The proposed safety-critical Connected Cruise Control (CCC) can be extended to handle input constraints and additional safety criteria by integrating control barrier functions (CBFs) that account for these factors. Input constraints, such as maximum acceleration and deceleration limits, can be incorporated into the safety-critical CCC framework by modifying the quadratic programming problem used to derive the safety filter. This involves adding constraints that ensure the control input remains within specified bounds, thereby preventing unsafe maneuvers that could lead to collisions or loss of control.
To address other safety criteria beyond time headway, such as maintaining a safe distance from obstacles or ensuring safe lateral movements during lane changes, the CBFs can be designed to encapsulate these criteria. For instance, a CBF could be defined to ensure that the distance to the nearest obstacle remains above a certain threshold, or that the vehicle's lateral position remains within safe limits during maneuvers. By formulating these criteria as additional constraints in the safety filter, the CCC can dynamically adjust its control inputs to maintain safety while still optimizing for performance.
Moreover, the integration of multi-objective optimization techniques can be employed to balance various safety criteria, allowing the CCC to prioritize different safety aspects based on the driving context. This holistic approach ensures that the safety-critical CCC remains robust and adaptable to a wide range of driving scenarios, enhancing the overall safety of connected automated vehicles (CAVs).