Instruments For Road Network Navigation TRIZ Cases

Map acquisition status is checked before and during planning so autonomous driving can adjust control behavior and make interruptions perceptible.

Speed threshold warnings keep sensor detection and host vehicle route estimation accurate during surrounding environment data storage.

Biometric sensing and road-context recognition tailor driver alerts to physical condition, reducing overload while improving traffic safety.

Cluster EV charging points by usage patterns to predict availability with lower computing load, shorter waits, and better load distribution.

Facility-linked character and guidance output fills map coverage gaps, helping vehicles deliver route, location, and ad information inside facilities.

Onboard cameras and neural networks build a runtime BEV costmap for precise autonomous path planning without GPS or LiDAR.

Combining biometric signals with driving behavior enables earlier risk prediction and route guidance to mitigate high-risk driving situations.

Combining camera images with lidar pointclouds helps autonomous vehicle guidance adapt to new environments and unexpected obstacles.

Phase differences across multiple SPR receive elements extend radar coverage beyond the antenna footprint for more efficient vehicle localization.

Map-based lane line fusion corrects false detections and restores missing markings when real-time sensing fails in complex driving scenes.

Fleet sensor data and map attributes improve intersection turn prediction for driver assistance without relying on user-specific data.

Landmark-based object positioning lets vehicles share reliable control references despite outdated HD maps and map mismatches.

Satellite weather data is refined with vehicle travel-state sensing to estimate real road weather and surface conditions more accurately.

Synchronized sensors on pivotably coupled vehicle units are fused with relative position and pivot angle data to build a more accurate environmental map.

A route-based charging plan selects stops by low-carbon power ratio, reducing user planning burden while increasing renewable energy use.

Automated request matching secures preferred conveyance services in real time, improving vehicle utilization and reducing manual evaluation delays.

Different camera readout rates detect flickering lights for navigation while limiting image data load and preserving display output.

Preloaded map tiles let fleet systems compare vehicle speed with local limits and issue timely alerts without image-recognition latency.

Sensor-detected closed lane nodes are pruned from a lane graph so vehicles can reroute faster and avoid blocked segments.

Stored object references and a high-precision anchor position help parking assistance determine bay proximity when self-position accuracy drops.

Routes EVs to alternate charging stations using shared preference and availability data, with secure authorization via blockchain.

Multi-timescale driver assistance compares driving actions with behavior models to detect repeated errors and trigger alerts or corrective action.

Dynamic route control resets destinations and meeting plans based on reachability, driving conditions, and participant arrival timing.

Exterior cameras estimate flooded-road depth from nearby objects, then alert drivers or block unsafe vehicle movement.

When mute lines disconnect or ground during emergency calls, this case shows how user prompts silence speakers while keeping navigation display active.

Live camera images are matched to stored reference views to score route safety and generate control and acceleration values in changing environments.

Heat sensed on the road reveals recently traveled vehicle tracks, helping navigation choose reliable paths when maps or sensors conflict.

Historical travel patterns and user preferences are used to predict destinations and recommend charging stops that reduce time loss.

Predictive field routing uses harvest productivity and transport availability to cut harvester wait time and improve throughput.

Historical travel patterns, parking locations, and energy needs are used to schedule EV charging around departures, rates, and renewable availability.

Curated map features help infer building access points near a requested location, improving autonomous vehicle pickup and drop-off accuracy.

Indoor valet parking uses particle-filter positioning and mapped walls, pillars, signs, and floor markings to guide autonomous parking and retrieval.

Compares onboard traffic sensing with swarm data, then switches modes to avoid outdated road information in assisted driving.

GPS, map, ADAS, and road grade data guide regenerative braking and route choice to improve EV energy recovery and drivability.

When satellite signals fade, ADAS localizes the vehicle from static objects, lane curvature, and wheel travel to maintain precise control.

Clustered route and stop points are converted into serviceable area boundaries to improve robotic fleet permissions, routing, and reassignment.

Multiple vehicle priority schemes are evaluated to improve collaborative trajectory quality while cutting computation time and planning load.

Notice areas placed before relevant POIs let vehicles plan control actions early while cutting map communication load and avoiding irrelevant responses.

Camera analysis of pedestrian gaze at crosswalks helps autonomous vehicles choose navigational actions that improve safety and response accuracy.

Projects speed, steering, and navigation cues onto the road by integrating image projection with the headlight assembly for clearer driving-state display.

Predicts charger availability from regular charging behavior and navigation data to cut queuing time and improve station allocation.

Dynamic localization parameter switching uses environmental recognition and sensor fusion to keep mobile body positioning accurate across changing conditions.

Upcoming speed changes and route cues are shown through visual, audio, or haptic feedback to reduce driver disorientation during assisted driving.

Obstacle-avoiding turn paths are generated to match vehicle orientation at entry and exit, reducing sudden speed changes and lateral acceleration.

Probe and sensor trajectories are split and classified to detect lane-level slowdown events quickly and accurately for current traffic alerts.

A software app matches EVs with mobile charging vehicles, setting route-based meeting points and times to avoid detours and battery depletion.

GUI-based rider guidance helps autonomous vehicles handle precise pickup and drop-off selection without a human driver.

Synchronized multi-axis magnetic signals help vehicles detect road markers more reliably despite interference from nearby passing vehicles.

Calculates V2L energy use against route-to-charger needs, showing external device runtime without risking loss of driving range.

Predicts vehicle paths by combining map context, future location distributions, and surrounding-vehicle interaction features for higher driving precision.

Client device authentication helps an autonomous vehicle confirm the assigned rider and reroute when the wrong passenger enters.

Sensor-based parking offset adapts to occupied seats, lane markings, and nearby objects to improve passenger entry and exit space.

A drag-and-drop AR map lets users correct POI arrival points, improving parking lot and entrance guidance without complex map updates.

Predicted energy overlays on planned and deviated vehicle routes help drivers visualize remaining battery or fuel before detours.

Wide-lane route control creates virtual lane center lines so autonomous vehicles can pass safely in parallel without stopping traffic.

Deviation possibility scoring predicts likely route errors, distinguishes driver intent, and triggers timely warnings or recalculation.

Predicted routes and sensor FOV data reveal blind areas ahead, then AR visualization helps drivers anticipate reduced visibility during AV navigation.

Historical terrain data and cloud inference generate last-10-feet routes for autonomous delivery without heavy real-time sensor processing.

Black box image data and 3D driving simulations quantify braking and steering effects to calculate accident fault ratios more objectively.

Sensors adjust road image projection to ambient brightness and vehicle motion, keeping markings visible despite headlights and changing light.

FFT-based route records link multi-sensor frequency patterns to road segments, improving heavy-duty EV control accuracy and route reliability.

Uses vehicle attributes, entry-exit lanes, and target positions to set stable intersection routes for safer, more efficient navigation.

Aerial images, ML bounding boxes, and homography transforms help vehicles locate and read obscured traffic signals at intersections.

Roadside units detect objects and share processed road data to back up onboard sensing, improving autonomous driving robustness without full in-vehicle redundancy.

RTK-corrected GPS and continuous wheel rotation keep power equipment on tight parallel turf paths while avoiding divots during turns.

Fuzzy-logic EV routing weighs battery state, charging stops, traffic, and travel time to guide low-charge vehicles in real time.

Destination-based control selects a platoon and joining point, enabling lane changes and mid-route entry without disrupting spacing.

Routes are scored by wireless coverage and bandwidth so driving assistance features stay available while reducing rerouting, time, and energy waste.

Candidate routes are scored by predicted ESS state before descents, helping fuel cell trucks avoid battery saturation during regenerative braking.

Offline context compensation and online feedback correction improve autonomous vehicle MPC accuracy under model uncertainty and nonlinearity.

IMU-based fleet telemetry maps recurring hazardous driving zones, enabling dynamic geofence updates and safer utility vehicle operation.

When nearby vehicles request conflicting lane changes, route arbitration assigns priority and avoidance travel areas to prevent overlap and keep traffic smooth.

When a transport sensor malfunctions, the control logic lowers autonomy and limits operation by fault severity to maintain safe travel.

A mobile charging vehicle calculates and delivers just enough roadside power for a low-battery EV to safely reach a charging station.

Passenger stop requests from a mobile device are synced with vehicle routing to swap charging stops and keep EV battery capacity sufficient.

Weather-based dispatch shifts pickup or drop-off points and adjusts cabin conditions to improve autonomous ride comfort in adverse conditions.

A cancel switch blocks door unlock or opening before full opening, letting a rider stop shared boarding after visual assessment.

Audible cues from vehicle speakers help visually impaired passengers locate an autonomous pickup vehicle more safely and with less waiting.

Vehicle-state sound combines moving and trigger audio cues to reflect driving conditions more naturally and reduce EV driver discomfort.

A management server assigns routes and takeover positions across routine-run areas to keep enough leading vehicles at intersections and avoid platoon splits.

Compares detected and mapped lane lines to suppress false lane-deviation support when map alignment is poor, improving control reliability.

Audio fingerprinting links in-car ad playback to geographic locations, enabling timely navigation prompts without relying only on broadcast data.

Direct-route charging station pre-selection improves EV route reliability, preserves charge sufficiency, and helps reduce station waiting time.

Vehicle and user data are used to predict likely in-car interface actions, cutting menu steps and reducing driver distraction.

A visor-mounted reflective panel assembly keeps navigation imagery in the driver's line of sight without requiring a dashboard glance.

Fusing lane curvature, heading angle, and travel distance helps ADAS maintain accurate vehicle localization when satellite signals are weak or lost.

Folding the HUD optical path and adjusting mirror-to-panel distance keeps virtual image magnification uniform while reducing distortion and size.

Polygon area comparison checks whether a vehicle stays within a route boundary in real time, avoiding false positives and heavy processing.

Determines response times for vehicle map errors using encounter rates and detector precision to trigger timely mitigation and safer autonomous driving.

Route-specific speed profiles and deceleration points improve fuel estimation accuracy and cut vehicle energy use across diverse routes.

Camera-map matching corrects wheel speed and yaw rate sensor errors, improving vehicle location estimation without extra sensors.

Vehicle telemetry and machine learning replace aerial imagery to generate lane lines and road edges faster for autonomous map updates.

Local in-vehicle suggestion generation stores personalized prompts by context to cut latency, network use, and user data exposure.

Charging events are clustered by geolocation and frequency to identify primary and routine EV charging sites for better infrastructure planning.

Road risk and vehicle distance guide where map interpolation is applied, cutting storage and power use without hurting near-field localization.

Sensor-based turn-region mapping guides long and articulated vehicles through tight turns while avoiding obstacles, lane deviation, and congestion.

Sensor and device inputs automate personal mobility vehicle status updates, cutting transfer delays and improving allocation accuracy.

Measured driving data enables usage-based insurance pricing and compares trip costs with alternative transit routes in real time.

Beacon signals from street lighting devices guide drivers to available parking spaces, reducing time spent searching and minimizing driver distraction.

A vehicle position determiner corrects satellite navigation data using roadside error vectors and vision detection.