Instruments For Road Network Navigation TRIZ Cases

Infrared ranging detects stage edges and floor patterns to keep moving apparatus self-location accurate despite lighting, cables, and EM noise.

A UAV tracks vehicle pose, then leaves its set flight path to image obstacles and extend driver visibility on roads or trails.

A two-stage route and track search cuts computation while producing continuous obstacle-avoiding vehicle tracks for accurate target arrival.

Targeted route requests and user-uploaded dashcam footage keep low-traffic road section videos current for more reliable navigation.

On-board sensors and HD map objects are matched to refine coarse vehicle position while reducing localization workload and latency.

Multi-factor route and schedule evaluation cuts energy use, CO2 emissions, and travel cost in vehicle dispatch planning.

Precomputed forward and reverse route segments help trucks and trailers maneuver in narrow areas while reducing collision risk and energy use.

Coordinates a second mobile retail vehicle from arrival-time data so it can reach the same point promptly with less wasted travel and lower routing cost.

A dynamic turn-cost model adds delay penalties for crossing oncoming traffic, balancing safer routing with shorter delivery times.

Selectable passage width options let autonomous vehicles avoid overly narrow routes, balancing route flexibility with safer travel.

Selective state-information requests keep autonomous-driving maps current while avoiding constant vehicle sensor uploads and heavy communication load.

Real-time emissions data and predictive routing help MaaS fleets stay within legal limits without disrupting vehicle availability.

Map data is converted into a directed graph to plan complete fingerprint survey routes with less manual effort and route rework.

Automated steering runs a machine along a calibration path to capture pitch and roll data, correct sensor bias, and improve GPS accuracy.

Switching between network-planned and local paths keeps navigation available in low-connectivity zones while reducing onboard memory and compute strain.

A reversible vehicle control scheme selects travel direction so priority seating aligns with the better landscape side on a route.

Automated post-accident handling uses event and nearby device data to tailor route changes, evidence capture, and location sharing.

Image-based feature extraction and tagging match vacant properties to user preferences, reducing manual search time and site visits.

Localized link updates correct misaligned multi-vehicle sensor data, improving map accuracy while limiting re-alignment time and compute use.

Passenger walking distance and road-edge data are turned into map scores that help autonomous vehicles choose safer, more convenient stops.

Passenger approach distance and road-edge proximity are combined to map safer, more convenient autonomous vehicle pickup and drop-off points.

Displays expected work distance and time with field shape and headland settings, helping tractor operators validate routes faster.

Field control zones combine predictive maps with sensed conditions to adjust machine actuators in real time and improve harvesting accuracy.

Shared drone and environmental data is converted into simplified flight area safety output, making hazard checks easier than raw 3D wind maps.

By making the IMU the trusted primary sensor and using GPS and perception as corrections, this case improves AV localization reliability and latency.

Multispectral thermal imaging combines spectral channels with position and orientation data to detect projected-course obstacles in low-contrast conditions.

Detailed map scoring suggests safer pickup and drop-off points near a requested location, balancing passenger access and vehicle reachability.

Location-based objective assignment lets autonomous vehicles collect map data during service or idle time while the fleet tracks scouting progress.

When onboard sensing fails, the vehicle uses V2V data from a nearby target vehicle to follow a parking path and continue travel safely.

Machine learning and validation engines generate vehicle paths that cut emissions in dense zones without sacrificing safety or efficiency.

Geometric feature matching replaces dense 3D point comparisons to speed mobile robot self-localization while preserving map coincidence accuracy.

Multiple drone scans classify semi-static and dynamic objects to build maps that keep autonomous vehicles on available driving paths.

Tree trunk matching enables reliable autonomous steering in orchards and vineyards where GNSS is blocked and leaf patterns change seasonally.

Tolerance filtering turns general lattice plans and actual operator routes into specific autonomous vehicle paths with better traversal efficiency.

Distributed nodes analyze mobile sensor streams for trip detection and user records while reducing resource load and configuration overhead.

Automated steering repeats the same terrain in both directions to measure pitch and roll bias and improve GNSS position accuracy.

Cluster-based planning groups vehicles by motion constraints and shared resources to cut search complexity and reduce coordination conflicts.

External information and map data are used to detect event areas, identify affected robots, and recommend new routes with less operator effort.

Visible encoded patterns in vehicle image frames let remote terminals verify latency, data integrity, and rendering for safer remote operation.

Sensor-built 3D maps locate items without tags and plan obstacle-aware routes to improve tracking precision in complex spaces.

Pedestrian density, attributes, and movement direction are used to plan routes that reduce stops, lower load, and protect battery health.

Route deviation and stop-state monitoring enable rapid retrieval of personal mobility vehicles when unmanned or manned travel cannot continue.

Regularly spaced roadside elements let vehicle cameras estimate object distance, speed, and trajectory more accurately, even for small targets.

Perception-based map data scores walking distance and road-edge proximity to choose more convenient autonomous vehicle pickup and drop-off points.

Dispatchers use driver log time budgets and reachable-area boundaries to assign trips more accurately and avoid hours-of-service violations.

Preset sounds, messages, or stopping actions at route endpoints help work vehicles shift from autonomous to manual travel with less delay and missed switching.

Embedded motifs and watermarks let one DNN detect hardware or software faults in real time without running multiple network instances.

Robots reorder dock pallets when vehicle ETAs change, cutting wait times and improving warehouse loading throughput.

Approximate GNSS and odometry pose is refined along mapped road trajectories to cut localization computation while preserving accuracy.

Battery-aware routing selects fallback retrieval points and arrival times when a small electric vehicle may fail to return autonomously.