Vehicle Data Traffic Control via Predictive Capacity Allocation

The patent dynamically changes the capacity allocation parameters for different functional units based on predicted future communication requirements. Instead of allocating fixed maximum capacity to all units, the system adjusts capacity parameters (data rate, bandwidth) according to the specific needs of each unit at different time points, thereby improving communication reliability when needed while avoiding resource waste when full capacity is not required.

The system implements dynamic capacity allocation where the available capacity for each functional unit changes over time based on predictions of future communication requirements. The determination device continuously updates capacity assignments according to predicted traffic patterns, ensuring that capacity is available when needed while being released when not required, thus balancing reliability and resource efficiency.

The patent applies preliminary action by predicting future communication requirements of functional units before they actually occur. The determination device uses prediction algorithms to anticipate future traffic demands and pre-allocates capacity accordingly, preventing future overload situations. This allows the system to maintain simple current allocation while ensuring future stability through advance planning.

The system implements feedback mechanisms where actual communication requirements are continuously monitored and compared with predicted requirements. This feedback loop allows the determination device to adjust capacity allocations dynamically, ensuring that future overload situations are prevented while maintaining allocation simplicity through automated adjustment based on real-world performance data.

The patent applies local quality by allocating different capacity levels to different functional units based on their specific priority and requirements. High-priority functions receive sufficient capacity to maintain quality of service, while low-priority functions receive adequate residual capacity to prevent complete starvation. This localized quality differentiation ensures that each function receives appropriate resources according to its specific needs rather than applying a uniform allocation strategy.

The system uses partial action by allocating capacity in proportions that satisfy minimum requirements for all functional units while providing excess capacity to high-priority functions when available. This ensures that low-priority applications receive partial service (preventing starvation) while high-priority functions can utilize excess capacity when needed, balancing quality of service with overall application utilization.