**Leveraging Multimodal and Hierarchical Control Strategies**

Future developments in autonomous driving are expected to increasingly focus on complex multimodal and hierarchical control strategies that can handle the dynamic and intricate nature of driving environments. The integration of deep learning's adaptability and advanced processing with the consistent reliability of rule-based methods is a promising direction. This integration can boost the adaptability and efficiency of autonomous driving systems, enabling them to effectively manage and adapt to changing conditions and complex tasks.

#### **Enhancing Trajectory Planning Intelligence via Human–Machine Interaction**

By integrating brain–computer interfaces and eye-tracking technologies, autonomous systems can now incorporate human input in various forms, such as human languages. This integration taps into the potential of cutting-edge fields, such as large language models, allowing for a dynamic and interactive driving experience. Such advancements mean that human drivers can assist autonomous systems through verbal commands or receive guidance through machine-interpreted human language, thus creating a collaborative and intuitive driving environment. This approach not only adds an extra layer of information to the decision-making process but also provides an additional safety net, making autonomous driving safe and aligned with human instincts and responses.

#### **Re-evaluating the Role of Machine Learning in Autonomous Driving Planning**

The integration of machine learning, particularly deep and reinforcement learning, is a growing trend in the field of autonomous driving motion planning. These methods, known for their ability to process and learn from large datasets, promise advancements in understanding and navigating complex and interactive driving environments. However, their current applications remain confined to simulations, with limited real-world deployment on real vehicles. This situation is partly due to the methods' generalization challenges; they sometimes act as supplementary tools relying on rule-based systems for core decisionmaking [7]. The true suitability of machine learning approaches for autonomous driving planning is a subject of ongoing research. A balance must exist between the innovative potential of these learning-based methods and the reliability of traditional rule-based approaches. This balance is crucial to developing practical, safe, efficient autonomous driving systems that can operate effectively in diverse and unpredictable real-world scenarios.

#### **4. Conclusions**

This Special Issue has successfully showcased a range of innovative approaches in the planning and control of autonomous vehicles by drawing insights from 11 groundbreaking papers. Our editorial has revisited these contributions, emphasizing their role in tackling real-world challenges and setting the stage for future advancements in autonomous driving. The collected papers highlight the shift toward highly specialized planning and control

methods, the integration of complex control strategies, the enhancement of human–machine interaction, and the evolving application of machine learning in this dynamic field.

The field of autonomous driving is poised for considerable advancements. The focus on the development of planners and controllers that are effective in real-world scenarios marks a major progression. The incorporation of human input through advanced interfaces and the strategic use of machine learning techniques are redefining the capabilities of autonomous vehicles. These developments are not just technological achievements; they represent a shift toward highly intuitive, safe, and efficient transportation solutions.

Autonomous driving technology is filled with promise and potential. As we continue to innovate, the vision of safe, efficient, accessible transportation becomes increasingly realistic. This Special Issue not only captures the current state of the art but also serves as a guidepost for the future, where autonomous vehicles are expected to become an integral part of our daily lives by reshaping our approach to mobility and connectivity. The path forward is filled with excitement and endless possibilities. We are at the threshold of a new era in transportation driven by intelligence, adaptability, and a commitment to enhancing the human experience.

**Author Contributions:** Conceptualization, B.L.; investigation, X.C.; writing—original draft preparation, B.L., X.L. and X.C.; writing—review and editing, T.A. and Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Lotus Youth Talent Program of Hunan Province, China under grant number 2023RC3115.

**Conflicts of Interest:** The authors declare no conflict of interest.
