Instruments For Road Network Navigation TRIZ Cases

Site-specific driving rules and centralized waypath planning let delivery vehicles handle parking, fueling, and maintenance with less human intervention.

Position-based matrix selection converts mining vehicle coordinates between worksite models for accurate automation control across mixed positioning systems.

Automated target determination, position estimation, and circling simplify UAV POI orbit control while maintaining accurate target tracking.

Automatically warps lane and crosswalk map elements after trajectory updates to keep 3D map data accurately registered for autonomous driving.

Route-segment QoS scoring combines map, weather, traffic, and sensor health data to rank paths by achievable autonomy level.

Plans all robot behaviors up to a goal-reaching time so goal-less robots can be coordinated without conflicts in multi-robot path planning.

AI combines vehicle constraints, routing data, and telematics feedback to improve heavy-vehicle urban guidance and cut emissions and costs.

Real-time map feedback shows an autonomous vehicle, a pickup zone, and route progress so users can find the actual stop location.

Facility map and mobility data are used to assign guide robots by area, cutting travel time and enabling smooth handover between devices.

Land vehicles act as mobile drone bases, extending UAV delivery range and capacity without adding more sorting facilities.

Weighted autonomy indices from GPS, LIDAR, and road markings rank road segments for more reliable autonomous navigation.

Partial-derivative criticality values expose sensor measurements that overly influence Kalman navigation estimates, improving maneuver reliability.

Candidate VTOL routes are ranked by noise limits, weather, and traffic to cut travel time without exceeding urban noise thresholds.

Spatial residual maps calibrate nutrient models to each agronomic field using historical yield and application data, improving recommendation accuracy.

Geographic pre-setting of vehicle image sensor parameters cuts sensing latency and improves object detection under local lighting conditions.

Candidate transfer stations split large grid searches, helping robots avoid blocked areas and return to charging bases faster.

Orientation-specific reliability maps let moving objects keep stable position estimation while using routes that fixed low-reliability exclusions would block.

Time-stamped image updates and position data let a mobile body generate selectable routes more easily through wireless guidance processing.

Combining transit time and waiting time predictions improves public transport departure reliability for more precise journey planning.

Real-time demand data guides autonomous vehicles to relocate into higher-use, easier-to-access areas, improving fleet availability and utilization.

AI routing coordinates concurrent delivery tasks and updates routes from live conditions to improve vehicle revenue and time use.

Occupancy-map filtering discards false sensor detections before localization, cutting computation and improving autonomous vehicle positioning.

A virtual leader smooths aircraft guidance to moving target points, reducing bank-angle oscillation while preserving approach alignment.

Dynamic ETA-based queue routing coordinates matched drivers at shared pickup zones to cut wait times and ease congestion.

Traffic sign height from map data helps distinguish closely spaced roads at different altitudes and improve vehicle self-location accuracy.

Dynamic VTOL routing uses real-time noise, weather, and traffic data to balance urban travel speed, capacity, and noise exposure.

Signatured Gaussian Mixture Maps improve vehicle and robot localization by matching dynamic sensor data with less noise sensitivity and lower storage demand.

A flexible virtual bumper changes size or shape during route simulation, helping mobile robots keep clearance and pass through tighter spaces safely.

By detecting weak radio access points ahead of travel, the vehicle manager reroutes or adapts remote vehicles to maintain reliable control.

Discrete time-series path weighting schedules shared travel routes to avoid collisions and deadlocks while reducing waiting time and path cost.

Cost-based leader and follower routing helps vehicle swarms avoid obstacles while preserving formation in complex environments.

Keyframe transforms and double buffering cut world model data 10:1 to 40:1 while preserving timely autonomous vehicle navigation.

Combines AVs, transit, and walking into one real-time route and booking flow, cutting manual trip planning and separate orders.

AI-driven routing schedules concurrent vehicle tasks from real-time data to cut idle time, simplify task selection, and raise revenue.

AI-driven routing uses vehicle and sensor data to reassign concurrent delivery tasks in real time, improving utilization and revenue.

Demand-driven autonomous vehicle relocation moves fleet cars to higher-use areas, improving renter access and vehicle utilization.

Idle autonomous vehicles are pooled through owner registration, dispatch, billing, and payout tools to provide taxi and delivery service without fleet ownership.

High-frequency in-vehicle position sequences let a server detect parking trajectories and update parking occupancy more accurately with less data transfer.

By using laser observation direction and distance ratios at grid intersections, this case improves 2D and 3D robot map positioning accuracy.

Preplanned vehicle routes pass cleaning-capable areas so baggage locker odors or dirt can be resolved quickly with minimal detour and downtime.

When satellite signals fail in shadowed areas, stored object locations and distance sensing keep a robotic work tool navigating accurately.

Corrected odometry uses map-based localization and a vehicle-specific drift model to maintain accurate vehicle position where precise map data is unavailable.

Dynamic action planning adjusts robot safety distance by user acceptability and task purpose to balance safe interaction and service efficiency.

Reference-station correction data improves GNSS positioning so air vehicle flight paths can be determined more accurately and with less manual setup.

A network node checks wireless coverage along planned vehicle routes and suggests safer alternatives for reliable remote fleet teleoperation.

Signature, motif, and watermark inputs let a single DNN detect hardware or software faults in real time without multi-network overhead.

Camera and LiDAR data extract lane-marking geometry automatically, cutting manual control-point work in HD map updates and localization.

Real-time driver location and order preparation updates enable delivery reallocation that cuts wait time and keeps assignments optimal.

Real-time LiDAR and depth-camera path adjustment helps a guiding vehicle steer visually impaired users around obstacles in complex spaces.

When SLAM or loop closure shifts a 3D mapping trajectory, warped map elements keep lanes and crosswalks accurately registered with updated data.