Instruments For Road Network Navigation TRIZ Cases

Synchronizing driver behavior and vehicle data enables replayable cause analysis of abnormal driving events such as drowsiness.

By combining battery charge, GPS, weather, and traffic forecasts, this case improves EV range prediction and route updates.

Segmented map data and dynamic acquisition keep automatic driving available when road information is incomplete or communication is limited.

Cost-based node pairing helps autonomous vehicles choose lane change points in real time and avoid routing loops from missed transitions.

Distributed in-vehicle and remote AI cuts sensor-data latency and stabilizes teleoperation links for safer autonomous driving handover.

Multi-camera traffic light detection and sparse map navigation cut data load while improving autonomous vehicle decisions at red-light timing.

Compares time-series positions and headings to permit interpolation only within distance and direction ranges, improving route accuracy for vehicle control.

Coordinates platoon routes with vehicle charge levels and station stops to cut charging cost and driving time during autonomous travel.

Sensors adjust projected image brightness and position against headlight and ambient light changes to keep road markings visible.

Wired roadside modules and contact-line coordinates locate a vehicle's mass center in real time without GNSS, even in tunnels or dense urban roads.

Sensor-based scene and intent displays show passengers which objects matter and what the autonomous vehicle plans to do next.

When a vehicle blocks a following car, assistance adapts to driver awareness and road width to avoid needless alerts and support safe pull-over.

Route-aware track generation uses vehicle, traffic, and deceleration-point data to cut energy use and improve urban driving safety.

Multiple curb, traffic, legal, and feedback inputs are combined into a pullover quality score to guide safer, more convenient AV stops.

Authorized gestures, voice, and touch inputs guide autonomous vehicles to exact stopping points when mapping is limited.

Aggregated trip and service data set driver-specific baselines, flagging real-time deviations to recommend breaks and improve fleet safety.

Portable terminal alerts prompt drivers to resume manual control as automated driving nears its termination point, improving handover awareness.

Fused map and sensor data help cruise control assess lane changes around slower vehicles while maintaining set speed and reducing collision risk.

A computing-controlled movable partition wall lets autonomous vehicles reconfigure cabin space for passengers or cargo without depot visits.

Onboard optical neural processing turns LiDAR signals into actuation commands for safe real-time vehicle merging without external connectivity.

AR overlays emergency vehicle position and obscured hazard cues in the driver's view to cut distraction and improve response in low visibility.

Vehicle data from multiple cars is fused with a road condition model to recognize hazardous road areas earlier and warn planned routes in time.

Balances travel time and charger availability by updating EV routes from battery state, user preferences, and live station reservations.

Pre-registered and verified outlet data helps users quickly find compatible EV charging points without manual searching or uncertain availability.

Linear temporal logic lets autonomous vehicles score motion segments against operating constraints, cutting route computation cost in real time.

Vehicles detect construction objects, map zone boundaries, and classify active or inactive work areas to avoid unnecessary manual takeover.

Personalized charging station recommendations combine user, vehicle, and road data to reduce wait risk and improve station availability.

Sensor-based crossing logic identifies mandatory-stop vehicles at railroad crossings, helping autonomous vehicles wait, reroute, or proceed safely.

Filtered maneuvering options cut random trajectory generation, helping autonomous vehicles avoid collisions and reduce uncomfortable maneuvers.

Switching between synchronized and independent display regions preserves a source image and improves in-vehicle viewing convenience.

Early support contact stiffens the tilting display mechanism against vehicle vibration, reducing arm bending and abnormal noise.

A separate ECU adds vehicle AR-style display functions using camera, GPS, and driving data without changing infotainment or cluster software.

Digital maps assess landmark coverage on straight and curved road sections to enable automated driving with accurate localization and speed limits.

Camera-based action screening compares stopping distance with next-state spacing to keep autonomous navigation safe and liability-aware.

Fusing lane markings, road curvature, and vehicle motion data improves lane keeping when markings disappear at intersections.

When a stop condition is missed in the recognizable range, the controller shifts to a calculated braking speed to avoid sudden deceleration and stop safely.

Map matching and link connection analysis infer pedestrian passage records from sparse location data, reducing terminal power use.

Stores only high-feature surrounding environment data during travel to cut memory size and cost while preserving vehicle guidance accuracy.

An agenda model combines appointments, route data, and battery state to place charging stops with less planning effort and waiting time.

Map-based vectors and Mahalanobis distance help filter false lane boundaries and improve vehicle positioning in complex driving scenes.

A communication band map lets vehicles predict link quality by location and adjust route, speed, or mode to avoid unsafe communication loss.

Road magnetic markers provide stable azimuth references to correct gyro drift from temperature and vibration in vehicle navigation.

Vehicle sensor and driver assistance data are compared with mapped position to detect outdated road features and avoid unnecessary map updates.

When an automated vehicle leaves the planned route, guidance is paused until the next waypoint to avoid confusing map-display discrepancies.

Vehicle-mounted sensors and ML track users between the autonomous vehicle and destination, adding threat detection and real-time status updates.

Camera-based motion estimation predicts wheel rotation, then compares it with sensor readings to detect slip and trigger navigation responses.

Real-time behavior assessment and map-aware tracking improve pedestrian intent prediction, helping autonomous vehicles avoid unnecessary braking.

Sensor-based frustration detection triggers autonomous driving on demanding road segments to reduce risky behavior and improve driver safety.

ANN-based resource prediction switches autonomous driving apps to lower-load modes when route and location data indicate rising vehicle resource demand.