Instruments For Road Network Navigation TRIZ Cases

Road-surface magnetic fingerprints are linked to map points so vehicles can estimate position accurately without more complex positioning hardware.

Zone-specific road indices are correlated with driver behavior to trigger tailored assistance, route guidance, and vehicle setting changes.

Dynamic window masking reveals relevant outside objects by vehicle position, speed, and direction to guide driver attention and limit distractions.

Multiple camera captures before and during parking let the control unit track vehicle-lot position accurately for assured autonomous parking.

Dynamic adjacency graph routing updates edge travel times with traffic data to improve vehicle ETA accuracy without excessive computation.

Wireless alerts and charger feedback let a vehicle verify actual inductive charging events for precise usage accounting and dispute handling.

Segmenting a route into adjacent polygons enables quick, accurate position checks with simpler processing hardware and fewer navigation errors.

Route segmentation and edge task offloading let vehicles meet autonomous driving thresholds without adding costly onboard sensors and processors.

Switching between GNSS relative and absolute positioning keeps vehicle location available for safer, more efficient platoon connection.

A controller detects an add-on battery, maps route charging stops, and guides dual-battery charging to extend EV range with less planning burden.

Road-grade breakpoints and segment-wise speed tuning cut vehicle energy use while preserving near-baseline travel time.

Real-time road-segment risk scoring guides lower-risk routes for semi-autonomous vehicles and supports more accurate insurance decisions.

Broadcast telematics from nearby vehicles enables real-time travel event detection and corrective alerts for safer, more efficient driving.

Selective sensor processing targets vehicle perception zones from route and motion data to cut computational load without losing critical awareness.

Overlap-aware feature map addition fuses mixed-resolution image regions in one CNN, improving ADAS object recognition without heavy runtime.

Predicted map nodes are matched with HD map nodes to improve object localization accuracy beyond sensor-only positioning limits.

A controller locates nearby EVs with available charge and coordinates rescue power transfer so stranded vehicles can reach a charging station.

Telematics exception rates and road-edge collision data are combined to model vehicle collision probability and improve driver risk scoring.

When a requested stop falls just outside the service area, the system suggests a reachable in-area pickup point and shows it on a map.

An earth-frame occupancy grid turns vehicle sensor updates into absolute object velocities, separating static and moving objects with lower algorithm complexity.

When a mapped object falls outside the HUD field of view, a substitute overlay keeps navigation cues and warnings visible to the driver.

Parking location is chosen from terminal-area conditions and battery charge to guide automated valet driving more safely and efficiently.

When the display is off, vehicle functions keep data ready while the GPU sleeps, cutting infotainment energy use without losing fast display recovery.

Available memory is used to predict remaining autonomous driving mileage, preventing log storage from failing as onboard space fills up.

Crowdsourced image-based road alignment builds sparse local maps that cut storage and processing load while improving autonomous navigation.

Road mark recognition in surround view corrects the HD map start point, enabling reliable autonomous parking without costly parking-lot infrastructure.

A vehicle sends function approval requests to a designated secondary device, protecting personal information from unauthorized access.

Task-type dispatching replaces vehicle-specific workflows, simplifying mixed unmanned fleet management and easing integration of new vehicle types.

Sensor-generated estimated maps from camera and lidar data expose outdated or inaccurate stored maps before trajectory errors create hazards.

Peer vehicles and a central repository share attributes and corrective actions to speed edge-case decisions in autonomous driving.

Predictive monitoring switches from occupant assist to driving assist before unsafe conditions, improving vehicle safety and transition timing.

Route-based passive pedal torque lets drivers override autonomous control quickly while avoiding added cruise control complexity.

Data is staged on nodes along a vehicle's predicted route to cut transfer delay and preserve complete delivery despite short communication windows.

When a vehicle is delayed, coordinated city nodes keep a real-time event stream active and hand it over by proximity to complete delivery.

Predictive monitoring switches from occupant assist to driving assist before unsafe conditions, improving safety with less distraction.

Vehicles exiting autonomous mode reveal where road conditions changed, enabling clustered map updates that refine ODD entry states.

By scoring links and intersections separately, this case shows how route stress indexing helps balance travel efficiency with lower accident risk.

By scoring links and intersections separately, this case helps navigation systems balance route efficiency with lower driving stress and accident risk.

A vehicle terminal hands off the remaining route to a carried moving object terminal, maintaining guidance through no-vehicle areas.

Sensor-driven rules let autonomous vehicles detect shoulder hazards, traffic backlogs, and lane-change limits while adapting path planning safely.

AR overlays on outdoor displays add occupant-specific safety cues, improving low-visibility driving and emergency vehicle detection.

AI-based EV route guidance weighs battery limits, charging stations, traffic, and travel time to cut energy use and range anxiety.

A relaxation coefficient adjusts vehicle speed to balance delay risk, arrival penalties, energy use, and cargo delivery cost.

Azimuth-angle and map-based lane detection identifies whether an approaching vehicle shares the host lane, improving alerts on multi-lane roads.

When autonomous vehicles face construction or blocked lanes, proposed path overlays help remote operators choose safe maneuvers quickly.

Radar and lidar on logistics vehicles build stored 3D environment maps during shipping runs, improving AV object detection in unmapped areas.

Trigger-based switching between fixed-path and free-form driving adjusts speed, detection range, and maps for safer navigation in changing zones.

Predicting log growth across route candidates lets autonomous driving preserve memory free space and avoid overflow during travel.

When onboard log storage runs low, routes with more overlap to past trips are chosen so only difference data must be stored for verification.

Map matching and odometer data refine vulnerable road user position data so nearby vehicles can predict trajectories and avoid collisions.