Vehicle Trajectory Cross-Checking for Collision-Free Autonomous Driving

The patent applies parameter changes by dynamically adjusting the verification threshold (uncertainty or confidence level) based on traffic density and vehicle proximity. When traffic is dense or vehicles are close, verification is triggered more frequently; when traffic is sparse, verification is reduced. This resolves the contradiction by making the verification intensity adaptive rather than static, ensuring collision safety while avoiding unnecessary computational overhead in low-risk scenarios.

The patent implements partial verification by selectively checking trajectories only when uncertainty thresholds are exceeded or confidence levels fall below thresholds, rather than continuously verifying all trajectories. This partial action approach maintains collision safety by verifying only when necessary, thereby reducing overall computational effort and data traffic while preserving essential reliability.

The patent reduces data traffic by dynamically changing the verification parameter thresholds based on traffic conditions. By adjusting uncertainty and confidence thresholds according to vehicle density and proximity, the system triggers verification messages only when necessary, thereby maintaining collision safety while significantly reducing the quantity of verification data transmitted across the network.

The system performs partial verification by sending trajectory verification requests only when specific conditions are met (uncertainty threshold exceeded or confidence threshold not met), rather than continuously broadcasting verification messages. This partial action reduces data traffic while maintaining sufficient verification coverage to ensure collision-free operation.

The patent implements periodic verification triggered by specific events or conditions rather than continuous verification. Verification is performed periodically when uncertainty thresholds are exceeded, confidence levels drop below thresholds, or at scheduled intervals. This periodic approach maintains vehicle control reliability by verifying trajectories at critical moments while conserving computational resources by avoiding unnecessary continuous verification.

The system changes verification frequency based on dynamic parameters such as vehicle proximity, relative speed, and traffic density. When parameters indicate high risk, verification frequency increases; when parameters indicate low risk, verification frequency decreases. This adaptive parameter-based approach ensures reliability when needed while reducing computational resource consumption during normal operation.