Instruments For Road Network Navigation TRIZ Cases

A virtual AV fleet tests routing versions and facility locations to cut energy use and trip time without slow real-world data collection.

Manual route runs build and refine confidence-scored virtual maps, enabling autonomous driving with fewer map updates and lower system complexity.

Vehicle and mobile sensor data are combined with accident and environmental context to assess driving risk in real time and guide safer routes.

Predicted vehicle dynamics drive real-time camber and caster adjustment to improve autonomous cornering stability and collision avoidance.

Observed object acceleration reveals traction on road surfaces ahead, helping autonomous vehicles adjust speed and braking before entry.

When a caution point falls outside the HUD view, the system switches from overlay to non-overlay alerts to avoid delayed driver notification.

When battery charge drops, the aircraft detects brighter locations and reroutes there to generate power and avoid altitude loss.

Side-by-side narrow and wide map views show nearby roads and the full disaster area, improving in-vehicle guidance during emergencies.

Environmental factor correction improves route energy prediction by combining speed fluctuation forecasts with section-based energy estimates.

External infrastructure position data cross-checks on-board localization to maintain accurate vehicle positioning when GNSS signals are blocked.

Movement-state detection switches navigation displays between walking and riding modes for seamless real-time guidance across mixed transport.

Thermal road-surface traces from earlier vehicles help autonomous systems choose a valid path when maps or other sensors are unreliable.

Sensor data converts driving behavior and conditions into risk unit consumption, enabling more accurate vehicle insurance coverage and feedback.

Lane-by-lane weighting uses vehicle position, lane direction, and intersection distance to improve route prediction accuracy at intersections.

GPS and accelerometer data from a mobile telematics app classify route familiarity in real time without dedicated in-vehicle hardware.

Virtual HUD elements are split into layered sub-elements with size, sharpness, and brightness cues to improve road-aligned depth perception.

Traffic-aware meeting point and timing selection lets a semiautonomous vehicle coordinate mobile order delivery with less manual planning.

Multi-sensor telematics combines GPS, sound, imagery, and vehicle motion data to detect unsafe road conditions and alert drivers in real time.

Vehicles pre-validate sensor travel data and share reliable detailed maps, speeding road updates for autonomous navigation.

Crowdsourced fleet energy rates let a vehicle adjust for extra load and driving conditions, improving distance-to-empty accuracy.

Routes are chosen by radar-localization object availability, then adjusted when localization quality drops to reduce driver takeovers.

Sensor-map comparison lets a lead AV detect road structure changes, update its route, and push revised map data to following AVs.

Prioritized switching among map, camera, and stored drawing data keeps vehicle navigation displays continuous when key sources fail.

Per-target matching of onboard sensor data with map features enables lane-level map quality checks and selective updates for automated driving.

By separating each passenger's voice and combining occupant records, the system estimates requests more accurately in multi-occupant vehicles.

Automatic route recording starts and stops at parking lot boundaries, cutting driver input while generating parking and unparking paths.

Combining map data, vehicle position, and surrounding road sensing enables stable lane-level localization on multi-lane roads despite GPS noise.

V2V, GNSS, and map-matching let vehicles assess roundabout priority and collision risk without roadside infrastructure.

Predicted road-segment conditions from multiple data sources enable rerouting and scheduling that keep automated vehicles moving within ODD limits.

Scenario-specific control modules generate real-time actions for merge and obstruction events, improving autonomous vehicle safety and traversal efficiency.

Predictive alarm stopping reduces repeated alerts in areas with many unfamiliar drivers, improving driving comfort without losing location awareness.

Route assignment uses road grade, speed, motor stress, and battery use to cut wear on alternative energy vehicles and extend fleet life.

Road surface markings replace adjacent vehicles as references to detect parking exit direction more accurately and avoid incorrect path calculation.

When a final destination is off pre-approved roads, the vehicle routes to an intermediary stop and guides the remaining transfer.

Fixing the charging station on the map display makes EV distance and reachable range easier to judge, helping prevent energy shortages.

A head-up display projects a virtual lead vehicle before tunnel entry to improve lane guidance, visibility, and driver comfort in low-visibility driving.

AR-guided remote verification removes misrecognized obstacles and restores a drivable path for more reliable autonomous vehicle control.

Route-aware OTA control pauses downloads before low-reception sections and resumes later to avoid restart failures and extra communication cost.

Urban light source color and brightness patterns are matched to dynamic map references to keep automated vehicle positioning precise across weather and time changes.

Historical vehicle logs score road-segment difficulty so autonomous vehicles can choose routes that balance time, distance, and disengagement risk.

Urgency- and importance-based voice output highlights critical vehicle information while reducing driver distraction and adapting to communication status.

Real-time lane mark type and target vehicle analysis helps autonomous vehicles handle merges and splits with safer steering and speed decisions.

By matching live electromagnetic reflections to a 3D surfel map, this case cuts false positives in autonomous vehicle foreign object detection.

Vehicle sensor and OBD data are analyzed to detect road defects early, trigger repair alerts, and update navigation guidance.

Perceived risk is added to trajectory cost optimization so autonomous vehicles stay objectively safe while improving passenger comfort and reducing intervention.

Lane-based vehicle lateral positioning is checked against mapped landmarks to reject false estimates caused by lane line recognition errors.

Preselecting nearby vehicle cameras and opening connections early cuts display delay when traffic or destination conditions trigger video viewing.

Zone-by-zone road matching flags position gaps between navigation and location maps, helping automatic vehicle control stay safe.

A neural network estimates alternative drop-off points and ranks routes to match passenger preferences on time, distance, and cost.

Early alerts compare remaining vehicle range with nearby supply station distance to help drivers avoid running out before refueling or recharging.

A surrounding environment recognizing apparatus determines time-of-presence ranges for vehicles and obstacles to assess driving risks.

A navigation system detects frequent route segments to generate travel information from map data changes.

A host vehicle determines its global position using target vehicle location data received via wireless communications.

Contextual analysis of route, location, and user history generates cleanliness scores that trigger operator alerts when thresholds are breached.

A navigation system corrects dead reckoning position errors using feature measurements from surrounding objects.

A map-centric technique projects probe points onto road segments using defined vertices and spatial search separation distances.

A navigation system limits displayed lane graphics to a maximum count using replacement symbols for unshown lanes.

A navigation system calculates an optimum via-point for a fellow passenger using preferred traffic information.

A position estimation system calculates an expected error radius from multiple sensor inputs to manage automated driving operations.

A navigation system aligns approach links with the 12 o'clock screen position using circle crossing points.

Segmented grip detection zones prevent accidental switch activation while allowing convenient onboard equipment operation.

A prediction system selects historical models using confidence metrics to determine real-time traffic flow.

A driver assistance method shifts and rotates a target parking position using an input unit for precise vehicle placement.

A portable navigation device stores position and time data only when specific operational events occur.

Converts subjective pilot reports into objective EDR metrics to resolve data accuracy contradictions and improve flight path planning.

A navigation system generates alerts when user location differs from a remote location.

Pollution mapping system processes relative variation data from low-cost vehicle sensors to generate high-resolution traffic maps.

A dynamic mapping system applies multiple visual styles to render navigation routes with high detail while de-emphasizing non-route features.

Segmented sensor arrays on gantry struts resolve space constraints to ensure reliable collision avoidance.

A navigation system processes sensor data packets to identify real-world coordinates for parking spaces and obstacles.

A congestion prediction server generates reference information for in-vehicle devices using scheduled route data.

Route planning system calculates optimal paths using vehicle-specific data and tachograph inputs to satisfy mandatory driving rest periods.

A navigation system detects mobile device location to guide users toward selected places of interest.

A controller extracts map change points by analyzing line information from surrounding vehicles to update navigation data.

Caching occlusion data from a preliminary three-dimensional simulation reduces computational load and runtime for autonomous vehicle testing.

Client devices cache remote resource references within map tiles to render graphics locally, eliminating network dependency during offline map rendering.

Automatic image capture system uses geospatial and temporal triggers to reduce continuous data collection burden while maintaining geographic accuracy.

Group road segments into paths using representative data to reduce map tile storage size, resolving network throughput throttling at middle zoom levels.

Navigation routes process manmade weather data to avoid cloud seeding hazards and maintain delivery speed.

System calculates preferred shop and nearest entrance to eliminate manual navigation time in large parking lots.

Detects traffic light systems by comparing sensor-derived positional data against a reference model to identify marked hazard areas.

Region-based publish-subscribe middleware delivers dynamic vehicle information while minimizing bandwidth consumption and preserving user privacy.

A lidar system validates virtual horizon estimates using GPS and IMU reference data to adjust sensor parameters.

Segmenting cargo into packets routed via relay stations reduces empty miles and fuel consumption while maintaining delivery productivity.

Segmenting high-resolution map data into subsets and applying an XOR function reduces network bandwidth consumption while maintaining navigation accuracy.

Adjusts probe data weights via decay functions to remove outdated traces and improve map accuracy.

A vehicle seat sensor system monitors biometric and environmental data to determine occupant fatigue levels.