Instruments For Road Network Navigation TRIZ Cases

Precomputed local and global walkable paths cut mobile route setup time and reduce manual input errors in indoor guidance.

By matching overlapping route sections between two vehicles, this case guides or automates lead-vehicle following to reduce navigation errors.

Multiple sampled vehicle images are mapped into a reference coordinate system to build a complete junction view when maps or cameras are incomplete.

Tracks reverse maneuvers with location and task data to report backup counts by route, supporting driver training and route optimization.

SNS-linked spot data lets navigation displays request nearby vehicle images, giving faster visual updates on traffic ahead.

Multiple vehicles stitch timestamped sensor data to track object movement across blind spots and improve situational awareness.

Continuous wearable tracking compares location with a planned route and alerts authorized contacts when a person is disoriented or unable to communicate.

A corridor around a road-level route filters lane-map segments to build lane-level guidance for precise vehicle control and lane changes.

User-generated parking and entrance data is merged with trip history and courier behavior to improve last-segment delivery navigation.

Predictive parameters switch between exclusive and multi-invite modes to improve provider matching speed and fit in transport services.

Trip history, origin, time, and day are used with gradient boosted trees to predict likely vehicle destinations and reduce manual entry.

Real-time vehicle sensing and aerial imagery update static road elements to improve localization without high-definition maps.

Mobile data on location, road type, traffic, and ambient noise helps classify stopped vehicles and trigger timely roadside assistance.

Aggregated vehicle sensor and localization trails build accurate, scalable HD maps with dynamic cloud updates for ADAS and autonomous driving.

Maps vehicle trip GPS data to specific road segments with graph-based matching to separate nearby roads and derive routes, stops, and speed limits.

Predicted-versus-actual vehicle state deviations enable map updates with less stored data while improving route-relevant navigation accuracy.

Adjusts spot recommendation priority by spot type count to avoid biased results in dense areas and improve user convenience.

Distance-based icon switching replaces traffic congestion markers with parking icons near the destination to keep navigation maps readable.

Geomagnetic and gravity sensors map camera direction into the force guidance device frame for accurate 2D and 3D user guidance.

Price-time curb queues turn route waypoints into tradable capacity units, improving allocation flexibility and market transparency.

Expanded POI boundaries and historical state tracking reduce GPS drift errors in location-based recommendations while avoiding wasteful computation.

A navigation preview reveals only a complexity-based initial route segment before pickup, helping drivers plan ahead without exposing rider destinations.

By linking facility departure counts, weather, and nearby vehicle data, this model predicts road congestion several days ahead.

Selecting between image-based and structure-based map elements by time and difference metrics improves SLAM localization in changing environments.

Joint-based angle and probability correction improves VRU path prediction from map and sensor data for more reliable sidelink navigation.

Current and historic parking occupancy signals are used to predict point-of-interest wait times, helping users avoid long queues.

Estimated object points are compared with sparse HD map points to improve lane-level vehicle localization when map object data is incomplete.

Placing the inertial sensor between side-plate fixing points reduces vibration and deformation, improving vehicle position detection accuracy.

Custom submap projections centered at arbitrary global points cut cross-UTM routing errors, processing time, and resource use for AV maps.

Tracks occupant gaze with vehicle perception and maps to log roadside POIs automatically, preserving ad information without driver distraction.

Route-based reservations guide drivers to truck stops with healthy meals, exercise, and hygiene services without adding major detours.

A blockchain-backed virtual hub market enables transparent price discovery for transport seats and freight capacity while improving utilization.

Predicts whether a parking space supports vehicle fit, door opening, and post-parking occupant actions by simulating obstacles and clearances.

Vehicles generate map data locally from shared mapping software, cutting server load and bandwidth while keeping dynamic road maps consistent.

Demand-based route offsets steer travelers toward selected paths, easing congestion and improving transportation network efficiency.

Time-slot boarding and alighting data are converted into passenger flow vectors to plan vehicle paths that raise carrying capacity.

ML uses verified location data and user profiles to surface nearby travel objectives, reducing group planning overload and missed itinerary options.

Predicts illegal maneuvers in unfamiliar areas by combining driver behavior profiles, local law estimation, and real-time vehicle feedback.

Historical racing data is segmented, stitched, and smoothed to derive a feasible optimal lap path with minimal user tuning.

Satellite images and stored driver preferences are combined to rank routes by scenery, plants, and events with higher travel relevance.

Road gradient and other-vehicle speed data improve route-specific load estimates, helping drivers cut vehicle stress and maintenance.

Historical pickup point analysis narrows route choices and surfaces faster, cheaper, or shorter ride options with less user comparison.

Combining voice, touch, and steering-wheel controls, this case shows how in-car interfaces reduce driver distraction and manual input.

GNSS positioning is corrected with V2X lane-level maps and vehicle trajectory data to achieve accurate lane positioning without RTK cost.

Parallel LNS route optimization updates target-specific cost matrices to handle multi-node routes with conflicting time and cost goals.

Telemetry-driven viewport framing adjusts zoom and rotation around UI obstructions to keep key map features visible and reduce camera oscillation.

Camera-based object correlation links virtual location indicators to real landmarks, improving navigation where GPS alone lacks precision.

Consolidated status and coaching messages reduce redundant feedback processing while helping transportation providers improve service quality.

Preplanned and dynamically updated fuel stops help trucks refuel on route while reducing delay, compliance risk, and manual stop selection.

Motion trajectories on topological maps are learned with GNN and RNN models to localize objects accurately where GPS is unreliable.