Instruments For Road Network Navigation TRIZ Cases

When a mapped parking destination is blocked or outdated, the system detects access failure and redirects to a viable alternate spot.

Map features are tagged with change timing attributes so localization can weight outdated data and keep vehicle positioning robust.

By combining autonomous driving maps, navigation maps, and external positioning, this case identifies the correct road on unmapped routes.

Real-time route changes bypass construction zones and unsafe stop areas to extend Level 3 driving with safer occupant handoff.

Planned getting-off and getting-on points split field vehicle travel into manual and automatic segments for safer, lower-burden operation.

Precomputed 3D map-based wind factors guide urban drone routes by link and altitude to cut travel time, energy use, and cargo risk.

Inter-vehicle detection and distance data strengthen SLAM map creation where sparse landmarks limit localization accuracy.

Low-resolution onboard sensor data updates authoritative maps faster, reducing special mapping trips and navigation restrictions.

Grid cells with absorption levels and target-point diffusion enable fast aircraft route updates that respect flight constraints and FMS acceptance.

Nearby alternate drop-off points are proposed for client approval to reduce shared automated-taxi detours, travel time, and route inefficiency.

Machine-learned risk scores annotate map segments so autonomous vehicles can choose lower-risk routes under dynamic conditions.

Task dependencies, parallel labeling, and version control help distributed HD map workflows raise accuracy, consistency, and release throughput.

Real-time camera data from other vehicles updates route costs so autonomous vehicles avoid road conditions beyond their handling limits.

A flight management system plans speed envelopes around waypoint constraints to hold target speeds longer while lowering operating cost.

Aggregated vehicle curvature samples reveal where driven and map-based road curvatures diverge, helping autonomous driving avoid unreliable segments.

Pre-generated alternative routes and selective telemetry transmission help autonomous vehicles adapt faster to closures, congestion, and diagnostics needs.

Sensors and onboard control let a grain cart avoid obstacles, relocate between full containers, and unload autonomously to cut harvest delays.

Real-time position sensing replaces zone logic to create 3D safety bubbles that improve ride vehicle spacing, recovery modes, and collision control.

A layered request interface targets map data collection to relevant vehicles, cutting bandwidth and processing load while keeping digital maps current.

Sensor-equipped machines build terrain-adjusted field maps that improve guidance on slopes while avoiding the processing load of full 3D mapping.

LiDAR point clouds are tessellated into local plane cells to model uneven roads more accurately while keeping automated vehicle processing manageable.

Semi-static object filtering removes hidden landmarks from HAV localization maps, cutting data load while improving positioning robustness.

3D sensor data is projected into terrain-adjusted 2D paths, improving machine guidance accuracy on uneven and sloped land.

Magnetic road markers and IMU feedback keep vehicle positioning stable in tunnels and urban canyons where GPS accuracy drops.

Real-time machine status, timelines, and visual cues help users understand autonomous actions and adjust operations with confidence.

Vehicle-collected images are matched across and within groups to update map element locations with higher precision and faster coverage for autonomous driving.

Automated in-car links to fuel stations replace manual data exchange, speeding secure resource transfers along a planned route.

Combining linear programming with reinforcement learning, this case adapts vehicle schedules to road closures and breakdowns while reducing idle time.

Predefined change attributes let vehicle localization weight map features by update risk, improving positioning when digital maps are outdated.

Position and route matching lets sellers stop along existing trips to fulfill buyer demand while cutting delivery fees and vehicle energy use.

Projects 2D waylines onto 3D terrain data to compensate for slope and height, improving mobile machine guidance on uneven land.

An ECU predicts traffic on current and alternate routes to guide autonomous vehicles safely through congested intersections.

Operational log attributes are extracted and grouped into driving scenarios, speeding challenge-case discovery and reducing manual labeling effort.

Angled one-dimensional ground codes let AGVs read position and direction from any approach while avoiding costly RFID or 2D readers.

Dynamic SLAM parameter tuning adapts to road conditions and sensor quality to improve map clarity, localization accuracy, and reaction speed.

Linking AV lanes with coverage lanes creates a hybrid map that expands routing options and reduces delays across autonomous and manual modes.

Compressed predicate and trajectory indices enable faster real-time collision checks and motion selection for autonomous agents.

Feature point tracking estimates close-range inter-vehicle distance even when the target vehicle's lower portion is missing from the image.

A localized map quality index lets vehicle navigation weight map, camera, and detector data in real time with lower computation.

Edge filtering and distributed analysis convert mobile sensor data into trip and user records while easing compute and bandwidth demands.

Real-time ETA queueing staggers transport-provider arrivals at a common rendezvous point to cut congestion and wait times.

Trip-aware route optimization selects fueling stops and amounts for autonomous ride-hailing vehicles to cut fuel cost and idle time.

Historical re-route data flags confusing turns, letting navigation alerts adapt by location and user profile to reduce missed turns and distraction.

Historic ride demand, POIs, and map data are combined to model pickup and drop-off patterns in locations with no prior vehicle activity.

Real-time vehicle location and ETA interval tracking replaces manual haul scheduling, improving dispatch accuracy and fleet coordination.

Vehicles receive reason-coded SPaT updates based on traffic, incident, and emergency data, improving route decisions and response to signal changes.

Fusing GPS, vision, lidar, and ultrasonic data into one coordinate system improves UAV obstacle maps for reliable navigation.

A 3D path overlay shows vehicle routes, obstacles, and traffic flow so remote operators can update autonomous navigation in construction zones.

Curve fitting across GPS, inertial, and camera tracks improves lane-level map accuracy and supports updates for changing road features.

Real-time route weighting combines road, traffic, vehicle, and user preference data to balance travel time against energy use and emissions.