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IEEE Transactions on Intelligent Transportation Systems
Issue 3 • March-2025
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Table of Contents
Year: 2025 | Volume: 26 | Issue: 3
IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY
Year: 2025 | Volume: 26 | Issue: 3
Scanning the Issue
Year: 2025 | Volume: 26 | Issue: 3
A Multifaceted Analysis of Intelligent Vehicle Route Optimization
Publication Year: 2025,Page(s):2833 - 2850
In the constantly changing realm of logistics and transportation management, the incorporation of Information and Communication Technology (ICT) has catalyzed transformative paradigm shifts in the approach and resolution of Vehicle Route Optimization (VRO). The current scientometric research paper embarks on a comprehensive exploration of the scholarly endeavors acquired from the Scopus database i...Show More
A Guide to Image- and Video-Based Small Object Detection Using Deep Learning: Case Study of Maritime Surveillance
Aref Miri Rekavandi;Lian Xu;Farid Boussaid;Abd-Krim Seghouane;Stephen Hoefs;Mohammed Bennamoun
Publication Year: 2025,Page(s):2851 - 2879
Detecting small objects in optical images and videos is a significant challenge in numerous intelligent transportation and autonomous systems. State-of-the-art generic object detection methods fail to accurately localize and identify such small objects (e.g., pedestrians, small vehicles, obstacles). Because small objects occupy only a small area in the input image (e.g., $32 \times 32$ pixels or...Show More
Uncertainty Quantification for Safe and Reliable Autonomous Vehicles: A Review of Methods and Applications
Publication Year: 2025,Page(s):2880 - 2896
In the past decade, deep learning has been widely applied across various fields. However, its applicability in open-world scenarios is often limited due to the lack of quantifying uncertainty in both data and models. In recent years, a multitude of uncertainty quantification (UQ) approaches for neural networks have emerged and found applications in safety-critical domains such as autonomous vehicl...Show More
Recent Estimation Techniques of Vehicle-Road-Pedestrian States for Traffic Safety: Comprehensive Review and Future Perspectives
Cheng Tian;Chao Huang;Yan Wang;Edward Chung;Anh-Tu Nguyen;Pak Kin Wong;Wei Ni;Abbas Jamalipour;Kai Li;Hailong Huang
Publication Year: 2025,Page(s):2897 - 2920
Accurate and real-time acquisition of vehicular system dynamic states, road surface conditions, and motion states of surrounding participants is crucial for the safety, passenger comfort, and operational efficiency of autonomous vehicles (AVs) and connected automated vehicles (CAVs). In recent years, a significant amount of research has contributed to the field of state estimation for vehicles, ro...Show More
Advancing Vulnerable Road Users Safety: Interdisciplinary Review on V2X Communication and Trajectory Prediction
Behzad Abdi;Sara Mirzaei;Morteza Adl;Severin Hidajat;Ali Emadi
Publication Year: 2025,Page(s):2921 - 2943
The advancements in Intelligent Transportation Systems have brought a heightened focus on safety, driven by innovative solutions like Vehicle-to-Everything (V2X) communication, Advanced Driver Assistance Systems(ADAS), and Cooperative Intelligent Transport Systems (C-ITS). Ensuring the safety of vulnerable road users (VRUs) remains a top priority in the transportation sector, and harnessing these ...Show More
An Efficient Deep Spatio-Temporal Context Aware Decision Network (DST-CAN) for Predictive Manoeuvre Planning on Highways
Jayabrata Chowdhury;Suresh Sundaram;Nishanth Rao;Narasimman Sundararajan
Publication Year: 2025,Page(s):2944 - 2954
The safety and efficiency of an Autonomous Vehicle (AV) manoeuvre planning heavily depend on the future trajectories of surrounding vehicles. If an AV can predict its surrounding vehicles’ future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) for predictive manoeuvre decisions for AVs on hi...Show More
Calibration-Free Driver Drowsiness Classification With Prototype-Based Multi-Domain Mixup
Publication Year: 2025,Page(s):2955 - 2966
Drowsy driving is one of the greatest threats to road safety, which increases the importance of intelligent systems that can monitor driver drowsiness. Electroencephalogram (EEG)–based monitoring systems have gained attention because EEG is known to directly measure brain activities that reflect the mental state of the driver. However, calibration is necessary before using the system because EEG s...Show More
Design of a Cost Effective Spatial Image Registration System for Augmented Reality in Vehicular Applications
Publication Year: 2025,Page(s):2967 - 2976
The paper describes the design and validation of a spatial image registration algorithm for a vehicular Head-Mounted Augmented Reality (AR) system. AR can considerably improve the driving experience by increasing the driver’s situational awareness. AR can only work if stable and realistic holograms are generated. The process of generating the holograms so that they appear in a specific position in...Show More
Adaptive Haptic Assistance Control Considering Individual Driver’s Arm Characteristics
Publication Year: 2025,Page(s):2977 - 2987
To improve the overall performance of human-vehicle cooperation and enhance the drivers’ confidence in the advanced driver assistance system (ADAS), an adaptive haptic assistance control scheme for the steer-by-wire (SBW) vehicle is presented in this paper. A comprehensive human-vehicle system model is built, including vehicle dynamics, the SBW model, and the driver’s arm neuromuscular dynamics mo...Show More
Optimized Long Short-Term Memory Network for LiDAR-Based Vehicle Trajectory Prediction Through Bayesian Optimization
Publication Year: 2025,Page(s):2988 - 3003
In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on user expertise and prior knowledge, while newer deep learning approaches, such as Long Short-Term Memory (LSTM) networks, also face challenges related to human intervention and subjective hyperparameter selection. This study proposes a systematic approach for Light Detection and Ranging (LiDAR)-based ...Show More
A Data-Driven Dynamics Simulation Model for Railway Vehicles Based on Lightweight 3DCNN With Physics-Informed Constraints
Publication Year: 2025,Page(s):3004 - 3015
The dynamics simulation of complex railway vehicles requires a dedicated vehicle model, such as multi-body dynamics model. However, the multi-body model is time-consuming in long-distance simulation due to its computational complexity. This issue can be alleviated by using a data-driven vehicle dynamics model due to its effective generalization and computational speed. Firstly, the construction of...Show More
Curricular Subgoals for Inverse Reinforcement Learning
Shunyu Liu;Yunpeng Qing;Shuqi Xu;Hongyan Wu;Jiangtao Zhang;Jingyuan Cong;Tianhao Chen;Yun-Fu Liu;Mingli Song
Publication Year: 2025,Page(s):3016 - 3027
Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning. To promote expert-like behavior, existing IRL methods mainly focus on learning global reward functions to minimize the trajectory difference between the imitator and the expert. However, these globa...Show More
Binocular-Separated Modeling for Efficient Binocular Stereo Matching
Publication Year: 2025,Page(s):3028 - 3038
Binocular stereo matching is a crucial task in autonomous driving for accurately estimating the depth information of objects and scenes. This task, however, is challenging due to various ill-posed regions within binocular image pairs, such as repeated textures and weak textures which present complex correspondences between the points. Existing methods extract features from binocular input images m...Show More
Time-Aware and Direction-Constrained Collective Spatial Keyword Query
Publication Year: 2025,Page(s):3039 - 3055
Collective spatial keyword query (CoSKQ) is an important variant of spatial keyword queries and has become a research hotspot. In real life, user behavior usually has a certain directionality, so they may want to obtain the result object that conforms to a specific direction, which is what the direction-constrained query studies. In addition, query time information also plays an important role in ...Show More
OST-HGCN: Optimized Spatial-Temporal Hypergraph Convolution Network for Trajectory Prediction
Publication Year: 2025,Page(s):3056 - 3070
Pedestrian trajectory prediction is a key component for various applications that involve human and vehicle interactions, such as autonomous driving, traffic management and smart city planning. Existing methods based on graph neural networks have limited ability to capture group interactions and precisely model complex associations among multi-agents. To solve these problems, we propose OST-HGCN, ...Show More
An Empirical Study of Ground Segmentation for 3-D Object Detection
Hongcheng Yang;Dingkang Liang;Zhe Liu;Jingyu Li;Zhikang Zou;Xiaoqing Ye;Xiang Bai
Publication Year: 2025,Page(s):3071 - 3083
The ratio of foreground and background points directly impacts the accuracy and speed of the lidar-based 3D object detection methods. However, existing methods generally ignore the impact of ground points. Although some traditional ground segmentation algorithms are available to remove ground point clouds, they usually suffer from over-segmentation, which leads to a sub-optimal and even worse perf...Show More
Co-Evolving Traffic State Parameters Prediction Based on Mechanism-Data Blending Driven Deep Learning
Publication Year: 2025,Page(s):3084 - 3100
Traffic state prediction, a classical task for traffic management, is a central component of intelligent transport systems to maintain safe and efficient operation. While extensive and intensive research has been conducted on traffic state prediction, most studies have concentrated on enhancing the accuracy of specific traffic state parameters. However, traffic state is a co-evolutionary multivari...Show More
Optimized Feature Points and Keyframe Methods for VSLAM in High-Dynamic Indoor Environments
Zhuhua Hu;Wenlu Qi;Kunkun Ding;Hao Qi;Yaochi Zhao;Xuebo Zhang;Mingfeng Wang
Publication Year: 2025,Page(s):3101 - 3114
VSLAM is one of the key technologies for indoor mobile robots, used to perceive the surrounding environment, achieve accurate positioning and mapping. However, traditional VSLAM algorithms based on the assumption of a static environment still face certain challenges. The movement, occlusion, and appearance changes of dynamic objects can lead to feature point-matching errors, making data associatio...Show More
Stochastic Calibration of Automated Vehicle Car-Following Control: An Approximate Bayesian Computation Approach
Jiwan Jiang;Yang Zhou;Ghazaleh Jafarsalehi;Xin Wang;Soyoung Ahn;John D. Lee
Publication Year: 2025,Page(s):3115 - 3127
This paper presents a stochastic calibration method based on Approximate Bayesian Computation (ABC). This method is applied to calibrate two car-following control models: linear control and model predictive control (MPC). The method is likelihood-function-free, where the likelihood function is replaced by simulation to approximate the posterior distribution of model parameters. This structure affo...Show More
An Accelerated Filter for Critical Scenario Identification in Automated Driving Function Testing: A Model-Free Approach
Publication Year: 2025,Page(s):3128 - 3146
Automated Vehicle (AV) safety is a critical issue and appeals to worldwide focus. To ensure AV safety, AV functions should be tested and evaluated in an enormous number of scenarios. Since such AV testing is time-consuming, scenario filters have been developed to identify safety-critical scenarios and omit ordinary ones. However, the scenarios identified by these filters do not uniquely match the ...Show More
Service-Oriented Edge Collaboration: Digital Twin Enabled Edge Collaboration for Composite Services in AVNs
Yilong Hui;Xiaoqing Ma;Changle Li;Nan Cheng;Rui Chen;Zhisheng Yin;Tom H. Luan;Guoqiang Mao
Publication Year: 2025,Page(s):3147 - 3160
Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combi...Show More
Multimodal Transport Scheme Optimization and Capacity Allocation Considering Customer Classification
Publication Year: 2025,Page(s):3161 - 3174
Although multimodal transport has been widely developed, the multimodal transport operation system is still not as mature as other single modes of transport. Currently, the optimization of multimodal transport solutions and the allocation of capacity are planned as two separate parts, and the research on customer classification of multimodal transport is insufficient. The current bottleneck in mul...Show More
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