# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 5 Conclusions  
In this paper, we have presented an innovative approach for Autonomous Intersection Management (AIM) utilizing adaptive control barrier functions (aCBFs). The proposed method effectively addresses the critical challenges of safety and efficiency in managing intersections with both static obstacles and autonomous vehicles. By incorporating adaptive fault compensation, the aCBF framework ensures robust performance even in the presence of power transmission loss, making it a reliable and scalable solution for autonomous intersection management.  
Through extensive simulations, our results demonstrate that the aCBF significantly outperforms existing multi-agent collision avoidance algorithms in ensuring safe vehicle navigation. In comparative studies, the aCBF achieves a 95% collision avoidance rate, whereas the TriPIield algorithm only maintains 65%. Furthermore, when power transmission loss occurs, the aCBF still ensures 100% collision avoidance, while the traditional CBF only achieves 45%. This highlights the superiority of the aCBF in ensuring intersection safety under real-world uncertainties. This work contributes to advancing robust and adaptive safety mechanisms in autonomous intersection management, offering a promising solution for large-scale deployment in intelligent traffic management.  
Despite these advantages, the current approach assumes that all vehicles entering the intersection are autonomous and controlled by the aCBF. However, in the foreseeable future, mixed traffic scenarios will emerge, where autonomous vehicles coexist with human-driven vehicles. Human drivers exhibit unpredictable behaviors, introducing additional challenges for intersection management. To further enhance the safety and adaptability of our algorithm, future work will explore the integration of machine learning techniques, such as human behavior prediction models and reinforcement learning, to anticipate human driving behaviors and proactively adjust aCBF-based control strategies.  
Figure 11: Top-view snapshots of AIM’s autonomous vehicles during experiments at different time points: (**a**) \(t=0\) s, (**b**) \(t=5\) s, (**c**) \(t=15\) s, and (**d**) \(t=20\) s. (**e**) The minimum distance between any two-pair of all autonomous vehicles’ centers during the experiments and (**f**) corresponding paths in experiments.  
**Author Contributions:** Conceptualization, M.S. and N.W.; Data Curation, J.S.; Formal Analysis, J.S.; Software, J.S.; Writing--Original Draft Preparation, J.S., M.S. and N.W.; Visualization, J.S.; Resources, M.S.; Writing--Review and Editing, M.S. and N.W.; Supervision, M.S.; Project Administration, M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
**Conflicts of Interest:** The authors declare no conflicts of interest.