Instruments For Road Network Navigation TRIZ Cases

Predefined automatic and manual drop-off options let autonomous vehicles match occupant alighting preferences without complex real-time setting.

Sparse vehicle observations are clustered by map tile confidence to generate weather warning polygons with lower computation and latency.

Historical and real-time PDZ data feed a probabilistic model that helps autonomous vehicles route to likely available pick-up zones.

RSU-assisted routing and platoon reconfiguration help urban vehicle groups clear intersections while limiting disruption to pedestrians and traffic.

Paired vehicles share only the navigation detail needed to follow together, improving trip coordination while limiting destination privacy exposure.

Transition markers extracted from occupancy grids cut memory use and transfer load while preserving real-time drivable space guidance for vehicles.

Dynamic depart constraints let autonomous vehicles avoid newly restricted areas while still exiting safely when a route change occurs.

Offline scoring of proximity, orientation, and length links map elements across different resolutions to improve route and cost-aware routing.

Lane-based offset and heading search improves map localization accuracy, reduces pose jumping, and stabilizes autonomous vehicle positioning.

Maps satellite signal weak zones by location, time, and satellite ID so work vehicles can reroute around terrain-related positioning loss.

Track changes are delayed until the movable object is far enough past a junction, reducing unsafe large-angle turns and temporary stops.

Mesh-linked autonomous vehicles share sensor data and reliability metrics to build trusted shared maps and coordinate safer, faster routing.

High-precision map features validate noisy long-range sensor data, helping automated vehicles detect short-term route changes earlier.

A virtual vehicle sends path points ahead to the AV, enabling safe remote guidance in unmapped or construction-altered areas without low-latency control.

Onboard sensors detect map-road inconsistencies and broadcast local V2V updates, keeping nearby vehicle maps current in near real time.

Vehicle sensor data predicts driver actions so navigation prompts can be suppressed or repeated to cut distraction and missed turns.

When a reroute cannot be achieved in time, the controller keeps the original route to avoid mode switching and maintain driving stability.

Fusing 3D camera, visual odometry, and GNSS data keeps row tracking and end-of-row turns accurate when GPS is obstructed.

Magnetic markers correct GPS and inertial position estimates to improve vehicle localization accuracy and route-following reliability.

Coordinated autonomous vehicles and shared base stations improve community item delivery, retrieval, and route efficiency while reducing duplication.

Selective hybrid map updates preserve stored positions and manual edits while refreshing only changed navigation data.

By ranking route candidates using map-certainty evaluation, this case improves map updates while maintaining viable assistance control.

Occupancy maps and graph analysis identify doorways and narrow passages where robots should not stop, reducing blockage and improving safety.

Context-aware selection of GPS, lidar, camera, and IMU localization variants improves autonomous vehicle positioning accuracy and computing efficiency.

Sensor-detected feature properties are compared with mapped setpoints to flag outdated automated vehicle maps and trigger timely updates.

Near-field wireless connection and user input verification let a vehicle set navigation destinations securely without repeated pairing.

Dynamic VTOL routing selects hub-to-hub flight paths using noise, weather, and traffic data to support quieter urban air transport.

Slope-section target data is excluded before 2D map matching, improving moving-body self-position estimation accuracy at intersections.

Passive metallic lane markers reflect RF signals to guide autonomous vehicles when GPS, LiDAR, and optical sensing struggle in rain, fog, snow, or glare.

Real-time crowd monitoring and controlled access help position mobile restrooms, charging, and Wi-Fi facilities where demand is rising.

Using telematics, survival analysis, and neural networks, this case predicts component life and future fleet incidents to guide prescriptive maintenance.

By identifying ride-sharing vehicles before toll collection, the controller enables ETC-based toll discounts without adding heavy roadside complexity.

Beacon triangulation and digital tickets replace paper haul slips to confirm pick-up and drop-off with more accurate vehicle tracking.

Iterative map matching refines lateral offset and heading to achieve stable lane-level vehicle localization in urban driving.

Vehicle-specific maps combine mixed-network data, access points, and real-time updates to guide autonomous vehicles indoors and outdoors.

Location- and preference-based ad ranking reduces AR navigation screen crowding by showing higher-priority ads first.

Roadside QR codes let vehicles refresh local map tiles when OTA links fail, improving autonomous driving reliability in low-coverage areas.

Relative pressure changes and ambient signal similarity group trajectory segments by floor despite noisy, device-dependent sensor readings.

CNN-based road image analysis classifies lanes without relying on manual map labels, improving autonomous navigation on marked or unmarked roads.

Real-time person locations from cameras and motion detectors update building maps so autonomous mobile bodies can route safely in occupied areas.

LIDAR landmark matching with map data corrects sensor-based vehicle localization, improving position accuracy with lower processing burden.

Radar markers and sensor fusion improve autonomous vehicle localization on existing roads without major infrastructure upgrades.

GPS-guided haul truck routing calculates safe approach paths to shovel loading zones, reducing collision risk and shovel waiting time.

A 2D planar beam and adjustable detector array reduce LiDAR beam count, power use, and processing load while preserving depth data quality.

A graphical UAV assignment interface links item requests to specific aircraft and status displays, improving pickup loading and tracking.

LIDAR intensity sub-regions and Gaussian mixture log-likelihoods enable fast, centimeter-level vehicle localization with lower compute load.

Obstacle data from different height layers is fused into a multilayer map, cutting path-planning overhead while improving route rationality.

Customer and tele-operator feedback help autonomous vehicles adjust pickup and drop-off parameters around obstructions while reducing support time.

User-traced map routes are refined with sensor data, direction, and time-of-day constraints to guide vehicles to feasible pickup and drop-off points.

Human input augments robot SLAM to identify blocked areas, room boundaries, and traversable zones for more complete maps.