Instruments For Road Network Navigation TRIZ Cases

See how a trip planning system reduces EV energy costs and emissions by pre-conditioning batter

See how seat reclining control integrates sleep detection with vehicle interior state monitorin

See how an IoT system uses sensor networks and image recognition to identify high-impact garbag

See how a navigation terminal sets destinations by receiving GPS position data via multimedia m

See how IoT sensors monitor road garbage accumulation and dynamically adjust cleaning routes to

See how sensor-based deposition restriction and automatic vehicle movement to cleaning location

See how a vehicle cooling box acquires target temperature values from a server before item plac

See how multimedia messages carrying position data enable navigation destination setting withou

See how a display rack sensor system separates pattern detection from weight measurement to mai

See how a control device calculates and transmits in-vehicle air conditioning preferences to de

See how an active vent system with air channels and thermal decoupling maintains optimal cookin

See how dynamic grid translation balances resolution and storage, enabling robots to find paths

See how an autonomous delivery vehicle uses automatic compartment dispensing and secure locking

See how a navigation terminal extracts destination coordinates from received messages to enable

See how navigation terminals extract location data from received messages to set destinations w

See how a vehicle cooling box acquires target temperature values from server or deliverer termi

See how targeted vibration to latissimus dorsi and gluteus medius tendon portions eliminates dr

See how an autonomous robot system with mobility, communication, and interaction subsystems ass

An arc-shaped slider guided by a curved support lets an insertion-aperture cover rotate smoothly with a smaller gap and better protection.

Selected objects are reordered by category, location, and timing to present route-optimized UI displays with less random searching.

Shared location messages let a mobile terminal set route coordinates directly, avoiding address lookup for highway points or mountain slopes.

Onboard sensors detect particulates and trigger cleaning elements or alerts to keep autonomous vehicle interiors clean without a driver.

ETA-linked timers trigger alerts or destination climate actions before arrival, turning passive navigation estimates into proactive automation.

Targeted seat vibration at latissimus dorsi and gluteus medius tendons improves driver awakening more effectively than general seat stimuli.

Dead reckoning with wheel rotation and heading sensing tracks wheeled objects without GPS or RF infrastructure, improving position reliability.

Precomputed speed, steering, and braking profiles cut route-specific vehicle energy use and help restore efficient motion after unexpected stops.

Road and sensor compatibility tables let the vehicle choose feasible autonomous driving functions and automation levels for each road section.

Pre-learned parking paths and scenario-aware interface prompts guide autonomous driving from lot entry to the target space with safer parking.

Sampling both sides of obstacle contours simplifies search space and yields smooth, collision-free paths in narrow lanes with multiple obstacles.

Dynamic 3D audio cues shift from the user toward the target direction, improving route understanding while reducing visual distraction.

Sensor-derived occupant sentiment guides route generation so navigation can better match driver condition, passenger needs, and safety constraints.

Predicted sensor visual range is fed into occupancy forecasting so ego vehicle maneuvers stay reliable despite occlusions and unseen areas.

A server matches low-charge portable power users with nearby surplus batteries using status and location data to keep appliances running off-grid.

Continuous-curvature turn paths use clothoid-based planning and curvature estimation to shorten narrow U-turn maneuvers without stopping.

A navigation server predicts nearby vehicles' next moves and shares only relevant action cues, reducing reliance on turn signals alone.

Shared warning data from a target vehicle lets the host adjust safety envelopes and respond to recognition gaps that can cause accidents.

A schematic route display keeps charging facilities in fixed proximity order, improving visibility over moving map overlays.

GPS, accelerometer, and gyroscope data are combined to map stop locations, detect rolling stops, and warn drivers about risky intersections.

Real-time vehicle status data allocates spare onboard computing power to external tasks without impairing safe vehicle operation.

Quadratic programming in Frenet coordinates improves obstacle-avoidance path smoothness, safety, and real-time planning quality.

A layered ADAS data protocol sends position data cyclically and requests map attributes on demand to cut bandwidth waste and support dynamic updates.

Compares live vehicle measurements with a motion model to reject skid- or slip-driven outliers and keep control decisions reliable.

A smart device and server offload battery and route analysis, giving two-wheel EV riders real-time trip, navigation, and parking guidance.

Weighted map and sensor checks confirm road class before autonomous mode switching, reducing risk from inaccurate satellite positioning.

Location-triggered tour content lets the vehicle infotainment system guide routes and adjust settings without hiring a tour guide.

Candidate basis paths and merge points help autonomous vehicles handle lane changes, drivability limits, and obstacle-aware motion planning.

Sensors detect cargo shift in transit, then the vehicle adjusts route and driving controls to prevent further movement and reduce manual checks.

Real-time traffic and vehicle geolocation guide neural road segment selection to maximize travel within ODD requirements.

Dynamic cost maps update road-center, edge, and occupied grid cells from LIDAR and radar data to keep autonomous vehicles clear of obstacles.

Adjustable spacing between paired magnetic sensors offsets vehicle mounting height variation for more reliable road marker detection.

Preloading analyzed data to adjacent city nodes cuts V2I transfer delays and keeps vehicle communication continuous in congested urban routes.

Manual driving data is averaged into nominal paths that guide AV planning through curved or complex routes with smoother, more human-like maneuvers.

Cloud-based comparison of live and crowdsourced object detections flags sensor mismatches and triggers safer autonomous driving responses.

Segmented route guidance uses traffic, signals, curvature, and gradient to cut stops and improve vehicle fuel efficiency.

Calculates only the charge needed to reach the next station, cutting EV stop time while keeping battery capacity above a safe arrival threshold.

When a guide-line start position is occupied, target start points are recalculated from projected mileage to keep unmanned vehicles moving.

Complex convex corridors expand feasible trajectory search space for autonomous vehicles while cutting planning cost, jerk, and power use.

Integrated reliability from multiple lane detectors improves traveling lane estimation when individual sensor accuracy drops.

AI tailors sign-linked AR content to occupant preferences, improving route relevance and ad engagement without fully generic displays.

Distance from a road reference point lets a vehicle match detected magnetic markers to database positions for precise lane guidance.

Multi-stage localization guides an autonomous vehicle to a charging station with precise alignment, reducing human intervention and cycle time.

Recognition-area overlays show what blind spots were actually searched and suppress misleading alerts when merge-entry risk is predicted.

Multi-source navigation data is filtered through constraint checks and selective object verification to improve autonomous driving accuracy and safety.

A segmented min-max range display shows whether a destination is reachable, when energy-saving driving is needed, and when charging stops may be required.

Compares map, vehicle behavior, and sensor road data to vote on accuracy, correct errors, and improve road recognition reliability.

Surplus PV power is matched to EV charging stations using vehicle state and route guidance to reduce curtailment and support grid stability.

Switching between route planning modes helps AVP vehicles handle narrow passages, sharp turns, and obstacles to improve parking success.

Driver inputs are combined with HD map guidance to enable lane switching, intersection maneuvers, and real-time route adjustment.

Visual readiness cues on an in-vehicle display show when automated driving conditions are met, reducing driver confusion and safety risk.

Obstacle projection and depth-based curve generation mark U-turn lane lines that follow vehicle paths and avoid curbstones and isolation belts.

Landmark and sensor-based path deviation scoring captures vehicle movement between alerts for more precise driver characterization.

Pre-stored reverse route patterns by trailer wheelbase cut computation while enabling collision-free coupled vehicle parking.

Sensor-based topographical data and V2V guidance help vehicles find or create passing space on narrow unpaved roads with incomplete maps.

Using existing vehicle sensors, this case estimates rider load against a user profile to detect tandem riding without added hardware.

Destination-based charging limits reserve road wireless power for vehicles headed to power shortage areas, improving emergency power delivery.

Adaptive EV route planning combines operating conditions and charger selection to improve range prediction and reduce range anxiety.

Compares map-based and sensor-based 3D positions to detect abnormalities while enabling random access and lower transmission volume.

A 60 GHz point-to-point link at the charging port moves autonomous vehicle data during charging, cutting transfer time and cable complexity.

Historical and real-time lighting maps help autonomous vehicles choose well-lit pickup and drop-off stops for safer, more comfortable rides.

Telemetric route coverage evaluation identifies missed path segments and updates test runs to improve autonomous vehicle training data quality.

Neural networks detect wheel slip and reject bad GNSS updates, improving vehicle localization in off-road and low-signal conditions.

Feature point tracking maintains inter-vehicle distance measurement when close-range images miss the front vehicle's lower portion.

Multiple weighted filter hypotheses fuse GNSS and camera data to adapt measurement noise for accurate vehicle positioning and map updates.

A two-level lane scoring approach combines route graphs and real-time traffic data to cut unnecessary lane changes in self-driving cars.

Variable grid resolution cuts graph-search cost in autonomous parking while preserving precise collision avoidance near the vehicle.

Different route guidance modes notify passengers before autonomous U-turns, reducing anxiety caused by abrupt direction changes.

GPS and IMU path data are calibrated against remote reference points to estimate vehicle state and terrain height for fleet monitoring.

Customer-configurable vehicle data requests use mobile linking and blockchain authorization to protect privacy while displaying selected in-vehicle data.

Real-time lane overlays on e-mirror displays help drivers distinguish their current lane from adjacent lanes during weather, fatigue, and lane changes.

Trajectory query points replace full occupancy grids, cutting computation while improving real-time collision avoidance in dynamic environments.

Standardized sensor units are oriented and synchronized across vehicle mounting positions to improve field coverage and data reliability.

Multi-sensor vehicle monitoring uses external context to adjust detection thresholds in real time for faster alerts and more accurate event records.

Static feature measurements fused with GPS and camera data improve lane-level vehicle positioning when satellite geolocation is too coarse.

Fusing current sensor-based drivable areas with historical map data improves route planning accuracy while keeping automated driving reliable.

V2X-based vehicle classification adjusts inter-vehicle distance by driving capability to keep mixed traffic safe and flowing efficiently.

Map-based obstacle data beyond sensor range lets the vehicle preplan avoidance paths that reduce sudden turns, braking, and passenger burden.

Telematics-driven battery monitoring predicts EV failure events and dispatches autonomous assistance with routes that reduce battery strain.

Adaptive lane-cell modeling adds enriched occupancy states and confidence-based discretization to improve medium-term vehicle tactical decisions.

Dangerous route points are detected from surrounding data and added to path information so autonomous vehicles can slow down before accidents.

When a lead vehicle changes lanes, target curvature is reduced at short longitudinal distance to limit steering angle and collision risk.

GPS comparison between rider devices and vehicle location confirms the assigned passenger and reroutes the autonomous vehicle when boarding is wrong.

Probabilistic parking route ranking combines real-time and user data to guide drivers toward spaces more likely to be vacant near a destination.

Roadside sensor pods predict and compare sensor sequences to spot rare road changes and guide vehicles when onboard perception misses them.

Retroreflective road features are vectorized into digital maps to localize vehicles accurately with lower computational load in complex urban settings.

Forecasted rainfall and road data are used to predict puddles, reroute vehicles, and adjust speed to reduce splash and hydroplaning risk.

When camera and map lane lines disagree, this case switches driving modes using distance checks to keep vehicle control aligned to the actual lane center.

Proactive EV charging recommendations use battery, driver, and trip data to avoid unplanned stops and keep travel on schedule.

A learning agent plans multi-service EV deliveries with charging, battery swapping, and V2G stops to cut trip cost and delivery time.

A mode-specific detection reference lets forward collision-avoidance assist be validated under low-risk driving conditions without actual collisions.

Geometric go and no-go zones bridge neural behavior planning and trajectory generation for safe, feasible autonomous driving.

Weighted front and peripheral sensor feature groups improve vehicle position estimation when image matching conditions vary with speed and steering.

Transition probabilities in a traffic scene graph are tuned to the navigation destination, improving autonomous trajectory planning and compliance.

Real-time graft path optimization helps autonomous vehicles rejoin a target path while avoiding obstacles and respecting speed and steering limits.

Dock and ship map-based path planning lets autonomous vehicles self-load onto ships, cutting manual handling, loading time, and placement errors.

Dynamic acceleration envelopes use friction, road grade, wind, and vehicle state to keep autonomous driving stable and feasible.

3D map look-ahead data and IMU signals are combined to estimate road grade early, reducing lag during adaptive cruise control transitions.

ML links map objects to robot route failures, helping choose robot types and update routes to cut delays, energy use, and damage.

Line objects such as curbs or walls let ultrasonic SLAM correct odometry drift and improve vehicle positioning accuracy during parking.

Route-based power planning triggers standby charging before battery depletion, helping fuel cell work vehicles preserve battery and hydrogen reserves.

Sensor and work-attachment data classify off-road obstacles by required traction, keeping route maps current and vehicles out of impassable terrain.

Monitored localization metrics detect position and orientation errors early, enabling safer autonomous driving without immediate shutdowns.

Classifying indoor vehicle states into entry or exit screens cuts display clutter and helps drivers identify relevant guidance faster.

Preloaded route-based city nodes and nearby vehicles split data delivery to cut latency while preserving transfer completeness.

Vehicle sensors detect rough lane areas ahead and bias LKAS toward smoother path zones to cut noise, vibration, and ride instability.

Charging prices are shown directly on map-based power facility locations, helping users compare options before charging.

AI combines occupant, health, and environment data to predict unsafe driving and switch from occupant assist to driving assist in time.

Predictive route control flags communication dead zones and recommends power-saving modes to preserve EV range before charging access is lost.

Geometric intersection checks replace nearest-neighbor mapping to assign tunnel and speed-limit attributes accurately on curved road reference lines.

Travel schedules are used to predict grid balancing needs, prompting EV connection or alternative travel with rewards to keep power supply and demand stable.

A map matching unit bridges incompatible SD and HD map formats so autonomous vehicles can keep routes aligned and reroute accurately.

Routing logic adds user event requests to autonomous vehicle paths, balancing direct travel with personalized stops and preferences.

Node-link map routing and transit pass control help autonomous mobility devices avoid pedestrian collisions in crowded walkways.

Route assignment based on fuel cell deterioration and road load helps equalize fleet wear and simplify maintenance scheduling.

Route-linked software search, training, and distribution cuts autonomous driving update time and computing load while preserving broad route coverage.

Virtual connection paths between intersection entry and exit nodes prevent overlap and preserve lateral distance for smoother multi-vehicle travel.

Static-object sensing and heading or curvature data keep ADAS vehicle localization accurate when satellite navigation signals are weak or lost.

Digital twin route evaluation uses vehicle load, configuration, and map data to choose lower-energy missions for trucks and buses.

Road- and lane-level map selection helps autonomous driving maintain accurate guidance and avoid control disruption during route deviations.

Digital twin prediction and IoT sensing trigger venturi air cooling before tire and brake overheating, reducing blowout and fade risk.

Area-type cost weighting guides parking paths away from grass, curbs, and pits while reducing gear shifts, steering difficulty, and collision risk.

Vehicle sensor data is compared with map data to find disparity causes and trigger map or sensor configuration updates for reliable navigation.

Offline path libraries and online sensor updates help tractor-trailer AVs avoid moving obstacles with faster, smoother path planning.

Reinforcement learning builds and scores parking path segments to cut onboard computation while keeping maneuvers smooth and responsive.

Route-based braking prediction estimates brake wear emissions and guides lower-braking navigation without added brake hardware costs.

Aggregated trip, service, and sensor data reveal fleet-specific driver fatigue and trigger timely alerts or break recommendations.

High-frequency torque and angle summation improves vehicle carbon emission accuracy while cutting cloud bandwidth and counterfeiting risk.

Risk maps combine free-space and actuation uncertainty to rank candidate vehicle paths and improve autonomous driving safety with less manual tuning.

Aggregated fleet data links software-environment interactions to adverse AV outcomes, enabling trip-specific risk checks and remedial actions.

Simulation-guided route control and retractable wheels let a rover mimic mobile actors for SDS testing with less manual control and damage.

Levy flight sampling boosts point density in narrow passages, helping path planning handle constrained, high-dimensional spaces with low overhead.

Real-time driver color profiling turns AI-based behavior assessment into simple occupant alerts, improving trip readiness and safety.

Multiple candidate dispatch routes are ranked by overlap and sensor-detected avoidance points to keep autonomous vehicle pickup arrivals on time.

Remote operators adjust map elements and suggest safe virtual paths so autonomous vehicles can handle construction zones and other dynamic conditions.

Camera neural-network output is fused with radar time-of-flight and echo-width data to improve obstacle probability mapping and drivable area recognition.

Assigned parking based on product provision time guides arriving vehicles to reduce store lot congestion and delivery delays.

Dual local and server-led control keeps a moving object operating safely during communication loss, avoiding abrupt automation interruptions.

Hybrid metric learning groups similar lane segments into protolanes, cutting autonomous vehicle test time while preserving coverage.

Only changed road features are distributed instead of full HD maps, reducing update delay and bandwidth for autonomous vehicles.

Displays road-segment verification progress so drivers can target data collection on underverified routes and speed ADS ODD expansion.

When lane markings are obscured by rain, fog, snow, or dirt, machine learning predicts lane boundaries from road features and updates them with rear camera data.

By fusing remote forecasts with onboard vehicle sensor data, this case improves real-time road and weather condition updates without constant connectivity.

Three coordinated planners speed autonomous vehicle trajectory updates around obstacles while preserving handling limits and safety.

Risk profiles and prior event patterns are used to create and deploy new fleet vehicle event definitions for more accurate detection and reporting.

A corrected parking angle is calculated from surrounding sensor data and used only when reliable, avoiding extra repositioning after entry.

Graph-based relational inference models agent interactions and reward functions to make autonomous vehicle actions easier for humans to understand.

Route-specific risk levels are combined with communication quality to avoid unnecessary deceleration while protecting autonomous driving safety.

A rotatable bin carousel with scanners and controller-assigned slots speeds parcel retrieval, cuts stops, and improves tracking.

Presents charging methods and dispatch options so drivers can choose suitable staff or mobile charging vehicles with less selection complexity.

Nearby EVs are matched by charge, location, route, and availability to deliver rescue power when a stranded vehicle cannot reach a charger.

Light-guided road charging helps drivers keep speed and position aligned with transmission coils, easing congestion and improving transfer efficiency.

Discrepancies between model and sensor values are used to correct estimation uncertainty, improving Kalman filter reliability in safety-critical state estimation.

Driving-state feedback guides EV route assignment by station spacing to cut power shortages, charging delays, and battery strain.

By fitting LIDAR lane points to perpendicular line projections, this case improves lateral vehicle localization and reduces freeway jitter.

Map-based road marking accuracy guides vehicle control toward reliable markings, improving position estimation when lidar views are blocked or markings fade.

Fleet ride-quality feedback and passenger preferences guide route changes that reduce motion sickness from turns, elevation, and speed shifts.

Historical deadlock causes and context cues are used to predict ambiguous traffic situations early, enabling smoother remote intervention.

A mothership deploys, recharges, and reroutes autonomous delivery units so item drop-off and pickup continue with lower labor cost.

Compares relative positions from multiple periphery detectors to quantify object-position reliability and improve surrounding-object detection accuracy.

Mindful driving data is used to calculate carbon offset rewards that compensate vehicle emissions while preserving transportation convenience.

Route-aware charging control adjusts current by station conditions and battery state to cut charging time, heat buildup, and cooling energy.

Reroute likelihoods for vehicle maneuvers are used to weight route ETAs, improving trip duration estimates under traffic and environmental uncertainty.

Multiple display boxes show the recommended lane and same-direction lanes together, helping drivers judge lane changes with less confusion.

High-precision map selection helps autonomous vehicles reach legal, available user-chosen parking spots with fewer delays in pick-up and drop-off.

Route segments are matched to drive modules with tailored energy parameters, enabling flexible vehicle assembly with lower energy use and less gearbox complexity.

Combining GNSS with extracted road-object laser point clouds improves vehicle positioning accuracy, speed, and success in changing environments.

A single scanned planar beam and detector array maintain LiDAR data quality at high speed while reducing beam count, power use, and complexity.

A management service syncs only relevant map partitions and bypasses conflicting changes to keep autonomous vehicle navigation current and consistent.

3D wind sections and weighted airflow data help route UAVs through urban canyons with lower energy use and safer cargo handling.

Pre-generated 3D mesh maps augment vehicle sensor data to render occluded and distant scene content with faster, more reliable AR views.