Autonomous Vehicle Control Using WRIM and Dynamic Envelopes

The patent introduces an intermediary processing layer between sensor measurements and control signals that derives contextual information (such as relative motion states of surrounding vehicles) from raw sensor data. This intermediary layer enables more accurate navigation decisions without directly increasing resource consumption, as it processes information computationally rather than through additional physical sensors or actuators.

The patent replaces direct mechanical response (immediate braking based on proximity) with a computational analysis system that substitutes mechanical action with information processing. By using processors to analyze sensor data and derive contextual understanding (e.g., determining if other vehicles are moving relative to the host vehicle), the system achieves better navigation accuracy without proportionally increasing energy consumption from mechanical systems.

The patent makes the existing control system multi-functional by enabling it to perform both basic proximity-based navigation and contextual analysis-based navigation using the same hardware components. The processors that already exist for basic control are utilized to additionally derive contextual information from sensor data, avoiding the need for separate dedicated hardware for each function and thus limiting the increase in device complexity.

The patent introduces dynamic adaptability to the control system by enabling it to switch between different navigation strategies (basic proximity response vs. contextual-aware response) based on the situation. This dynamic capability allows the system to maintain simplicity in routine situations while achieving higher accuracy when contextual analysis is beneficial, without requiring a permanently complex control architecture.

The patent applies preliminary action by pre-processing sensor data to derive contextual information (such as relative motion states) before generating control signals. This preliminary analysis is performed using existing processors and allows the system to make more energy-efficient navigation decisions (e.g., slowing down instead of stopping) without requiring additional energy-consuming hardware. The contextual understanding is prepared in advance to guide subsequent control actions.

The patent implements feedback by continuously monitoring sensor data and using the derived contextual information to adjust control signals dynamically. The system feeds back the contextual understanding (e.g., other vehicles are moving) to the control decision process, enabling energy-optimized navigation (such as maintaining speed or gentle deceleration) rather than wasteful aggressive braking, while using the same processing infrastructure.