Instruments For Road Network Navigation TRIZ Cases

Wireless relative-position feedback corrects dead-reckoning distance and cornering angle errors for more accurate host vehicle localization.

Automated destination service booking uses route timing and real-time updates to reduce passenger input and improve ride coordination.

Hyperparameter-tuned machine learning predicts a driver's current destination more accurately while limiting distracting low-confidence displays.

Fusing camera, radar, and ML classification improves active road work zone detection by separating construction workers from pedestrians.

Route segments use different traffic algorithms by grid coverage and road type to improve accuracy without wasting computation.

Dual-sided earbud sensors combine IMU, microphone, and signal processing data to estimate gait asymmetry more accurately despite noise and drift.

Map and image data are combined to reject traffic signs from parallel roads, reducing false positives without missing valid signs.

Dynamic cloud routing uses vehicle telematics and package data to cut fuel use and delivery time while tying driver pay to energy consumption.

Edge roadside units process local V2X traffic data to deliver low-latency route, speed, and lane recommendations at intersections.

Users can erase unwanted route sections, letting the navigation system recalculate a preferred path when initial routing is suboptimal.

Continuous device tracking and route recalculation help first responders reach changing emergency locations and intercept moving targets.

Visualize routes and regions by travel time, scenic value, terrain difficulty, or incentives through map projections that reshape geographic space.

Graph neural networks model actor interactions from sensor and map data to improve autonomous vehicle trajectory prediction in complex scenes.

ML quantifies SLAM map accuracy from landmark distances and node degrees, avoiding ground-truth data and heavy sensor processing.

Uses visit frequency and recency to surface familiar landmarks in maps, improving orientation, search relevance, and navigation cues.

Historical pickup-region matching helps drivers accept trips that fit their destination and scheduled arrival time while improving on-time routing.

Real-time destination images help passengers assess accessibility, adjust routes, or cancel trips before travel begins.

Preselected road segments and auto-set capture parameters help mobile terminals record worthwhile scenery on time during vehicle travel.

Timestamps and regression-to-unknown keep robot occupancy grids responsive to moving objects without overvaluing stale sensor data.

Lightweight on-device models screen POI user content by context and quality, cutting mobile compute load and server bandwidth.

Route elevation sampling identifies uphill and downhill segments to recommend cruise speeds that improve vehicle fuel efficiency.

Vehicle sensors and server-side conflict resolution verify changing speed limits and lane states for more accurate navigation and control.

Historical route clustering and ML prediction enable proactive in-vehicle services without user input while limiting real-time processing.

Route-specific accident risk modeling combines vehicle sensor and environmental data to guide navigation and support real-time insurance pricing.

Audio-driven map highlighting changes road feature color and intensity during playback to improve spatial awareness in vehicle navigation.

Traffic alerts and selective turn-by-turn guidance let familiar-route navigation cut distraction and battery use without losing rerouting support.

Vehicle sensors and stopping data identify probable drop-off points, improving route planning, travel time, and delivery estimates.

Ride tags and vehicle constraints narrow the itinerary search space, speeding ride allocation under changing traffic and request conditions.

Adds 3D location depth, risk cues, and selective transparency so routes can be viewed, analyzed, and modified with clearer obstacle context.

Captures second-order POI paths and spatial distances to preserve complex relationships and improve downstream recommendation accuracy.

Selective sensor sharing across grouped light mobility vehicles expands situational awareness while limiting data load and collision risk.

Annotated map images highlight queried point-of-interest details so users can verify metadata quickly and trust search results.

Real-time travel time is compared with item preparation and delivery time to align pickup readiness with vehicle arrival and cut waiting.

Motion data from a passenger's gesture is matched with vehicle sensor detections to identify the right person in a crowd with less visual data.

Aerial-model coarse pose estimation is refined with mapped ground models to raise visual localization accuracy and success in large-scale scenes.

Preconfigured travel scenarios combine region, curb, and maneuver rules so vehicle routes can adapt to traffic, weather, and construction.

IMU sensor fusion estimates misalignment between sensor heading and travel direction to keep pedestrian and vehicle positioning accurate when GPS fails.

Wireless switching lets a mobile device use stable vehicle sensor data instead of its own, improving information use and processing accuracy.

By adding tridimensional location data to route views, this case helps users spot obstacles, assess risks, and adjust navigation paths.