Instruments For Road Network Navigation TRIZ Cases

Statistical trip and energy data assign vehicles to specific replenishing stations in advance, cutting wait times and improving station use.

Balances travel time, manual driving time, and transition count to choose safer autonomous routes under changing road clearance conditions.

A path overlay interface maps vehicle and obstacle motion so operators can update safe routes through complex zones in real time.

A single planar beam and detector array replace many fixed LiDAR beams, preserving detection quality while cutting cost, power use, and complexity.

Sensor, map, and metadata are matched across similar road segments to assign risk profiles before navigation and improve hazard awareness.

Area-based map updates use road-shape change and vehicle activity to target sensing requests, improving freshness while reducing communication load.

When wind, obstacles, or battery limits block a route, the UAV switches to a lower-priority delivery region to keep delivery reliable.

Bias transformation corrects user-labeled object corners across image sensors, improving detection accuracy and map updates for autonomous driving.

Overlapping camera or LiDAR views use relative landmark constraints to correct GPS error and improve global landmark map accuracy.

A model library matched to vehicle sensor configurations speeds ML model updates and distribution while preserving detection accuracy.

Selective trip data storage lets autonomous vehicles keep needed records while deleting sensitive audio, video, and location data after the ride.

Clusters unknown object events by spatial and temporal conditions to identify autonomous driving scenarios where vision detection fails.

Before-and-after cabin images are compared to detect left-behind items or soiling and trigger wireless alerts or vehicle handling.

Preintegrated IMU data is corrected with external pose updates to limit drift and avoid repeated coordinate transformations in navigation.

Yaw range detection converts six-axis inertial sensor outputs into a virtual sensor frame, preventing Kalman filter divergence.

Virtual lines and ordinals compress intersection traversal data, cutting telematics storage and processing while preserving travel-direction analysis.

Ordinal pairs encode vehicle intersection crossings from spatial-temporal traces, cutting telematics storage and processing load.

Detected sensor yaw is remapped to a virtual ±90° frame so attachment-angle estimation converges quickly and avoids Kalman divergence.

Adjacent route sections with the same ranked gradient are merged to cut map data size while preserving power consumption calculation accuracy.

Multiple sound sources generate clearer distance and orientation cues from a user's location, improving audio AR spatial guidance.

Separating command speech from cabin noise prevents sudden volume jumps, keeping in-vehicle voice guidance audible without discomfort.

When radio reception weakens across regions or poor coverage areas, a server recommends stronger alternative signals for seamless in-vehicle listening.

When reception weakens across regions or weather conditions, the vehicle uses server-generated radio alternatives to keep listening continuous.

Virtual line ordinals compress repeated intersection traversals in telematics data, cutting storage needs and city-scale processing time.

Server-guided radio recommendations help vehicles switch to stronger regional frequencies and avoid interrupted listening without manual resets.

Navigation guidance shifts between speech, screen text, ducking, and pause control to avoid conflicts with calls, assistants, and audiobooks.

DSRC time-of-flight from roadside transmitters improves vehicle localization at urban intersections where weak GPS signals cause position errors.

Sensor fusion and statistical activity detection let mobile maps switch between walking and driving modes for more relevant navigation.