Instruments For Road Network Navigation TRIZ Cases

A virtual AV fleet tests routing versions and facility locations to cut energy use and trip time without slow real-world data collection.

Manual route runs build and refine confidence-scored virtual maps, enabling autonomous driving with fewer map updates and lower system complexity.

Vehicle and mobile sensor data are combined with accident and environmental context to assess driving risk in real time and guide safer routes.

Predicted vehicle dynamics drive real-time camber and caster adjustment to improve autonomous cornering stability and collision avoidance.

Observed object acceleration reveals traction on road surfaces ahead, helping autonomous vehicles adjust speed and braking before entry.

When a caution point falls outside the HUD view, the system switches from overlay to non-overlay alerts to avoid delayed driver notification.

When battery charge drops, the aircraft detects brighter locations and reroutes there to generate power and avoid altitude loss.

Side-by-side narrow and wide map views show nearby roads and the full disaster area, improving in-vehicle guidance during emergencies.

Environmental factor correction improves route energy prediction by combining speed fluctuation forecasts with section-based energy estimates.

External infrastructure position data cross-checks on-board localization to maintain accurate vehicle positioning when GNSS signals are blocked.

Movement-state detection switches navigation displays between walking and riding modes for seamless real-time guidance across mixed transport.

Thermal road-surface traces from earlier vehicles help autonomous systems choose a valid path when maps or other sensors are unreliable.

Sensor data converts driving behavior and conditions into risk unit consumption, enabling more accurate vehicle insurance coverage and feedback.

Lane-by-lane weighting uses vehicle position, lane direction, and intersection distance to improve route prediction accuracy at intersections.

GPS and accelerometer data from a mobile telematics app classify route familiarity in real time without dedicated in-vehicle hardware.

Virtual HUD elements are split into layered sub-elements with size, sharpness, and brightness cues to improve road-aligned depth perception.

Traffic-aware meeting point and timing selection lets a semiautonomous vehicle coordinate mobile order delivery with less manual planning.

Multi-sensor telematics combines GPS, sound, imagery, and vehicle motion data to detect unsafe road conditions and alert drivers in real time.

Vehicles pre-validate sensor travel data and share reliable detailed maps, speeding road updates for autonomous navigation.

Crowdsourced fleet energy rates let a vehicle adjust for extra load and driving conditions, improving distance-to-empty accuracy.

Routes are chosen by radar-localization object availability, then adjusted when localization quality drops to reduce driver takeovers.

Sensor-map comparison lets a lead AV detect road structure changes, update its route, and push revised map data to following AVs.

Prioritized switching among map, camera, and stored drawing data keeps vehicle navigation displays continuous when key sources fail.

Per-target matching of onboard sensor data with map features enables lane-level map quality checks and selective updates for automated driving.

By separating each passenger's voice and combining occupant records, the system estimates requests more accurately in multi-occupant vehicles.

Automatic route recording starts and stops at parking lot boundaries, cutting driver input while generating parking and unparking paths.

Combining map data, vehicle position, and surrounding road sensing enables stable lane-level localization on multi-lane roads despite GPS noise.

V2V, GNSS, and map-matching let vehicles assess roundabout priority and collision risk without roadside infrastructure.

Predicted road-segment conditions from multiple data sources enable rerouting and scheduling that keep automated vehicles moving within ODD limits.

Scenario-specific control modules generate real-time actions for merge and obstruction events, improving autonomous vehicle safety and traversal efficiency.

Predictive alarm stopping reduces repeated alerts in areas with many unfamiliar drivers, improving driving comfort without losing location awareness.

Route assignment uses road grade, speed, motor stress, and battery use to cut wear on alternative energy vehicles and extend fleet life.

Road surface markings replace adjacent vehicles as references to detect parking exit direction more accurately and avoid incorrect path calculation.

When a final destination is off pre-approved roads, the vehicle routes to an intermediary stop and guides the remaining transfer.

Fixing the charging station on the map display makes EV distance and reachable range easier to judge, helping prevent energy shortages.

A head-up display projects a virtual lead vehicle before tunnel entry to improve lane guidance, visibility, and driver comfort in low-visibility driving.