Sensor-Based Vehicle Maneuver Guidance Without HD Maps

The patent extracts and removes the dependency on high-definition maps from the autonomous vehicle navigation system. Instead of relying on HD maps for navigation, the system uses sensor data from cameras, RADAR, and LIDAR combined with machine learning models to perform vehicle maneuvers. This extraction eliminates the computational burden of processing and matching HD map data while maintaining navigation capability through direct sensor-based perception and control.

The patent replaces expensive, computationally intensive HD map processing with cheaper, more efficient sensor data processing. The system uses low-resolution map data or no map data at all, relying instead on real-time sensor inputs from cameras, RADAR, and LIDAR. This substitution dramatically reduces computational requirements while enabling universal operation in any location without needing pre-recorded HD maps.

The patent makes the autonomous vehicle navigation system universal by removing its dependency on HD maps. The system using sensor data from cameras, RADAR, and LIDAR combined with machine learning models can operate in any location regardless of whether HD maps are available. This multi-functional approach allows the vehicle to perform navigation and maneuvering tasks using only sensor inputs and learned models, enabling operation in diverse environments including unmapped areas, construction zones, and remote locations.

The patent enables the autonomous vehicle to navigate and perform maneuvers using its own sensor data and machine learning models without external HD map support. The system localizes itself and determines vehicle maneuvers based on its sensor inputs from cameras, RADAR, and LIDAR, combined with control inputs and vehicle status data. This self-service capability allows the vehicle to operate independently in any location without relying on pre-recorded map data.