Instruments For Road Network Navigation TRIZ Cases

Predicted QoS is used to switch tele-operated vehicle modes and speed limits, improving safety under latency and data-rate constraints.

Selective map patch downloads and landmark-sensor fusion cut bandwidth use while keeping autonomous vehicle positioning reliable.

Shared service assignment lets one autonomous vehicle carry users and robots together by balancing travel time, cabin space, and cost.

Grouped detections from multiple autonomous vehicles confirm traffic object changes before map updates, improving timeliness and accuracy.

Hardware and software similarity scoring ranks fleet vehicles for software, firmware, and security updates, reducing manual planning time.

Landmark-based distance correction offsets non-contact sensor error, keeping work machines on path when GNSS accuracy drops.

Broadcast fiducial position and identity data help vision and inertial navigation maintain precise guidance where GPS is degraded.

Selective transparent cubbies and recipient authentication secure item pickup in autonomous delivery vehicles while preserving multi-stop capacity.

Multilayer obstacle maps capture height differences for robot navigation while avoiding the heavy processing load of full 3D mapping.

Routing weighs lane line quality, curvature, and traffic so vehicles favor roads where assisted or automated driving can operate reliably.

Raw sensor and map data are fused into a cost volume to choose interpretable, accurate vehicle trajectories in complex urban driving.

Timed, intensity-based notification planning helps automated driving occupants receive failure and mode-change alerts without added distraction.

Recorded path coordinates and onboard sensors let autonomous robots clear sidewalks and other obstructed areas with less manual labor.

A limited visibility map keeps autonomous vehicle navigation current while reducing bandwidth use and preventing map version conflicts.

Selects standardized containers by parcel shape and assigns loading positions to cut wasted cargo-bed space and speed loading.

A fleet manager assigns map-scouting objectives by vehicle location and availability, improving coverage without wasting idle driving.

Astronomical route lighting forecasts help autonomous vehicles reroute or stop before poor visibility degrades sensor perception safety.

Predictive trip planning adjusts throttle, braking, and power sources before deration-prone areas to maintain vehicle group power.

Projected implement position and section states give operators early visual feedback to correct driving behavior near field boundaries.

Walsh kernel projection turns 3D point clouds into map-matched feature cells, improving autonomous vehicle localization accuracy with lower computation.

Joining costs from route data and vehicle locations are used to choose a transfer vehicle and meeting point that reduce rider burden.

Slow cruising collects running data to update maps in real time, balancing unmanned vehicle stability with adaptation to changing surroundings.

Autonomous frontier selection guides a smart device to unknown map regions, cutting manual mapping time while updating obstacle probabilities.

Movement trajectory and new visual poses help reject false relocalization hypotheses and improve alignment with a pre-built visual map.

Simulated routes with and without road blockages quantify travel time and maneuver impacts, helping prioritize autonomous service improvements.

Extracting floor points from 3D tunnel scans cuts route-planning load while preserving accurate underground vehicle positioning.

Polyline path deviations align sensor-captured road features with map references to improve vehicle pose estimation in noisy or GNSS-limited driving.

Preplanned canonical routes are updated after boarding using user input and current conditions to keep autonomous vehicle travel adaptable and efficient.

A map shows a flexible pickup zone, route endpoint, and live vehicle status so riders understand where an autonomous vehicle can actually stop.

A friction-coupled rear wheel layout keeps both wheels synchronized in bicycle mode, then separates them for stable autonomous tricycle travel.

Real-time container status and obstacle sensing let a grain cart switch unloading points autonomously and keep harvest transport moving.

Traffic sign height from map data helps distinguish stacked roads and correct dead reckoning without full 3D map matching.

Automatic registry-generated vehicle identifiers improve secure fleet communication and data reconciliation across multiple databases.

Detailed map data is used to suggest nearby pickup and destination stops that autonomous vehicles can reach safely and passengers can access easily.

Machine-learned waypoint scoring replaces brittle heuristics to map driveway and parking-area entries for more accurate autonomous vehicle behavior prediction.

Real-time LiDAR and camera data annotate drivable map features, improving autonomous navigation accuracy in changing road conditions.

Trajectory-based warping updates lanes and crosswalk map elements after loop closure or recalibration, reducing manual remapping time.

Dual-mode RFID tags and ML models track vehicle paths, predict travel times, and calculate charges with lower power and compute burden.

Keeps a work vehicle on one selected RTK base station to avoid positioning dropouts and navigation interruptions between coverage areas.

Raw sensor data feeds a cost-volume planner that selects target trajectories while handling uncertainty and multimodal traffic scenarios.

Path planning and coordinated control let a continuous miner and modular conveyor follow curved plunge tunnels while avoiding ribs and nearby equipment.

Intelligent raised floor elements use LED guidance and movement tracking to reconfigure AGV routes in real time without manual track changes.

Perception data measures passenger walking distance and road-edge offset to map inconvenient AV pickup points and improve access.

Real-time ETA-based virtual queues and route updates raise pickup throughput while cutting congestion and wait times at shared rendezvous points.

Upstream event prediction combines traffic, road, weather, and sensor data so a remote operator can take control before hazards are reached.

Separating destination input from autonomous mobile robots cuts onboard interface complexity, lowers fleet cost, and reduces user waiting time.