Instruments For Road Network Navigation TRIZ Cases

By associating guide and map layers from vehicle position and path data, this case reduces HUD misregistration discomfort and driver distraction.

Traffic-based segment scoring identifies where assisted lane changes are feasible before a route limit point, improving smoothness and certainty.

Blind-spot defects are removed from template point clouds so mobile body position and orientation can be estimated accurately and faster.

When unsafe driving is predicted, the vehicle stops occupant assist functions and switches to driving assist to maintain control and safety.

Route segments are evaluated for energy, time, resource use, and passability risk to generate speed profiles that improve track accuracy.

Machine learning predicts wheel load and vertical displacement from onboard data to detect road disturbances and update shared map data.

A battery scheduling device matches reachable charging stations to SOC, availability, cost, and user preferences to cut waiting time.

Movement data from multiple forward vehicles and sensor tracking are combined to calculate a more accurate driving path with manageable control complexity.

Sensor-guided autonomous aircraft control selects safe landing sites and reduces human operator needs for emergency medical service flights.

Multiple waypoint sequences and trajectory scoring improve lane-change prediction continuity, lateral avoidance, and smooth deceleration.

Dual soft constraints use obstacle appearance and speed to keep autonomous vehicles safely farther away with smoother, more stable avoidance.

Cross-checking map and sensor lane boundaries is selectively suspended near movable center lines to prevent vehicle position errors.

Probe data is partitioned into grid cells and directional groups to build accurate 3D road paths while filtering noise and special areas.

Priority-based bounding boxes create safety corridors that keep smoothed vehicle paths compliant with higher-priority rules and constraints.

Vision, motion, and lane-map fusion pinpoints vehicle lane position and lateral offset where GPS lacks lane-level accuracy.

Driving-environment analysis triggers a high-pressure air curtain to keep vehicle sensors clear of dust, rain, snow, and insects while limiting energy use.

BLE seat modules detect occupancy, guide riders to reserved seats, and align pickup logistics for multi-passenger ride sharing.

Probe data is map-matched and split into lateral spatial bins to compare lane speeds and identify when a restricted lane offers a real travel benefit.

Coordinated moving position-targets let autonomous vehicles switch between roadway control systems without trajectory conflicts, jams, or collisions.

Camera data from multiple autonomous vehicles is compared with stored maps to correct GPS errors and keep road information reliable.

Sensors shift vehicle UI elements between driver and passenger sides to improve shared access while preserving operator attention.

Sensor fusion infers hidden traffic signals and traffic flow from surrogate data, helping autonomous vehicles navigate occlusions safely.

A portable device overlays the parked space and starting point on an image, helping users quickly find a vehicle after automated parking.

Local feature extraction and frequency compression cut sensor data volume, improving transmission reliability in multi-sensor measuring systems.

Priority-based road pattern projection organizes vehicle and environment cues by importance and position to keep critical information clear.

LiDAR-based road surface sensing lets the HUD align AR virtual images to sloped roads and avoid image penetration through the surface.

Real-time geofence demand and operator bids guide EV charging recommendations that improve station utilization and user choice.

Trajectory deviation from mapped lane boundaries is used to flag unreliable map sections and support safer driving assistance.

Feature point tracking measures inter-vehicle distance when the target vehicle's lower portion is missing from the driving image.

Neural-network scoring of trajectory deviations quantifies road user influence, improving collision avoidance and trajectory planning.

Cloud-based navigation lets lower-equipped vehicles use shared sensor data and remote processing to reach higher automation with less onboard hardware.

Predictive route demand and battery health are used to switch SOC limits, balancing usable energy with lower battery deterioration.

When sensor recognition limits autonomous speed, the system reroutes vehicles to roads with compatible speed limits to reduce traffic disruption.

A server coordinates multiple rescue vehicles, secures battery charge levels, and pools energy to assist a stranded EV with low battery.

Graph neural networks turn road topology and vehicle trajectories into compact drivable path maps, reducing data volume while preserving navigation accuracy.

Gesture-aware map scaling distinguishes pinch-in/out from swipe input to keep the correct current-location scale without manual reset.

Predicts weather-driven standstill delays, estimates heating battery drain, and flags when extra EV charging is needed before travel.

Displays route energy use and charging amounts separately for stations and wireless charging roads to help EVs reach the destination.

A virtual leading vehicle overlaid on the road helps drivers follow turns and exits more clearly in complex or low-visibility conditions.

Vertical wheel-motion matching to stored road profiles improves vehicle localization when GPS lacks the resolution needed for suspension and autonomous control.

When an in-vehicle emergency occurs, the vehicle compares ambulance, first-aid vehicle, and hospital routes to choose the fastest care path.

Pre-recorded routes and before-after crop data let different work vehicles share task paths, cutting setup time and calibration effort.

Illumination maps combine sensor and route-segment solar data to guide vehicles along navigable paths with higher solar energy exposure.

Position-tagged image data are turned into concise VQA text descriptions, enabling safer component actuation and targeted V2X sharing.

Route planning adapts autonomous trips to passenger or remote worker driving ability, avoiding unsafe manual zones or preparing safe handoffs.

Road condition mapping lets an autonomous utility vehicle adjust speed and steering ranges ahead for reliable rough terrain travel.

Fusing GNSS/INS, NDT map-based, and lane-matching localization with region-specific weights improves vehicle position and orientation in landmark-poor roads.

A vehicle mirror projector casts aligned arrows, maps, or text after parking, guiding drivers to the destination without smartphone re-entry.

A navigation system estimates return-path energy from current position and battery status to avoid random base-station searching and cut charging downtime.

Pre-arrival messages use item attributes and user context to prepare recipients and adjust drop-off settings for hard-to-carry deliveries.

Aggregated width and position data from preceding vehicles helps automatic driving stay active in snow when lane markers and sensors fail.

Surface friction estimates are turned into motion-planning constraints so autonomous vehicles stay within traction limits on changing roads.

A vehicle map interface turns remaining mileage into a visible reachable area, reducing manual estimation and speeding route planning.

Cropping, vanishing-point detection, and sensor tilt compensation keep in-vehicle AR navigation aligned without manual setup.

A control device matches charging time requests with suitable parking lots and facility privileges to improve mobile EV charging convenience.

By comparing sensor-detected road geometry with map data, this case improves curved-road accuracy and stabilizes vehicle control.

Real-time navigation directions are converted into light, sound, and vibration cues to cut driver distraction and cognitive load.

Low-res candidate screening followed by high-res traffic light analysis cuts processing load while preserving detection accuracy for autonomous navigation.

Selective sensor activation matches vehicle pose and landmark type to cut parking-localization energy use while maintaining accurate positioning.

Sparse factor graphs link shared trajectories and sensor data to cut map storage and update time while preserving autonomous vehicle navigation accuracy.

Fleet-shared uncertainty maps combine object locations and ID probabilities to improve vehicle detection and tracking in low visibility.

Regional map data is split and prioritized by route proximity so autonomous driving control gets timely map coverage with less transmission load.

Map and GNSS route data stabilize target course generation when lane lines break or become unreliable at road forks and merges.

Multiple vehicle detectors identify whether an emergency light signals an accident or another cause, enabling faster, more accurate rescue guidance.

Real-time route planning combines charging, vehicle, driver, and weather data to improve EV trip efficiency and navigation safety.

Visual indicators and route guidance help customers find selected vehicles in large lots without human guides, improving self-service navigation.

Edge-generated traffic light ROI templates cut onboard image-processing load and latency while improving detection in complex road topologies.

Uses travel data from other vehicles on candidate routes to account for weather, road surface, and parking conditions in route selection.

Dynamic mode switching adjusts speed, detection range, and maps so self-driving vehicles can navigate changing pedestrian traffic and guide paths safely.

An energy transfer arm lets a support vehicle power a disabled battery machine while both travel in convoy to maintenance.

Adjusts lane-change notification timing when an earlier branch appears, helping drivers avoid mistaking it for the intended exit.

Route options mark autonomous, semiautonomous, and manual segments using failure probabilities so drivers can prepare for control handovers.

Autonomous vehicle fleet data creates low-resolution tiles and semantic updates to cut HD map latency and reduce mapping car dependence.

Adaptive point group selection improves vehicle self-position estimation by matching traveling maps to past maps, even in residential areas.

Passenger inputs are ranked against safe, legal AV actions so riders can influence maneuvers without compromising reliability.

Boundary-based centerline path generation gives vehicles more free space in curves and U-turns while reducing jerks during navigation.

AR glasses display autonomous vehicle paths, service needs, and hazard alerts to reduce service center risks and missed tasks.

Edge and V2I data incrementally update vehicle routing models, improving adaptation to changing road conditions without static retraining.

Seat and luggage occupancy sensing triggers empty vehicle auto-return, improving facility sharing efficiency while maintaining safe operation.

Offline trail maps, shared rider locations, and vehicle telemetry are combined to improve route planning and remote support in rural off-road travel.

Intercepted requests and tenant-based web tokens secure robotic fleet reassignment while adapting access policies and resource permissions.

Selective lossless and lossy LiDAR encoding cuts map data volume while preserving the precision needed for fast HD map updates.

Precomputed charger-to-charger paths and failure prediction keep EV fleets charging while reducing relocation delays and collision risk.

Tiered service-code authentication verifies third-party operators before they take secondary control of autonomous vehicles in difficult service scenarios.

Adaptive EV monitoring predicts SOC from route and driving history to plan charging, manage thermal limits, and preserve track or off-road range.

Limits autonomous vehicle trajectory planning to the traffic-control stopping point, cutting constraint calculations while maintaining safe path generation.

Nearby connected cars are tasked by sensor type and location to verify regional changes, improving HD map accuracy and update speed.

Relative terminal-to-vehicle positioning is updated by reliability to guide autonomous parking exit where GPS is weak or unavailable.