Instruments For Road Network Navigation TRIZ Cases

Low-altitude, repeated vehicle communication interruptions are used to distinguish flood damage from other outages and predict affected areas.

Checks candidate routes against object dimensions, speed conditions, and nearby structures so only traversable paths are set.

An AR display scans vehicle interior surfaces and fits a 3D navigation map to the dashboard for clearer, less distracting route guidance.

Lane-group features and current-target position offsets improve lane change success prediction while filtering redundant inputs for faster guidance.

Gaussian mixture modeling and a 2D Markov chain predict lane congestion to improve route changes, ETA accuracy, and driving comfort.

Preloaded map reference frames let a voice assistant resolve spoken location references and start navigation with less network traffic.

Environmental and location metadata refine per-localizer error variance, improving autonomous vehicle positioning under changing conditions.

Multi-trip landmark matching uses similarity measures and SLAM alignment to improve route map accuracy at signalized nodes for automated guidance.

Voice commands and text-to-speech replace screen and keypad use, enabling safer in-vehicle navigation, calls, email, and SMS.

A 3D voxel-grid network uses selective 3D convolutions and anchor grids to detect stacked traffic signs for more precise HD maps.

Scored POIs, building boundaries, and hotspots make ride pickup points easier to identify, even where landmarks are weak.

Compares image-derived road regions with stored map data to detect shielded areas and improve road and lane reliability for driving systems.

Combining local and shared SGM models extends vehicle or robot environment range while cutting real-time data transmission and processing load.

Cached directions are compared with restored online routing so active navigation stays current without abrupt rerouting.

When a target location is crowded, the system predicts occupancy at alternative destinations using swarm and historical data to improve parking selection.

Multi-source fusion of GNSS-INS, wheel odometry, and vision SLAM improves traffic boundary mapping accuracy without costly LiDAR.

Standard navigation prompts are converted with region-specific templates so voice guidance matches local dialects, place names, and speaking styles.

Bloom filters let map servers send only outdated area content, cutting bandwidth and storage while keeping navigation data current.

Multi-leg transit guidance combines route filtering, user preferences, and real-time updates to improve navigation accuracy without overwhelming users.

Correlating multi-vehicle drive data into sparse road segment models cuts map data volume while preserving autonomous navigation accuracy.

A low-compute navigation approach estimates and displays safe return and reachable areas, easing range anxiety on mobile devices.

Map coverage is adjusted by driving level and vehicle position to limit data transfer while sustaining autonomous driving continuity.

Traffic light timing patterns are used to predict stop counts and vehicle energy use more accurately for route planning and range estimation.

Threshold-based vehicle magnetic field monitoring shares field strength and position so nearby vehicles can avoid sensor interference.

A road-network graph links GPS trip data to the right road segments, enabling popular route, stop, and roadway speed analysis at lower cost.

Fleet vehicle orientations are binned by grid cell and combined with lane boundaries to derive accurate lane courses, even in changing road layouts.

Map and surroundings descriptors are compared across time to raise confidence in vehicle self-position and keep automated driving within ODD limits.

Keeps the target area in a fixed on-screen direction by rotating the digital map around a set point, reducing navigation disorientation and UI clutter.

Visible-spectrum color sensing combined with side-camera position correlation helps vehicles distinguish primary and secondary road markings.

Predicted routes are compared with executed trace data to detect stale map segments, correct guidance, and generate alternate routes.

Compares heading angles and road types at common intersections to catch navigation-to-HD map route conversion errors before vehicle guidance.

Coordinate matching aligns moving ship maps with harbor terminal maps, enabling safe autonomous vehicle transfer between ports.

A navigation status layer classifies in-vehicle service requests and routes them to modules, easing expansion and reducing maintenance complexity.

Real-time outside video, location overlays, and voice calls turn simple vehicle streaming into an immersive virtual ride-along.

Segment-based GNSS validity checks filter urban canyon positioning errors, improving environment map quality and reducing correction work.

Tracks occupant gaze with vehicle location data to log roadside POIs for later access without manual note-taking while driving.

Real-time obstacle detection shifts the HUD navigation arrow within the driver's view to match road conditions and improve guidance clarity.

Selective prefetching of lane, lane-group, and road connectivity data cuts map storage needs while preserving driving assistance performance.

Low-cost 2D cameras build 3D road maps, flag likely missing features, and trigger targeted re-recording to improve coverage at lower cost.

An uncertainty map filters unreliable optical flow estimates to improve vehicle pose, velocity, and attitude estimation with fewer outliers.

Vehicle traces are recalibrated against known road objects to correct sensor offset and improve map-level road object positioning.

Travel plan data is used to check map update needs before departure, then request user approval so the vehicle has current navigation data.

Octree-based hazard filtering sends low-resolution V2V alerts and high-resolution map updates to cut bandwidth and latency in HD maps.

Proximity alerts link buyer location with a parked vehicle to help owners avoid face-to-face encounters during private inspections.

A batched backward search over contraction hierarchies cuts repeated memory loading and speeds multi-destination route computation.

Driver characteristic data is matched with onboard map routes, triggering re-search when coincidence is low to improve route accuracy.

Prioritized landmark and distance cues make route voice guidance easier to understand without overloading users at each guidance point.

Uses current location, travel time, distance, and motorcycle data to propose more suitable destinations and boost rider engagement.

By predicting route congestion and using user history, the server proposes alternative routes or destinations to users most likely to accept them.

Relative height data and fitted reference points preserve real elevation confidentiality while keeping map display images accurate and smooth.