Sigl;Martin Fischer;Fin Heuer;Andreas Kuhn;Jacqueline Erhart;Manfred Harrer;Wolfgang Schildorfer
Publication Year: 2023,Page(s):311 - 324
Cited by: Papers (24)
Driving requires continuous decision making from a driver taking into account all available relevant information. Automating driving tasks also automates the related decisions. However, humans are very good at dealing with bad quality, fuzzy, informal and incomplete information, whereas machines generally require solid quality information in a formalized format. Therefore, the development of autom...Show More
Contact Information
Editor-in-Chief
Associate Professor, Samueli School of Engineering; Director, UCLA Mobility Lab; Associate Director, UCLA Institute of Transportation Studies
University of California - Los Angeles
Los Angeles
USA
jiaqima@ucla.eduContact Information
Editor-in-Chief
Associate Professor, Samueli School of Engineering; Director, UCLA Mobility Lab; Associate Director, UCLA Institute of Transportation Studies
University of California - Los Angeles
Los Angeles
USA
jiaqima@ucla.eduIEEE Account
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