Vehicle Control System Risk-Based Target Area Adjustment

The patent applies local quality by differentiating the target area size based on the specific object being monitored. High-risk objects (pedestrians, cyclists) receive enlarged target areas for enhanced monitoring, while low-risk objects (stationary vehicles, animals) receive standard target areas. This selective approach ensures that driving support is intensified only where necessary, maintaining safety for vulnerable road users while avoiding unnecessary intervention for low-risk objects, thereby resolving the contradiction between collision avoidance reliability and driver comfort

The patent implements dynamics by making the target area size adjustable and object-specific rather than fixed for all objects. The system dynamically enlarges the target area for high-risk objects and maintains standard size for low-risk objects. This dynamic adaptation allows the system to concentrate monitoring resources on critical threats while reducing false alarms for benign objects, thus improving both safety and driver comfort simultaneously

The patent applies local quality by implementing object-specific target area enlargement rather than uniform expansion. Only high-risk objects such as pedestrians and cyclists receive enlarged target areas, while low-risk objects maintain standard target areas. This selective approach ensures that detection sensitivity is enhanced only for vulnerable road users where false detections are less critical, while avoiding false alarms for stationary objects and low-risk animals, thus resolving the contradiction between detection coverage and false detection rate

The patent implements dynamics by making target area size adaptive and object-dependent. The system dynamically adjusts the target area based on object classification, enlarging it for high-risk moving objects while maintaining standard size for low-risk objects. This dynamic adjustment allows the system to optimize detection coverage for critical threats without proportionally increasing false detections across all object types, thereby improving reliability while controlling false alarm rates

The patent applies local quality by implementing risk-level-based intervention timing. High-risk objects receive early driving support intervention with enlarged target areas, while low-risk objects receive standard timing. This differentiated approach ensures that premature intervention is applied only where it is critical for safety (pedestrians, cyclists), while avoiding unnecessary early alerts for low-risk objects, thus resolving the contradiction between collision avoidance reliability and intervention timing

The patent implements dynamics by making intervention timing adaptive based on object risk classification. The system dynamically adjusts when driving support is activated, providing early intervention for high-risk objects and standard timing for low-risk objects. This dynamic timing adjustment ensures that safety-critical early warnings are delivered for vulnerable road users while avoiding premature alerts that would cause driver discomfort for benign situations, thereby optimizing both safety and driver experience