The reachability-based optimization approach, while effective in providing insights into the impact of attacks on cooperative driving systems, has certain limitations that need to be addressed. One potential limitation is the assumption of bounded disturbances, which may not always hold in real-world scenarios where attacks can be more sophisticated and unpredictable. To improve the approach and provide tighter bounds on the attack impact, more advanced modeling techniques could be employed to capture a wider range of attack scenarios and their effects on the system.
Furthermore, incorporating uncertainty quantification methods into the optimization framework can help account for the variability and unpredictability of attacks. By considering probabilistic models of attacks and their consequences, the optimization process can provide more robust and reliable results that account for a broader range of potential attack scenarios.
Additionally, integrating machine learning algorithms for anomaly detection and attack prediction could enhance the reachability-based optimization approach. By leveraging historical data and patterns of attacks, the system can proactively identify and mitigate potential threats, leading to more effective security measures and tighter bounds on the attack impact.