Instruments For Road Network Navigation TRIZ Cases

Coordinated ground and aerial vehicle handoffs bypass urban obstacles, cutting delivery time, energy use, and service cost.

Dual 2D maps from 3D LiDAR data separate obstacles for faster indoor robot positioning and safer path planning without beacons.

Wind-aware platoon selection compares drag and parasitic energy loss across routes to cut fuel use, labor hours, and travel risk.

Loads high-detail maps only where vehicle localization needs them, cutting processing time and data burden for automated driving.

Preplanned route extensions let an autonomous vehicle bypass unavailable stopping spots without jerky slowing, reacceleration, or extra routing load.

A center-truncated measurement model helps radar tracking estimate object position, size, and orientation under noise and multiple reflections.

Simulation compares blocked and unblocked autonomous vehicle routes to identify affected trips and prioritize service-area improvements.

Low-density Lidar regions are analyzed by shape, position, and map context to detect dark or smooth objects and separate them from shadows.

Grid-based block partitioning and reciprocating coverage paths reduce obstacle-driven rerouting while improving complete area coverage.

Movement-route scheduling selects the best communication network by location to balance transmission cost with delay requirements.

Motion values are scaled to fixed USB intervals, correcting asynchronous sensor timing for steadier cursor speed and accurate tracking.

Key-frame image matching calibrates odometer and gyroscope drift, improving mobile robot map accuracy and path planning.

HD maps, LIDAR, cameras, and AI overlays improve pickup and drop-off location accuracy when GPS is unreliable.

Sensor and contextual data are converted into fault percentages for vehicles, pedestrians, and conditions to improve autonomous vehicle insurance pricing.

Iterative offset-based tuning calibrates localization parameters across map and sensor data sources for accurate vehicle positioning without manual setup.

Subscriber profiles and route data drive network-sliced vehicle settings for personalized cabin comfort and route-aware travel.

Visibility constraints help robots distinguish co-located markers with limited fields of view, improving localization accuracy in warehouses.

Low-altitude UAV routing uses holographic light patterns to mimic road vehicles, improving driver recognition and reducing urban safety concerns.

Passengers complete micro-tasks during autonomous vehicle trips, with detected completion used to adjust fares and improve time use.

Route-segment QoS scoring combines map, weather, road, and sensor factors to rank routes by achievable autonomous driving levels.

Routes platoons by RSU availability and intersection limits, then reorganizes vehicle formations to improve urban traffic flow and safety.

3D terrain-based stochastic planning balances distance, roughness, and altitude change to speed rover route replanning and reduce vehicle damage.

Low-resolution sensor data overlays changed road features on a primary map, reducing autonomous vehicle mis-localization during map lag.

Pre-retrieved partial routes keep automated driving control active while the navigation unit completes redundant route retrieval.

A cloud-linked vehicle infotainment platform connects IoT devices and vehicle data while filtering interfaces to reduce driver distraction.

A helper app finds accessible parking spots, guides the rider, and directs the autonomous vehicle for safe independent boarding.

Vectorized floor plans and convex hull simplification automate indoor path generation, reducing manual mapping time while preserving navigation accuracy.

Probabilistic and ML confidence scoring validates GNSS with IMU, camera, and LIDAR data for more accurate vehicle localization and HD map updates.

Vehicle sensor data is compared with mapped feature properties to flag outdated road sections and cut costly full-scale remapping.

Markers added at start points, waypoints, and docking stations help SLAM correct pose drift and improve map closing in large scenes.

Projected implement position and section states help operators anticipate ASC timing errors during speed or heading changes.

Location-resolved reliability data helps autonomous vehicles weight detectable map features for more accurate localization and path planning.

Calculating intersection reference points and lane offsets from map data makes MAP messages more consistent and accurate for vehicle communication.

Synchronized ground locating devices replace error-prone GNSS signals to deliver precise 3D vehicle positioning in obstructed urban environments.

Map-based scoring suggests nearby pickup and drop-off alternatives when a requested stop is unsafe or inaccessible for autonomous vehicles.

Per-trip vehicle insurance uses route, weather, road, and driver data to price coverage more fairly and process payments by trip segment.

Multiple detected roadside objects anchor two digital maps, correcting rotation and position errors for more reliable automated vehicle navigation.

Point cloud matching with Gaussian process and Kalman filtering corrects localized map errors for more accurate vehicle lane positioning.

Dual-map route planning balances lane-level map accuracy and travel cost to improve autonomous driving control on mixed-detail roads.

Detection-frequency mapping reveals weak warehouse landmarks, helping improve localization viability and faster vehicle deployment.

Real-time user location and provisioning limits help automate vendor delivery timing while improving interaction speed and data security.

Precomputed U-turn route elements let work vehicles balance turn space, ground damage, and route efficiency without delaying work start.

Sparse 2D mapping of cylindrical and planar roadside structures cuts map size while supporting fast lane-level vehicle positioning.

Pre-assigned autonomous vehicle trips use predicted pickup and destination options to cut empty miles, shorten pickup times, and improve ridership.

By mapping sunshine across road and floor surfaces, this case recommends indoor-outdoor routes that reduce sunlight exposure during travel.

Road-segment energy characterization helps multimodal vehicles allocate fuel and battery use in real time to cut fuel consumption and preserve charge.

When a vehicle stops away from the expected pickup point, a robot meets the user and guides them to the actual vehicle location.

By extracting key road geometry from navigation data, this case expands waypoint map coverage while limiting storage and preserving driving support accuracy.

Guidance data directs a manned vehicle to a stopped unmanned vehicle, cutting response time and limiting work site downtime.

Pre-trip route change approval and fare discounts let a vehicle add co-riders without disrupting the original user's schedule.