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Advances in Autonomous Driving and Smart Transportation

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 13162

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Special Issue Editors

Dr. Lalitha Dabbiru

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Guest Editor

Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS, USA
Interests: image segmentation; object detection and perception; autonomous driving technology

Dr. Christopher T. Goodin

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Guest Editor

Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS, USA
Interests: physics-based LIDAR simulations/supercomputing; simulation of autonomous ground vehicles; ground vehicle mobility and vehicle dynamics simulation; tire–soil interaction simulations/terrain impacts on mobility; parallel rendering, graphics, and radiative transfer

Special Issue Information

Dear Colleagues,

Research efforts to achieve autonomous driving capabilities have increased significantly in the last decade. The technology to empower autonomous vehicles with different sensors, including LiDAR, RADAR, ultrasonic devices, and cameras is developing at a rapid pace, and the data collected through these platforms are extremely useful for developing AI/ML models. One of the most significant advantages of autonomous cars is their potential to make roads safer by enabling them to navigate the environment safely and efficiently. This Special Issue invites contributions on the following topics.

Perception and sensing in adverse weather conditions like traversing in fog, rain, and snow, which is an important aspect to consider for safer navigation.
Advances in sensor fusion, perception enhancement, classification, and localization techniques are important solutions to enhance autonomous vehicles’ capabilities.
Deep learning models for object detection are extremely desirable in self-driving cars.
All supervised deep learning models require labeled datasets to train the algorithms. As the labeling effort is very cumbersome and time-consuming, advancements in auto-labeling techniques are of great importance to annotate large datasets.
Advances in vehicle connectivity solutions that connect vehicles to other vehicles (V2V), infrastructure (V2I), the electric network (V2N), and even pedestrians (V2P) are important for safer navigation.
Navigation of unmanned ground vehicles (UGVs) in off-road/unstructured environment is of prime concern in defense applications. Advances in obstacle detection and navigating safely in this complex terrain is a major challenge in off-road autonomous driving.

Dr. Lalitha Dabbiru
Dr. Christopher T. Goodin
Guest Editors

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Keywords

automated driving
machine learning
perception and sensing
object detection
adverse weather models

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Published Papers (9 papers)

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38 pages, 28331 KiB

Open AccessArticle

Robustness Benchmark Evaluation and Optimization for Real-Time Vehicle Detection Under Multiple Adverse Conditions

by Jianming Cai, Yifan Gao and Jinjun Tang

Appl. Sci. 2025, 15(9), 4950; https://doi.org/10.3390/app15094950 (registering DOI) - 29 Apr 2025

Abstract

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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18 pages, 2115 KiB

Open AccessArticle

A Vehicle–Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion

by Jianhu Liu, Ping Wang and Xia Wu

Appl. Sci. 2025, 15(6), 3399; https://doi.org/10.3390/app15063399 - 20 Mar 2025

Viewed by 313

Abstract

Vehicle-infrastructure cooperative perception enhances the perception capabilities of autonomous vehicles by facilitating the exchange of complementary information between vehicles and infrastructure. However, real-world environments often present challenges such as differences in sensor resolution and installation angles, which create a domain gap that complicates the integration of features from these two sources. This domain gap can hinder the overall performance of the perception system. To tackle this issue, we propose a novel vehicle–infrastructure cooperative perception network designed to effectively bridge the feature integration between vehicle and infrastructure sensors. Our approach includes a Multi-Scale Dynamic Feature Fusion Module designed to comprehensively integrate features from both vehicle and infrastructure across spatial and semantic dimensions. For feature fusion at each scale, we introduce the Multi-Source Dynamic Interaction Module (MSDI) and the Per-Point Self-Attention Module (PPSA). The MSDI dynamically adjusts the interaction between vehicle and infrastructure features based on environmental changes, generating enhanced interacting features. Subsequently, the PPSA aggregates these interacted features with the original vehicle–infrastructure features at the same spatial location. Additionally, we have constructed a real-world vehicle–infrastructure cooperative perception dataset, DZGSet, which includes multi-category annotations. Extensive experiments conducted on the DAIR-V2X and our self-collected DZGSet datasets demonstrate that our proposed method achieves Average Precision (AP) scores at IoU 0.5 of 0.780 and 0.652, and AP scores at IoU 0.7 of 0.632 and 0.493, respectively. These results indicate that our proposed method outperforms existing cooperative perception methods. Consequently, the proposed approach significantly improves the performance of cooperative perception, enabling more accurate and reliable autonomous vehicle operation. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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16 pages, 6255 KiB

Open AccessArticle

Development of a Path Tracker Based on a 4WS Vehicle for Low-Speed Automated Driving Systems

by Heung-Sik Park and Moon-Sik Kim

Appl. Sci. 2025, 15(6), 3043; https://doi.org/10.3390/app15063043 - 11 Mar 2025

Viewed by 499

Abstract

With the increasing demand for various autonomous driving services in urban environments, low-speed autonomous vehicles, such as autonomous shuttles and purpose-built vehicles, equipped with enhanced driving characteristics suitable for urban roads featuring narrow streets, intersections, congested traffic, and small radii, are emerging. In particular, the 4WS (four-wheel steering) system, which is being integrated into these vehicles, is designed to steer both the front and rear wheels. This system improves steering responsiveness and stability, providing maneuverability under various driving conditions and making it highly suitable for urban environments. However, the 4WS system involves complex dynamic modeling and poses challenges in designing a path tracker, especially if factors such as the vehicle’s turning radius and road curvature are not properly considered. To address these challenges, this paper proposes a path tracker for a low-speed autonomous driving system based on a 4WS system, optimized for the characteristics of urban roads to minimize the vehicle’s turning radius and enhance driving performance. The proposed path tracker independently controls the front and rear wheels and incorporates road curvature and vehicle turning radius as feedforward terms to improve the response performance of the path tracker. The performance of the proposed path tracker was evaluated through simulations and real-car experiments. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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21 pages, 957 KiB

Open AccessArticle

Human Trajectory Imputation Model: A Hybrid Deep Learning Approach for Pedestrian Trajectory Imputation

by Deb Kanti Barua, Mithun Halder, Shayanta Shopnil and Md. Motaharul Islam

Appl. Sci. 2025, 15(2), 745; https://doi.org/10.3390/app15020745 - 14 Jan 2025

Cited by 1 | Viewed by 1125

Abstract

Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges in adverse weather conditions, interference from other vehicles’ sensors and electronic devices, and signal reception failure, leading to incompleteness in the trajectory data. But for real-time decision making for autonomous driving, trajectory imputation is no less crucial. Previous attempts to address this issue, such as statistical inference and machine learning approaches, have shown promise. Yet, the landscape of deep learning is rapidly evolving, with new and more robust models emerging. In this research, we have proposed an encoder–decoder architecture, the Human Trajectory Imputation Model, coined HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model is evaluated using the Intersection drone the inD dataset, containing trajectory data at suitable altitudes, preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we utilize L1, MSE, and quantile and ADE loss. Our experiments demonstrate that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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16 pages, 4515 KiB

Open AccessArticle

Steering Assist Control for Bicycles with Variable Trail Effect

by Takaatsu Kihara, Yuzuki Sugasawa, Keigo Kuriyama and Masami Iwase

Appl. Sci. 2025, 15(1), 251; https://doi.org/10.3390/app15010251 - 30 Dec 2024

Viewed by 3447

Abstract

The purpose of this study is to realize a power steering control that restores the maneuverability of the bicycle by assisting in handlebar operation during loading according to the analysis of the effect of loading children and luggage in the front basket on maneuverability. Placing baggage and child in the front basket of a bicycle causes an increase in the moment of inertia around the handlebar axis and a change in the position of the center of gravity, and leads to a decrease in the maneuverability of the bicycle. To solve the issue, previous studies have contributed to improve the maneuverability by applying the assist torque proportional to the user’s steering one by a power steering mechanism. However, in general, bicycle steering is affected not only by handlebar operation but also by the roll angle of the bicycle body through the trail effect. Therefore, in this study, we propose to design a control system to realize the variable trail effect, which is related to the roll angle of the bicycle body, and use it as the steering assist. To verify the effectiveness of the proposed method, we developed a bicycle equipped with the power steering mechanism and implemented the proposed control algorithm. The behavior of the experimental bicycle during straight-line and slalom biking has been analyzed, and it has been shown that the control system with the variable trail effect can recover maneuverability when a weight is placed in the front basket. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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25 pages, 9789 KiB

Open AccessArticle

Comparing User Acceptance in Human–Machine Interfaces Assessments of Shared Autonomous Vehicles: A Standardized Test Procedure

by Ming Yan, Lucia Rampino and Giandomenico Caruso

Appl. Sci. 2025, 15(1), 45; https://doi.org/10.3390/app15010045 - 25 Dec 2024

Viewed by 3263

Abstract

Human–Machine Interfaces (HMIs) in autonomous driving technology have recently gained significant research interest in public transportation. However, most of the studies are biased towards qualitative methods, while combining quantitative and qualitative approaches has yet to receive commensurate attention in measuring user acceptance of design outcome evaluation. To the best of our knowledge, no standardized test procedure that combines quantitative and qualitative methods has been formed to evaluate and compare the interrelationships between different designs of HMIs and their psychological effects on users. This paper proposes a practical and comprehensive protocol to guide assessments of user acceptance of HMI design solutions. We first defined user acceptance and analyzed the existing evaluation methods. Then, specific ergonomic factors and requirements that the designed output HMI should meet were identified. Based on this, we developed a protocol to evaluate a particular HMI solution from in- and out-of-vehicle perspectives. Our theoretical protocol combines objective and subjective measures to compare users’ behavior when interacting with Autonomous Vehicles (AVs) in a virtual experimental environment, especially in public transportation. Standardized testing procedures provide researchers and interaction designers with a practical framework and offer theoretical support for subsequent studies. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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19 pages, 2402 KiB

Open AccessArticle

Autonomous Navigation for Personal Mobility Vehicles Considering Passenger Tolerance to Approaching Pedestrians

by Motonobu Omori, Hiroshi Yoshitake and Motoki Shino

Appl. Sci. 2024, 14(24), 11622; https://doi.org/10.3390/app142411622 (registering DOI) - 12 Dec 2024

Cited by 1 | Viewed by 826

Abstract

There are high expectations for autonomous personal mobility vehicles (PMVs) to support the mobility of older people. Autonomous navigation systems are being developed to assist mobility in public areas with mixed pedestrian traffic, such as airports and shopping malls. For autonomous navigation of PMVs, achieving both comfort and efficiency, even in crowded environments, is important. In this study, we focused on the characteristic of passenger tolerance, in which a passenger’s discomfort is relatively small concerning an approaching pedestrian. The objective was to propose an efficient autonomous navigation method without increasing passenger discomfort, considering the characteristics of passenger tolerance. First, the passenger tolerance characteristics were clarified through data analysis of a previous study’s dataset and a newly collected dataset. Next, a path-planning method considering the characteristics was proposed, and the proposed method was evaluated by numerical simulations. The evaluation results showed that the proposed method has a potential to achieve efficient autonomous navigation in crowded environments. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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15 pages, 4682 KiB

Open AccessArticle

MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications

by Ying Li, Wupeng Zhuang and Guangsong Yang

Appl. Sci. 2024, 14(22), 10667; https://doi.org/10.3390/app142210667 - 18 Nov 2024

Cited by 1 | Viewed by 1798

Abstract

With advancements in autonomous driving, LiDAR has become central to 3D object detection due to its precision and interference resistance. However, challenges such as point cloud sparsity and unstructured data persist. This study introduces MS3D (Multi-Scale Feature Fusion 3D Object Detection Method), a novel approach to 3D object detection that leverages the architecture of a 2D Convolutional Neural Network (CNN) as its core framework. It integrates a Second Feature Pyramid Network to enhance multi-scale feature representation and contextual integration. The Adam optimizer is employed for efficient adaptive parameter tuning, significantly improving detection performance. On the KITTI dataset, MS3D achieves average precisions of 93.58%, 90.91%, and 88.46% in easy, moderate, and hard scenarios, respectively, surpassing state-of-the-art models like VoxelNet, SECOND, and PointPillars. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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11 pages, 21341 KiB

Open AccessOpinion

Expanding Ground Vehicle Autonomy into Unstructured, Off-Road Environments: Dataset Challenges

by Stanton R. Price, Haley B. Land, Samantha S. Carley, Steven R. Price, Stephanie J. Price and Joshua R. Fairley

Appl. Sci. 2024, 14(18), 8410; https://doi.org/10.3390/app14188410 - 18 Sep 2024

Viewed by 1354

Abstract

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University’s Center for Advanced Vehicular Systems. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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