Instruments For Road Network Navigation TRIZ Cases

By dividing a communication network into node-based areas, this case cuts similar route searches and shortens path calculation time.

Presents route options that combine roadway ride-on vehicles and sidewalk self-propelled mobility to improve guidance usefulness and accessibility.

Real-time occupant-state and route monitoring enables AI to suggest personalized detours and activities without constant route disruption.

Selective arc and lookup table updates let vehicle routing adapt to road edits in real time without full hierarchy reprocessing.

Distributed mobile sensors fuse drone position and trajectory data to deliver real-time alerts and tracking for unauthorized aerial activity.

Multiple LiDAR views and time-slice motion cues improve detection of occluded objects, hazards, and navigable space for safer planning.

Conditional customizable contraction hierarchies add and configure arcs to preserve connectivity while reducing compute load for real-time vehicle routing.

Modified Hausdorff scoring and linear optimization improve road polyline matching in complex intersections and small GIS links.

Segmented high-precision road data enables fast lane-level guidance on poor-network route segments while limiting download time and data use.

Segments branched vehicle path graphs into linear features, then matches and recombines them to improve digital map alignment accuracy and efficiency.

Geofenced vehicle journey monitoring detects route deviation, prompts for updated arrival time, and escalates alerts to operators and emergency services.

Imbalanced left-right headset volume cues upcoming turns, helping riders follow guidance routes with less distraction and fewer missed directions.

Machine-learned place categories guide pose graph optimisation and node annotation, improving mapping accuracy with lower computational cost.

Guide information is generated before arrival so passengers can use transfer time effectively between a vehicle and scheduled transit.

Vehicle sensor paths are normalized and aligned to map off-street parking and detect open spaces without GPS or site installations.

Navigation prompts adjust animation speed to vehicle speed and distance so guidance finishes before a turn and remains visible.

Caches only location-matched vehicle map data and builds a local spatial index to cut memory use while keeping fast, accurate queries.

A navigation app runs in the background, wakes the screen near turns, and restores foreground guidance without constant display use.

Segments a directed vehicle path graph into linear features to improve noisy trajectory matching against digital road topology.

Sensor-equipped vehicles fill road monitoring gaps with fresh, privacy-checked hazard and weather data for real-time map overlays.

Vehicle camera images and navigation data are combined to identify the current road automatically, improving lane positioning in complex traffic.

By combining GPS, crowdsourced, and stored route data, the vehicle can rate situational complexity and alert drivers to nearby challenges.

ATSC station broadcasts cut one-to-many vehicle data costs while filtering navigation updates by location and vehicle characteristics.

Transparent trading of transport seats and capacity units uses real-time pricing and blockchain audit trails to cut congestion and unused space.

Collection site images and truck position data are used to estimate garbage volume and update arrival times so users do not miss pickup.

Machine-learned route preferences help reconcile rider and driver choices, improving route selection beyond ETA-only navigation.

Rule-validated itinerary scoring improves driver and route selection by balancing cost, speed, risk, and package constraints.

Interactive AR 3D maps link object selection, gaze, and voice input to layered visual and audio information for more immersive exploration.

Fuses target and nearby vehicle movement data to improve road-level positioning in congested urban networks where GPS alone is unreliable.

Models residence-service-transport networks under disaster damage to identify failure points and route emergency vehicles more safely in near real time.

Encoded ego-trajectory, GNSS, and INS data improve road-segment matching and vehicle positioning on SD maps when satellite visibility is limited.

Predefined collection conditions and guided routing help gather image data across diverse driving environments for safer update model verification.

Routes are generated to bypass user-defined prohibited areas, combining touch or voice input with history and real-time data.

Road feature observations are used to correct GNSS, odometry, and sensor drift, improving vehicle trace alignment for precise localization.

Sequential magnetic anomaly readings and inertial distance updates narrow multiple map matches to one position when GNSS is jammed or spoofed.

Left-right audio volume control makes motorcycle turn guidance more intuitive, helping riders follow routes with less distraction.

Imbalanced left-right audio cues signal upcoming turns on a guide route, helping riders grasp direction faster through a headset.

A speed- and heading-based travel shape predicts likely waypoints ahead, improving map guidance when no route is predefined.

Precomputed geomagnetic and gravity vectors correct indoor azimuth distortion, enabling more accurate force-guided navigation.

Combining vehicle, handling, rider, and environmental data, this case uses ML to estimate driver state and tune e-bike comfort and safety.

Real-time route, task, and behavior detection adjusts pay-per-ride insurance rates to match gig driving risk and improve cost efficiency.

Location point distribution analysis helps identify a vehicle's route or lane in complex networks, improving tracking accuracy where GPS alone is ambiguous.

Geographic tile fingerprints and resequenced location data help match similar or reverse vehicle trips with lower comparison complexity.

Crowdsourced vehicle images are clustered and refined across journeys to build precise 3D maps despite calibration errors and changing roads.

Coordinates recommended-place sharing across multiple vehicles, then selects one location to trigger in-car ordering, payment, and route services.

AR eyewear detects package IDs and overlays delivery guidance, reducing manual scanning and handheld navigation during courier drop-offs.

Personalized routes pair ad checkpoints with route deviation limits and monetary benefits to make navigation ads more relevant and less disruptive.

Combining route conditions with a traveler's historical behavior, this case scores route risk to support safer transportation choices.

Multiple magnetic anomaly readings plus inertial vector distances resolve map ambiguity and improve position fixes when GNSS is jammed or spoofed.

Combining floor plans with outdoor map data enables continuous route guidance, real-time updates, and user-specific navigation in complex spaces.