Navigation Instruments TRIZ Cases
- ADAS Routing Using V2X Collaboration and ODD Constraints
Route planning uses V2X collaboration data and ODD constraints to increase ADAS activation opportunities while balancing travel time and reliability.
- Incremental Vehicle Map Generation for Occluded Multi-Lane Roads
Stores incomplete road sections and revisits hidden or multi-lane areas to build more precise vehicle positioning maps.
- Lane Network Generation Using Trajectory-Based Bézier Curves
Vehicle trajectory data is converted into probability-based control points and Bézier lane links to create more natural intersection paths.
- Vehicle Orientation Correction Using LiDAR Planar Surface Feedback
Relative distance data from LiDAR-detected planar surfaces corrects IMU orientation drift without adding sensors or reducing reliability.
- Vehicle Coordinate Conversion for Low-Latency Local Map Control
Fresh ego-vehicle position updates and slower local map transmission are combined to cut load while improving control accuracy.
- Speed Limit Data Matching for Multi-Mode Traffic Signs
Matches speed values with transportation modes and predefined conditions to reduce sign-reading errors and improve navigation and autonomous control.
- Object Maneuver Prediction Using Road-Mapped Trajectory Scoring
Predicted object trajectories are mapped onto road lanes and scored to improve self-driving car maneuver planning and collision avoidance.
- Vehicle AR Display Control for Building Overlays on Tilted Roads
AR destination graphics are aligned to building surfaces using map coordinates and road-slope adjustment to keep overlays visible on tilted roads.
- Weighted Map Data for Stable Autonomous Vehicle Localization
Selective weighting of stable map features over vegetation improves autonomous vehicle alignment accuracy and reduces map update burden.
- Recommended Lane Control Using Lane-Specific Map Data
Lane-level congestion and restriction data help determine a recommended lane, while occupant preferences shift lane-change timing for comfort.
- Road Boundary Image Processing for Hazard-Aware Vehicle Control
Deep learning identifies hazardous road boundaries from vehicle images to improve control signal credibility and driving accuracy.
- Occupancy Grid Caching for Faster Autonomous Vehicle Motion Planning
Precomputed occupancy grid movies and cached vehicle-cell queries cut motion planning time while preserving reliable obstacle prediction.
- Vehicle HUD Virtual Element Layering for Depth Perception
Layered virtual HUD subelements use size, sharpness, and brightness gradients to reduce the ramp effect and improve depth perception.
- UAV Carrier Signal Mapping for 3D Cellular Coverage at Altitude
Preplanned UAV flights collect cellular signal diagnostics across altitudes to map interference zones and improve 3D network coverage.
- Factor-Graph Road Map Updates for Real-Time Lane Correction
Sensor-driven factor graph updates correct lane line positions in real time, improving autonomous vehicle map alignment and steering.
- Swarm Trajectory Plausibility Check Using Nearby Vehicle Paths
Nearby vehicle trajectories are compared with swarm-based paths to validate assisted driving when lane markers are missing.
- Autonomous Route Planning Using AI and Driver Preference History
AI combines traffic data with driving history to choose autonomous routes that preserve efficiency while matching user habits.
- Dynamic Coordinate Origin Shifting for Vehicle Trajectory Precision
Shifting the coordinate origin by speed, distance, or compute limits keeps position values small, reducing rounding errors in vehicle trajectory planning.
- Autonomous Vehicle Map Region Updates for Faster Navigation Refresh
When sensors detect a map mismatch, only the affected region is annotated and replaced, cutting update time and data transfer for autonomous driving.
- Autonomous Vehicle Planning Profiling With Complexity-Weighted Scoring
Weights each autonomous driving planning cycle by curvature, obstacles, and junction context to evaluate trajectory performance more accurately.