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IEEE Intelligent Transportation Systems Magazine
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Let Us Reinstate the Public Trust in the Safety Benefits of Automated Vehicles [Editor’s Column]
Publication Year: 2022,Page(s):3 - 5
Message on Inauguration of the New ITSS President [President’s Message]
Publication Year: 2022,Page(s):6 - 7
Special Section on Safety of Automated Driving in Intelligent Transportation Systems [Guest Editorial]
Hong Wang;Chen Lv;Xiaosong Hu;Yisheng Lv;Lingxi Li;Ehsan Hashemi;Meng Wang
Publication Year: 2022,Page(s):8 - 9
Cited by: Papers (4)
Acclimatizing the Operational Design Domain for Autonomous Driving Systems
Publication Year: 2022,Page(s):10 - 24
Cited by: Papers (37)
The operational design domain (ODD) of an automated driving system (ADS) can be used to confine the environmental scope of where the ADS is safe to execute. ODD acclimatization is one of the necessary steps for validating vehicle safety in complex traffic environments. This article proposes an approach and architectural design to extract and enhance the ODD of the ADS based on the task scenario an...Show More
Distributed Stochastic Model Predictive Control for Heterogeneous Vehicle Platoons Subject to Modeling Uncertainties
Publication Year: 2022,Page(s):25 - 40
Cited by: Papers (36)
This article proposes a distributed stochastic model predictive control (DSMPC) algorithm for vehicle platoons in which every vehicle is subject to modeling uncertainties. When the distributions of the uncertainties are available, DSMPC enables a less conservative treatment of the uncertainties than is commonly used for distributed robust MPC. The DSMPC problem is transformed into a deterministic ...Show More
Jam-Absorption Driving Strategy for Improving Safety Near Oscillations in a Connected Vehicle Environment Considering Consequential Jams
Publication Year: 2022,Page(s):41 - 52
Cited by: Papers (12)
This article proposes an optimal jam-absorption driving (JAD) strategy, which not only prevents the occurring traffic oscillation but also avoids the consequential jam caused by the JAD vehicle. We first conducted a theoretical analysis to estimate the relationship between velocity and space headway within kinematic waves. Then an optimization function was formed to minimize the system travel dela...Show More
Human–Machine Adaptive Shared Control for Safe Driving Under Automation Degradation
Publication Year: 2022,Page(s):53 - 66
Cited by: Papers (38)
In this article, a human–machine adaptive shared control method is proposed for automated vehicles (AVs) under automation performance degradation. First, a novel risk assessment module is proposed to monitor driving behavior and evaluate automation performance degradation for AVs. Then, an adaptive control authority allocation module is developed. In the event of any performance degradation, the c...Show More
A Cloud Big-Data-Driven Dynamics Control Approach for Unmanned Ground Vehicles for Safety Improving
Publication Year: 2022,Page(s):67 - 79
Cited by: Papers (1)
Unmanned ground vehicles (UGVs) are important elements in the future intelligent transportation system (ITS), which is supposed to conduct various tasks for human beings. Obviously, the traditional edge-side (vehicle-side) dynamics control system strictly limits the performance improvement of multifunctional UGVs in various applications. Therefore, this article proposes a future ITS with multifunc...Show More
Adaptive Finite-Time Trajectory Tracking Control of Autonomous Vehicles That Experience Disturbances and Actuator Saturation
Hongbo Gao;Zhen Kan;Fei Chen;Zhengyuan Hao;Xi He;Hang Su;Keqiang Li
Publication Year: 2022,Page(s):80 - 91
Cited by: Papers (6)
This article presents a finite-time tracking control scheme for autonomous vehicles. Trajectory tracking is difficult to control due to dynamic couplings and various disturbances and uncertainties. To cope with a lumped disturbance, a fuzzy logic system is designed. In particular, a novel adaptive algorithm is constructed to adjust the gain online, and it works well even without the prior informat...Show More
A Revised Logit Model for Stochastic Traffic Assignment With a Relatively Stable Dispersion Parameter
Publication Year: 2022,Page(s):92 - 104
Cited by: Papers (2)
The optimal value of the embedded dispersion parameter in the logit-based stochastic traffic assignment (STA) model significantly impacts predicted network flows. It also varies dramatically for different systems. This prevents its application, especially when real-world network flow data are unavailable for model calibration. To address the problem, this article proposes a revised logit model. Ra...Show More
A Methodology for Shuttle Scheduling in Airports That Ensures Mitigating Arriving Passenger Congestion Under Uncertain Demand
Publication Year: 2022,Page(s):105 - 114
Cited by: Papers (3)
Passenger congestion at the airport is a topic of significance in the field of air transportation in our day. In this study, a three-phased methodology, which warrants mitigating arriving passenger congestion at the airport by scheduling shuttle vehicles even under uncertain passenger demand, is proposed. In the first phase, monthly demands are forecast via an appropriate model from the family of ...Show More
An Autonomous, Intelligent Sign Control System Using Wireless Communication and LED Signs for Rural and Suburban Roads
Antonio Torralba;Juan Pablo García-Martín;Juan Manuel González-Romo;María García-Castellano;José Peral-López;Ventura Pérez-Mira
Publication Year: 2022,Page(s):115 - 128
Cited by: Papers (6)
The number of road accidents remains too high. Different initiatives are being taken to reduce the total, whether acting on the vehicle, the driver, or the infrastructure. It is well known that most incidents occur in rural and suburban areas, which have light, unattended traffic. This work presents the Autonomous, Intelligent Sign Control System (AISCS), a robust framework based on wireless commu...Show More
Predicting Vacant Parking Space Availability: A Long Short-Term Memory Approach
Publication Year: 2022,Page(s):129 - 143
Cited by: Papers (25)
The accurate prediction of vacant parking space availability is becoming increasingly essential for assisting drivers to determine where to park in advance. It helps ease traffic pressure and reduce gas emission and pollution. This article proposes a novel multistep long short-term memory recurrent neural network (LSTM-NN) model to predict the number of the vacant parking spaces on the basis of hi...Show More
Predicted Network Equilibrium Model of Electric Vehicles With Stationary and Dynamic Charging Infrastructure on the Road Network
Publication Year: 2022,Page(s):144 - 155
Cited by: Papers (2)
With the rapid development of charging-while-driving technology, the deployments of charging lanes and direct current fast chargers (dcFCs) inevitably affect the charging and route choices behavior of battery electric vehicle (BEV) drivers. This article describes the first attempt to develop a predicted user equilibrium model on the road network considering both dcFC and charging lanes charging ch...Show More
Softwarization, Virtualization, and Machine Learning for Intelligent and Effective Vehicle-to-Everything Communications
Publication Year: 2022,Page(s):156 - 173
Cited by: Papers (18)
The concept of the 5G mobile network system has emerged in recent years as telecommunication operators and service providers look to upgrade their infrastructure and delivery modes to meet the growing demand. Concepts such as softwarization, virtualization, and machine learning will be key components as innovative and flexible enablers of such networks. In particular, paradigms such as software-de...Show More
Bus Travel Time Prediction Based on Ensemble Learning Methods
Gang Zhong;Tingting Yin;Linchao Li;Jian Zhang;Honghai Zhang;Bin Ran
Publication Year: 2022,Page(s):174 - 189
Cited by: Papers (7)
Buses have become indispensable in urban transportation systems, especially in developing countries. The prediction of bus travel time can provide essential information for passengers to coordinate their trip plans. By combining several prediction algorithms, ensemble learning methods have shown great potential for improving prediction accuracy in many research fields. In this article, ensemble le...Show More
Graph Attention LSTM: A Spatiotemporal Approach for Traffic Flow Forecasting
Publication Year: 2022,Page(s):190 - 196
Cited by: Papers (56)
This article investigates a traffic-flow forecasting problem based on long–short term memory (LSTM), an artificial recurrent neural network architecture used in deep learning. By representing the road network as an unweighted directed graph, the traffic flow prediction problem becomes how to capture the spatiotemporal dependencies among nodes in the graph. We present a novel graph-attention LSTM s...Show More
Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms
Publication Year: 2022,Page(s):197 - 208
Cited by: Papers (146)
Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system applications ranging from traffic simulations to driver assistance. The need for this ability is pronounced with the emergence of autonomous vehicles as they require the prediction of nearby vehicles’ trajectories to navigate safely and efficiently. Recent studies based on deep learning have greatly i...Show More
Road Roughness Estimation Through Smartphone-Measured Acceleration
Karol R. Opara;Karol Brzeziński;Marek Bukowicki;Katarzyna Kaczmarek-Majer
Publication Year: 2022,Page(s):209 - 220
Cited by: Papers (10)
Driving on an uneven road causes vibration of the vehicle. These vibrations affect the comfort of the road users but also convey information about the road profile. This study demonstrates the feasibility, accuracy, and repeatability of the accelerometer system for pavement evaluation that computes the international roughness index (IRI) using multiple smartphones to collect data. It uses quarter-...Show More
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