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Control and Path Planning for Autonomous Vehicles

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A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 9198

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Special Issue Editor

Dr. Chuan Sun

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Guest Editor

Suzhou Automotive Research Institute, Tsinghua University, Suzhou 215000, China
Interests: intelligent decision-making architectures for autonomous systems; safety-certified motion planning and control; virtual–real fusion testing methodologies; end-to-end system integration and standardization

Special Issue Information

Dear Colleagues,

The convergence of autonomous driving and smart mobility requires groundbreaking innovations in decision-making, control, path planning, and validation frameworks, all while ensuring seamless integration with intelligent transportation ecosystems. As key components of Industry 4.0, autonomous vehicles require embodied intelligence that bridges perception, action, and environmental interaction—a vision aligned with this journal’s focus on intelligent mechanical systems and cyber–physical advancements. This Special Issue seeks to advance the development of certifiable autonomous driving technologies through cutting-edge research in AI-enhanced decision-control architectures, adaptive navigation, and rigorous testing paradigms.

We invite authors to submit original research, reviews, and case studies that synergize theoretical depth with industrial applicability. We are particularly keen to publish dubmissions emphasizing embodied intelligence (e.g., sensorimotor integration for contextual decision-making) or human-centric smart mobility solutions. Topics include, but are not limited to, the following areas and themes:

Test and evaluation:

Virtual-real fusion validation platforms (digital twin, scenario mining);
SOTIF/ISO 34502-compliant safety certification;
Edge case generation for embodied interaction testing.

Decision and control:

Hierarchical decision-making with multi-modal perception fusion;
Reinforcement learning-based control under traffic uncertainties;
Game-theoretic interaction modeling for mixed traffic flows.

Path planning and mobility:

Risk-bounded trajectory optimization in dynamic environments;
V2X-enhanced collaborative navigation for smart cities;
Energy-efficient routing with road-vehicle co-simulation.

Embodied AI integration:

Cognitive architectures for vehicle-environment symbiosis;
Physical-world priors in decision-planning pipelines;
Adversarial robustness testing for sensor-actuator loops.

Contributions should demonstrate reproducible methodologies validated through simulations, hardware-in-the-loop (HIL) platforms, or real-world deployments. Interdisciplinary studies bridging robotics, transportation engineering, and intelligent mechatronic systems are welcome, reflecting our commitment to advancing industrial-ready intelligent machinery.

Join us in shaping the future of autonomous driving as a cornerstone of smart, safe, and sustainable mobility.

Dr. Chuan Sun
Guest Editor

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines 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 2400 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

path planning
mobility
autonomous driving

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Published Papers (8 papers)

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Research

25 pages, 3334 KB

Open AccessArticle

A Reproducible Evaluation Method for Intelligent-Driving Longitudinal Control Under Complex Weather Through Operational Design Domain Parameter Perturbation

by Yang Xu, Zhixiong Li, Chuan Sun, Shucai Xu, Haiming Sun, Yicheng Cao and Junru Yang

Machines 2026, 14(4), 454; https://doi.org/10.3390/machines14040454 - 20 Apr 2026

Viewed by 229

Abstract

Complex weather degrades both perception reliability and tire–road adhesion, thereby reducing the safety margin and responsiveness of intelligent driving longitudinal control. This study proposes a reproducible evaluation method for adverse weather operational design domains based on parameter perturbation testing and comprehensive assessment. Snow, fog, and rain are graded using standard quantitative thresholds and are coupled with road slipperiness to construct a weather–road state set. A mechanism-oriented indicator system, a combined subjective–objective weighting strategy, and a multi-level fuzzy comprehensive evaluation model are then used to generate quantitative capability scores. The method is validated on a co-simulation framework integrating vehicle–sensor simulation, a driving simulator, and a digital-twin testing environment using representative autonomous emergency braking scenarios. Results show that increasing weather severity, decreasing road adhesion, and higher initial speed reduce the post-braking safety margin and prolong collision-response time. The proposed method differentiates performance across weather–road states and provides quantitative support for test-coverage planning and capability boundary calibration in adverse weather operational design domains. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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31 pages, 4366 KB

Open AccessArticle

Distributed Multi-Vehicle Cooperative Trajectory Planning and Control for Ramp Merging and Diverging Based on Deep Neural Networks and MPC

by Linhua Nie, Tingyang Zhang, Yunqing Zhao, Yaqiu Li, Haoran Li and Junru Yang

Machines 2026, 14(3), 262; https://doi.org/10.3390/machines14030262 - 25 Feb 2026

Viewed by 545

Abstract

With the deep integration of the modern automotive industry and artificial intelligence technologies, connected and automated vehicles (CAVs) have emerged as a key breakthrough for improving traffic safety and operational efficiency. This study proposes a distributed multi-vehicle cooperative trajectory planning and control framework for ramp merging and diverging scenarios, integrating Deep Neural Networks (DNNs) with Model Predictive Control (MPC). The methodology consists of three key components: First, a distributed cooperative architecture based on dynamic topology is constructed to effectively reduce communication loads; second, a feature point-based Cubic Bézier Curve trajectory generation method is proposed, enabling flexible path planning with reduced reliance on high-precision maps; finally, a DNN-accelerated MPC solving strategy (NN-MPC) is designed. This strategy employs an offline-trained deep neural network to approximate the online optimization process, supplemented by a terminal Safety Check mechanism and a dynamic surrounding vehicle selection algorithm. Experimental results demonstrate that the proposed method successfully reproduces the planning capability of offline high-precision MPC in ramp merging and diverging scenarios while reducing computation time to the millisecond level. It effectively overcomes the myopic decision-making problem of traditional real-time algorithms, achieving smoother conflict resolution and higher traffic efficiency. Notably, quantitative validation confirms that this cooperative framework achieves an approximate 30% reduction in average travel delay compared to the non-cooperative baseline. This study confirms the engineering advantages of the hybrid architecture under dynamic high-density traffic flows, significantly enhancing the system’s real-time response capability while balancing the safety and riding comfort of cooperative driving. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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20 pages, 2671 KB

Open AccessArticle

Semantic-Aligned Multimodal Vision–Language Framework for Autonomous Driving Decision-Making

by Feng Peng, Shangju She and Zejian Deng

Machines 2026, 14(1), 125; https://doi.org/10.3390/machines14010125 - 21 Jan 2026

Cited by 1 | Viewed by 890

Abstract

Recent advances in Large Vision–Language Models (LVLMs) have demonstrated strong cross-modal reasoning capabilities, offering new opportunities for decision-making in autonomous driving. However, existing end-to-end approaches still suffer from limited semantic consistency, weak task controllability, and insufficient interpretability. To address these challenges, we propose SemAlign-E2E (Semantic-Aligned End-to-End), a semantic-aligned multimodal LVLM framework that unifies visual, LiDAR, and task-oriented textual inputs through cross-modal attention. This design enables end-to-end reasoning from scene understanding to high-level driving command generation. Beyond producing structured control instructions, the framework also provides natural-language explanations to enhance interpretability. We conduct extensive evaluations on the nuScenes dataset and CARLA simulation platform. Experimental results show that SemAlign-E2E achieves substantial improvements in driving stability, safety, multi-task generalization, and semantic comprehension, consistently outperforming state-of-the-art baselines. Notably, the framework exhibits superior behavioral consistency and risk-aware decision-making in complex traffic scenarios. These findings highlight the potential of LVLM-driven semantic reasoning for autonomous driving and provide a scalable pathway toward future semantic-enhanced end-to-end driving systems. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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24 pages, 4196 KB

Open AccessArticle

Real-Time Cooperative Path Planning and Collision Avoidance for Autonomous Logistics Vehicles Using Reinforcement Learning and Distributed Model Predictive Control

by Mingxin Li, Hui Li, Yunan Yao, Yulei Zhu, Hailong Weng, Huabiao Jin and Taiwei Yang

Machines 2026, 14(1), 27; https://doi.org/10.3390/machines14010027 - 24 Dec 2025

Viewed by 826

Abstract

In industrial environments such as ports and warehouses, autonomous logistics vehicles face significant challenges in coordinating multiple vehicles while ensuring safe and efficient path planning. This study proposes a novel real-time cooperative control framework for autonomous vehicles, combining reinforcement learning (RL) and distributed model predictive control (DMPC). The RL agent dynamically adjusts the optimization weights of the DMPC to adapt to the vehicle’s real-time environment, while the DMPC enables decentralized path planning and collision avoidance. The system leverages multi-source sensor fusion, including GNSS, UWB, IMU, LiDAR, and stereo cameras, to provide accurate state estimations of vehicles. Simulation results demonstrate that the proposed RL-DMPC approach outperforms traditional centralized control strategies in terms of tracking accuracy, collision avoidance, and safety margins. Furthermore, the proposed method significantly improves control smoothness compared to rule-based strategies. This framework is particularly effective in dynamic and constrained industrial settings, offering a robust solution for multi-vehicle coordination with minimal communication delays. The study highlights the potential of combining RL with DMPC to achieve real-time, scalable, and adaptive solutions for autonomous logistics. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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21 pages, 4628 KB

Open AccessArticle

High-Definition Map Change Regions Detection Considering the Uncertainty of Single-Source Perception Data

by Zhihua Zhang, Qingjian Li, Xiangfei Qiao, Jun Zhao, Peng Yin, Jian Zhou and Bijun Li

Machines 2025, 13(12), 1080; https://doi.org/10.3390/machines13121080 - 24 Nov 2025

Cited by 1 | Viewed by 1009

Abstract

High-definition (HD) maps, with their accurate and detailed road information, have become a core component of autonomous vehicles. These maps help vehicles with environment perception, precise localization, and path planning. However, outdated maps can compromise vehicle safety, making map updates a key research area in intelligent driving technology. Traditional surveying methods are accurate but expensive, making them unsuitable for large-scale and frequent updates. Most existing crowdsourced map update methods focus on matching perception data with map features. However, they lack sufficient analysis of the reliability and uncertainty of perception results, making it difficult to ensure the accuracy of map updates. To address this, this paper proposes an HD map change detection method that considers the uncertainty of single-source perception results. This method extracts road feature information using onboard camera and Global Navigation Satellite System (GNSS) data and improves matching accuracy by combining geometric proximity and consistency. Additionally, a probability-based change detection method is introduced, which evaluates the reliability of map changes by integrating observations from multi-source vehicles. To validate the effectiveness of the proposed method, experiments were conducted on both simulation data and real-world road data, and the detection results of single-source data were compared with those of multi-source fused data. The experimental results indicate that the probabilistic estimation method proposed in this study effectively identifies the three typical scenarios of addition, deletion, and modification in HD map change detection. Additionally, the method achieves more than a 10% improvement in both precision and recall compared to single-source data. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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15 pages, 7933 KB

Open AccessArticle

A Framework for Testing and Evaluation of Automated Valet Parking Using OnSite and Unity3D Platforms

by Ouchan Chen, Lei Chen, Junru Yang, Hao Shi, Lin Xu, Haoran Li, Weike Lu and Guojing Hu

Machines 2025, 13(11), 1033; https://doi.org/10.3390/machines13111033 - 7 Nov 2025

Viewed by 1077

Abstract

Automated valet parking (AVP) is a key component of autonomous driving systems. Its functionality and reliability need to be thoroughly tested before road application. Current testing technologies are limited by insufficient scenario coverage and lack of comprehensive evaluation indices. This study proposes an AVP testing and evaluation framework using OnSite (Open Naturalistic Simulation and Testing Environment) and Unity3D platforms. Through scenario construction based on field-collected data and model reconstruction, a testing scenario library is established, complying with industry standards. A simplified kinematic model, balancing simulation accuracy and operational efficiency, is applied to describe vehicle motion. A multidimensional evaluation system is developed with completion rate as a primary index and operation performance as a secondary index, which considers both parking efficiency and accuracy. Over 500 AVP algorithms are tested on the OnSite platform, and the testing results are evaluated through the Unity3D platform. The performance of the top 10 algorithms is analyzed. The evaluation platform is compared with CARLA simulation platform and field vehicle testing. This study finds that the framework provides an effective tool for AVP testing and evaluation; a variety of high-level AVP algorithms are developed, but their flexibility in complex dynamic scenarios has limitations. Future research should focus on exploring more sophisticated learning-based algorithms to enhance AVP adaptability and performance in complex dynamic environment. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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21 pages, 7263 KB

Open AccessArticle

Analysis of Driver Takeover Performance in Autonomous Vehicles Based on Generalized Estimating Equations

by Min Duan, Lian Xie, Jianrong Cai, Junru Yang and Haoran Li

Machines 2025, 13(11), 1032; https://doi.org/10.3390/machines13111032 - 7 Nov 2025

Viewed by 1241

Abstract

Current autonomous vehicles require human drivers to take over control during emergencies or in environments the system cannot handle. During other periods, drivers are permitted to engage in non-driving-related tasks. It is essential to investigate how the immersion in non-driving-related tasks affects drivers’ takeover performance under different scenarios. To address this, a mixed-design simulated driving experiment was conducted with 40 participants, incorporating three non-driving-related tasks (no task, watch video, play game), three takeover request lead times (3 s, 5 s, 7 s), and two obstacle types (dynamic, static). The takeover process was divided into three phases: preparation, obstacle avoidance, and recovery. Analysis of the areas of interest showed that engaging in non-driving-related tasks substantially reduced drivers’ visual attention tothe road ahead during the preparation phase. The Generalized Estimating Equations method was employed to investigate the effects of various factors on takeover performance. Model results showed that scenarios with static obstacles and longer takeover request times led to a significant reduction in mean lane deviation but a significant increase in the standard deviation of lane deviation, suggesting improved lateral control performance. A significant interaction was observed between the watch video task and static obstacles, which corresponded to a notable decrease in the mean vehicle speed during obstacle avoidance. Performance in the recovery phase was strongly predicted by that in the obstacle avoidance phase, indicating that the stability of the avoidance maneuver is a critical determinant of the subsequent recovery. These findings offer valuable insights for managing non-driving-related tasks and setting appropriate takeover request timings in automated driving systems. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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17 pages, 17446 KB

Open AccessArticle

Occlusion-Aware Interactive End-to-End Autonomous Driving for Right-of-Way Conflicts

by Jialun Yin, Kun Zhao, Xiaohan Ma, Siping Yan, Haoran Li, Junru Yang and Yin Chen

Machines 2025, 13(10), 965; https://doi.org/10.3390/machines13100965 - 20 Oct 2025

Cited by 2 | Viewed by 2682

Abstract

End-to-end autonomous driving has demonstrated remarkable potential due to its strong scene-understanding capabilities. However, its performance degrades significantly in the presence of occlusions and complex multi-agent interactions, posing serious safety risks. Existing methods struggle to understand partially observed environments and accurately predict the dynamic behaviors of surrounding agents. To address these limitations, we propose OAIAD (Occlusion-Aware Interactive End-to-End Autonomous Driving), a novel end-to-end framework designed to enhance occlusion reasoning and interaction awareness. This framework specifically addresses the critical challenge of right-of-way conflicts in complex multi-agent scenarios. OAIAD employs a stereoscopic vectorized representation to explicitly model occluded areas and incorporates a module for joint optimization of trajectory prediction and planning to better capture future agent dynamics. By explicitly modeling interactive behaviors and leveraging joint trajectory optimization, OAIAD enhances the ego vehicle’s ability to negotiate the right-of-way interactions in a safe and socially compliant manner, significantly reducing conflict-induced collisions. Extensive evaluations on both open- and closed-loop datasets demonstrate that OAIAD significantly improves performance in occlusion-heavy and interaction-intensive scenarios. Real-world experiments further validate the practicality and robustness of our approach, highlighting its potential for deployment in complex urban environments. Full article

(This article belongs to the Special Issue Control and Path Planning for Autonomous Vehicles)

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