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Vehicles
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AI-Empowered Assisted and Autonomous Driving
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AI-Empowered Assisted and Autonomous Driving

A Special Issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 20 October 2026 | Viewed by 14309

Editors

Dr. Xubin Sun

E-Mail Website
Guest Editor
School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Interests: train energy-efficient control; driver advisory system; train scheduling and control; utilization of regenerative braking energy
Dr. Weifeng Zhong

E-Mail Website
Guest Editor
School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
Interests: train energy-efficient control; maglev train control

Special Issue Information

Dear Colleagues,

The fast-paced development of AI technologies provides significant opportunities for vehicle intelligence. AI-empowered assisted and autonomous driving technologies are the latest developments in transportation. These advancements can redefine the mobility, safety, and efficiency of the transportation system, offering a glimpse into a world where human error, a leading cause of traffic accidents, is significantly reduced.
Assisted driving, also known as semi-autonomous driving, leverages AI to enhance driver capabilities and vehicle performance. AI systems in these vehicles function as co-pilots, providing real-time assistance to the driver through Advanced Driver-Assistance Systems (ADASs). 
 
Autonomous driving, on the other hand, takes the concept of AI in vehicles to the next level. Fully autonomous vehicles, also known as self-driving vehicles, operate without the need for human intervention.

At the brink of this technological revolution, AI-empowered assisted and autonomous driving will transform our transportation networks. The marriage of AI and automotive technology is not only about convenience but about creating a safer, more efficient, and more inclusive mobility ecosystem for the future.

Dr. Xubin Sun
Dr. Weifeng Zhong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent train control
  • ADAS
  • autonomous driving
  • enforcement learning
  • deep learning
  • energy saving

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Published Papers (7 papers)

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Research

19 pages, 2781 KB  
Article
Open-World Critical Scenario Recognition and Maneuver-Level Generation for Autonomous Driving Simulation Testing
by Weijun Dai, Changhui Liu, Bo Li, Jie Zhang, Hongbin Wang, Lihui Tang, Siqi Peng and Shan Zhu
Vehicles 2026, 8(7), 155; https://doi.org/10.3390/vehicles8070155 - 6 Jul 2026
Viewed by 392
Abstract
As autonomous driving moves toward large-scale deployment, controllable and efficient simulation testing has become a primary means of ensuring system safety. However, in open-world environments, existing scenario catalogs often fail to cover the full spectrum of potential traffic situations, while rare yet high-risk [...] Read more.
As autonomous driving moves toward large-scale deployment, controllable and efficient simulation testing has become a primary means of ensuring system safety. However, in open-world environments, existing scenario catalogs often fail to cover the full spectrum of potential traffic situations, while rare yet high-risk critical scenarios are even harder to obtain. This scarcity renders traditional random sampling and parameter-sweeping strategies ineffective for identifying unknown risks. This study addresses two core challenges: (1) incomplete scenario catalogs hindering unknown critical scenario recognition and (2) insufficient critical samples, where generated scenarios struggle to balance physical realism and edge case coverage. To tackle the first challenge, we propose an open-world recognition method integrating transformers, random forests, and extreme value theory for precise unseen sample detection. Outlier and validity filtering ensure clustering reliability, and random forest activation patterns cluster unknown samples into meaningful groups to expand the scenario catalog. Experiments show the overall F1_macro improved by 2.3 percentage points over SOTA MDENet, with its clustering accuracy surpassing iterative-AutoNovel by 6.2 percentage points. For the second challenge, we introduce a reinforcement-learning-based maneuver-level generation method. It extracts maneuver semantics from trajectories, constructs a low-dimensional parameter space, and models parameter correlations via a multivariate multimodal distribution. A dual-layer LSTM agent with a composite reward iteratively optimizes policies toward high-risk edge scenarios. The results outperformed RLBE; longitudinal and lateral reconstruction errors were reduced by 32.7% and 15.3%, respectively, while high-risk time steps and the collision rate increased by 4.3% and 5.1%, respectively. Finally, we develop a CARLA-based scenario-driven simulation framework, integrating recognized and generated scenarios into closed-loop testing on high-risk road segments. CAS failure cases validate the generated scenarios’ physical feasibility and extreme challenge. Targeted augmentation of scarce critical scenarios enriches the test library and ensures broader coverage of real-world driving conditions. Full article
(This article belongs to the Special Issue AI-Empowered Assisted and Autonomous Driving)
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30 pages, 35650 KB  
Article
MLRP-YOLOv8n: A Vehicle Target Detection Algorithm That Integrates Mixed Local Channel Attention and Large Kernel Separable Attention
by Wenqiang Yu, Shui Yu, Qingmin Zhu and Fangpeng Ning
Vehicles 2026, 8(6), 127; https://doi.org/10.3390/vehicles8060127 - 4 Jun 2026
Viewed by 685
Abstract
Autonomous driving, as a core component of intelligent transportation systems, relies highly on precise environmental perception capabilities. Vehicle target detection is the fundamental task of environmental perception. However, complex factors in real scenarios (such as target occlusion, illumination changes, and dense traffic flow) [...] Read more.
Autonomous driving, as a core component of intelligent transportation systems, relies highly on precise environmental perception capabilities. Vehicle target detection is the fundamental task of environmental perception. However, complex factors in real scenarios (such as target occlusion, illumination changes, and dense traffic flow) often lead to feature misjudgments, missed detections, target positioning deviations, and category confusions in existing methods. To address these challenges, this paper proposes the MLRP-YOLOv8n model that integrates Mixed Local Channel Attention (MLCA) and large kernel separable attention (LSKA). Three complementary attention mechanisms as well as improved regression loss are integrated into the lightweight YOLOv8n architecture to improve the accuracy of vehicle detection while maintaining computational efficiency. Firstly, MLCA is embedded in the C2f feature extraction module to enhance local feature focus; the SPPF module integrates LSKA optimize multi-scale feature fusion; RFCBAMConv convolution is used to replace the original convolution in the neck to enhance cross-level feature correlation; the PIoUv2 loss function is introduced instead of Complete Intersection over Union (CIoU) to accelerate model convergence and reduce regression errors. Experiments on the KITTI Detection dataset subset and UA-DETRAC datasets show that MLRP-YOLOv8n improves the mean average precision (mAP) by 1.9% and 3.2% respectively on the KITTI Detection dataset subset and UA-DETRAC datasets. This model achieves a balance between detection accuracy, tracking robustness, and computational efficiency, providing a reliable solution for autonomous driving environment perception. Full article
(This article belongs to the Special Issue AI-Empowered Assisted and Autonomous Driving)
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26 pages, 4957 KB  
Article
Detection of Traffic Lights and Status (Red, Yellow and Green) in Images with Different Environmental Conditions Using Architectures from Yolov8 to Yolov12
by Julio Saucedo-Soto, Viridiana Hernández-Herrera, Moisés Márquez-Olivera, Octavio Sánchez-García and Antonio-Gustavo Juárez-Gracia
Vehicles 2026, 8(4), 90; https://doi.org/10.3390/vehicles8040090 - 10 Apr 2026
Viewed by 1726
Abstract
Given that approximately 70% of traffic accidents are attributable to driver-related factors, it is necessary for vehicles to incorporate technologies that reduce risk through preventive actions derived from traffic-scene analysis. Interpreting the driving environment is non-trivial and is commonly decomposed into sub-tasks; among [...] Read more.
Given that approximately 70% of traffic accidents are attributable to driver-related factors, it is necessary for vehicles to incorporate technologies that reduce risk through preventive actions derived from traffic-scene analysis. Interpreting the driving environment is non-trivial and is commonly decomposed into sub-tasks; among them, traffic light perception is critical due to its role in regulating vehicular flow. This paper evaluates five YOLO CNN families (YOLOv8–YOLOv12) on two tasks: (i) traffic light detection and (ii) traffic light state recognition (green, yellow, red). The evaluation uses a hybrid dataset comprising the public LISA traffic light dataset and a custom dataset with images from Mexico City captured under diverse lighting conditions—a relevant setting given the city’s high traffic intensity. The results show mAP@0.50 = 94.4–96.3% for traffic light detection and mAP@0.50 = 99.3–99.4% for traffic light state recognition, indicating that modern YOLO variants provide highly reliable performance for both tasks under natural illumination variability. Full article
(This article belongs to the Special Issue AI-Empowered Assisted and Autonomous Driving)
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25 pages, 4371 KB  
Article
GTS-SLAM: A Tightly-Coupled GICP and 3D Gaussian Splatting Framework for Robust Dense SLAM in Underground Mines
by Yi Liu, Changxin Li and Meng Jiang
Vehicles 2026, 8(4), 79; https://doi.org/10.3390/vehicles8040079 - 3 Apr 2026
Viewed by 1592
Abstract
To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for [...] Read more.
To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article
(This article belongs to the Special Issue AI-Empowered Assisted and Autonomous Driving)
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21 pages, 5844 KB  
Article
A Rule-Guided Distributional Soft Actor–Critic Algorithm for Safe Lane-Changing in Complex Driving Scenarios
by Shuwan Cui, Hao Li, Yanzhao Su, Jin Huang, Kun Cheng and Huiqian Li
Vehicles 2026, 8(3), 58; https://doi.org/10.3390/vehicles8030058 - 13 Mar 2026
Cited by 1 | Viewed by 1457
Abstract
Mandatory lane-changing in complex driving scenarios poses significant challenges for autonomous driving systems due to complex vehicle interactions and strict safety requirements. Existing methods often rely on handcrafted rules or extensive expert demonstrations, which increase data collection costs and provide limited safety guarantees [...] Read more.
Mandatory lane-changing in complex driving scenarios poses significant challenges for autonomous driving systems due to complex vehicle interactions and strict safety requirements. Existing methods often rely on handcrafted rules or extensive expert demonstrations, which increase data collection costs and provide limited safety guarantees during learning. To address these issues, this paper proposes a rule-guided reinforcement learning framework for lane-changing policy optimization. A lightweight rule-based controller is employed to generate initial experience, guiding the training of an improved Distributional Soft Actor–Critic with Three Refinements (DSAC-T), while a safety-aware constraint controller filters high-risk actions to ensure stable and safe learning. The proposed method is evaluated in Regular Lane Change and Lane Merging scenarios under mixed traffic composed of aggressive and conservative vehicles within a simulation environment. Simulation results show that although lane-changing success rates decrease as traffic aggressiveness increases, the proposed method consistently outperforms SAC and TD3. Notably, under highly aggressive traffic conditions with an aggressiveness ratio of 0.7, the proposed approach improves the success rate by 17.13% compared to SAC and by 10.49% compared to TD3, demonstrating superior robustness and safety in complex, high-conflict lane-changing scenarios. The present study is conducted solely in simulation and requires further validation before application to real-world traffic environments. Full article
(This article belongs to the Special Issue AI-Empowered Assisted and Autonomous Driving)
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19 pages, 1680 KB  
Article
A Hybrid Decision-Making Framework for Autonomous Vehicles in Urban Environments Based on Multi-Agent Reinforcement Learning with Explainable AI
by Ameni Ellouze, Mohamed Karray and Mohamed Ksantini
Vehicles 2026, 8(1), 8; https://doi.org/10.3390/vehicles8010008 - 2 Jan 2026
Cited by 5 | Viewed by 2832
Abstract
Autonomous vehicles (AVs) are expected to operate safely and efficiently in complex urban environments characterized by dynamic and uncertain elements such as pedestrians, cyclists and adverse weather. Although current neural network-based decision-making algorithms, fuzzy logic and reinforcement learning have shown promise, they often [...] Read more.
Autonomous vehicles (AVs) are expected to operate safely and efficiently in complex urban environments characterized by dynamic and uncertain elements such as pedestrians, cyclists and adverse weather. Although current neural network-based decision-making algorithms, fuzzy logic and reinforcement learning have shown promise, they often struggle to handle ambiguous situations, such as partially hidden road signs or unpredictable human behavior. This paper proposes a new hybrid decision-making framework combining multi-agent reinforcement learning (MARL) and explainable artificial intelligence (XAI) to improve robustness, adaptability and transparency. Each agent of the MARL architecture is specialized in a specific sub-task (e.g., obstacle avoidance, trajectory planning, intention prediction), enabling modular and cooperative learning. XAI techniques are integrated to provide interpretable rationales for decisions, facilitating human understanding and regulatory compliance. The proposed system will be validated using CARLA simulator, combined with reference data, to demonstrate improved performance in safety-critical and ambiguous driving scenarios. Full article
(This article belongs to the Special Issue AI-Empowered Assisted and Autonomous Driving)
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14 pages, 1881 KB  
Article
Optimization of Adaptive Cruise Control Strategies Based on the Responsibility-Sensitive Safety Model
by Tengwei Yu, Yubin Tang, Renxiang Chen and Shuen Zhao
Vehicles 2025, 7(2), 28; https://doi.org/10.3390/vehicles7020028 - 26 Mar 2025
Cited by 4 | Viewed by 4237
Abstract
The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA [...] Read more.
The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA environment, where the lead vehicle underwent various rapid deceleration scenarios to optimize the following vehicle’s braking strategy. By integrating the multi-step predictive optimization capabilities of MPC with the dynamic safety assessment mechanisms of RSS, the proposed strategy ensures safe following distances while achieving rapid and precise speed adjustments, thereby enhancing the system’s responsiveness and safety. The model also incorporates a secondary optimization to balance comfort and stability, thereby improving the overall performance of autonomous vehicles. The use of multi-dimensional assessment metrics, such as Time to Collision (TTC), Time Exposed TTC (TET), and Time Integrated TTC (TIT), addresses the limitations of using TTC alone, which only reflects instantaneous collision risk. The optimization of the model in this paper aims to improve the safety and comfort of the following vehicle in scenarios with various gap distances, and it has been validated through the SSM multi-indicator approach. Experimental results demonstrate that the improved ACC model significantly enhances vehicle safety and comfort in scenarios involving large gaps and short-distance emergency braking by the lead vehicle, validating the method’s effectiveness in various extreme traffic scenarios. Full article
(This article belongs to the Special Issue AI-Empowered Assisted and Autonomous Driving)
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