Instruments For Road Network Navigation TRIZ Cases

Combining vehicle acceleration, shaft output, and position data enables road state detection for navigation alerts, repair planning, and city management.

Stable and available-state icons help drivers recognize autonomous mode changes quickly with clear visual and auditory cues.

Road- and condition-specific perception models are selected from a library to improve autonomous vehicle perception accuracy and control reliability.

Reliability-weighted fusion updates vehicle maps with new probe data while limiting error propagation and preserving position accuracy.

Position and map data are combined to show legal speed limits and route cues in the driver's view without blocking the road.

Monitored power, airflow, door, and temperature data trigger driver alerts that help prevent cargo spoilage and avoid wasted energy in transit.

Timed takeover prompts and pre-switch map display help drivers avoid missed intersections when self-driving shifts to manual control.

A virtual map adds obstacle icons and warning regions so the vehicle can react to distance and direction risks during driving.

Vehicle weight, size, and tire data are sent to a server so route planning can avoid roads with load or dimension limits.

Displayed HUD content shifts with road curvature and vehicle course to keep the driver's gaze aligned through turns and reduce steering errors.

Server feedback logic re-routes autonomous shared vehicles for lost article recovery while limiting passenger service delay and cancellation.

Charging suggestions are scored with weather, crime, amenities, time, and cost to improve EV route and stop selection.

When server links are weak, onboard map data and sensor fusion maintain lane-level routing, SLAM visibility, and dynamic path updates.

Historical heart rate and respiration data help identify low-stress road segments and guide personalized routes that reduce driver fatigue and anxiety.

Distributed V2X safety assessments let autonomous vehicles negotiate maneuver conflicts faster without centralized coordination complexity.

Route-aware speed planning uses vehicle, route, and traffic data to cut energy use near mandatory deceleration points in urban driving.

3D obstacle data and transport parameters automate oversize route checks, cutting planning time while improving safety and regulatory compliance.

Repeated manual drives build and update route characterizations with confidence thresholds, enabling autonomous navigation without costly map refreshes.

Fusing sensor evidence with Dempster-Shafer belief and plausibility measures improves lane-type and roadway hypotheses for safer vehicle planning.

Ray tracing includes occluded non-matching LIDAR points in map overlap scoring, improving autonomous vehicle position recognition.

Matches SD map waypoints to HD lane geometry, adding waypoints and lane detail for assisted and autonomous vehicle navigation.

Predictive vehicle positioning uses telemetry and user data to cut idle time, improve task allocation, and speed autonomous ride requests.

A closed-form route-relative integrator avoids singularities on high-curvature roads, improving autonomous vehicle trajectory stability and control.

Probe data linked to path segments reveals why ADAS features disengage, helping route planning avoid unreliable autonomous driving areas.

Combining bus data, vehicle dynamics, and 3D road maps reveals subtle tire, shock absorber, and chassis faults earlier.

Matches autonomous and manually driven vehicles by route compliance and availability to cut wait time and improve dispatch efficiency.

Fleet-level transition probabilities predict likely vehicle routes without personal data, helping optimize fuel use, emissions, and control planning.

Remote intervention, passenger authentication, and sensor-fed XR help autonomous vehicles handle unsafe conditions, unauthorized riders, and accessibility needs.

Periodic cover-position checks and cargo volume reporting enable route updates, load matching, and theft-aware monitoring for transport vehicles.

Stored camera images and feature-point matching maintain indoor vehicle localization when LiDAR fails, without heavy Visual-SLAM computing.

Walking-path heatmaps from map and sensor data help autonomous vehicles choose pickup points that match real pedestrian routes.

Preselects EV charging stations by matching vehicle, user, and environmental factors to cut planning time and avoid incompatible stops.

Travel information is reduced during hands-off automated driving, but restored by vehicle situation detection when driver awareness is still useful.

Dynamic AR windshield overlays align speed, navigation, and hidden-object cues with the road scene to improve driver awareness in real time.

A determination point on the travel track uses sensor-measured features and self-position estimates to suspend automated driving at the right time.

Route-segmented speed profiling helps vehicles handle mandatory stops with lower fuel use and more accurate resource-efficiency evaluation.

Fusing navigation maps, HD maps, road-sign images, and lane curvature yields safer lane-specific speed guidance on curves and straights.

Singular value analysis of vehicle coordinate data distinguishes straight and turning sections without GPS, extra sensors, or heavy image processing.

Switching between 2D video and layered 3D surrounding data cuts transmission and processing load while preserving needed spatial detail.

Nearby vehicle labels are transformed across coordinate systems to auto-label occluded or out-of-range objects with less manual effort.

Predicted autonomous driving duration helps the vehicle HMI present secondary tasks that fit available time and reduce interruptions.

Camera-based virtual lane references guide a vehicle smoothly to the road edge and stop it without unstable steering near curbs or guardrails.

A mobile charging vehicle calculates needed power and meets low-battery EVs on the road, avoiding towing delays and extending range.

Dynamic cost scoring helps autonomous vehicles choose lane change points in real time, avoid routing loops, and adapt to traffic conditions.

Shared spatio-temporal identifiers let different autonomous mobile objects exchange future position data and avoid route conflicts.

Route portions are analyzed separately and matched to vehicle-specific data to generate resource-efficient driving paths with lower consumption.

Map and sensor fusion lets automated longitudinal control decelerate correctly at traffic signals by accounting for stop-line distance.

Remote falsification data verifies detected surroundings by location, excluding false ADAS inputs before they trigger incorrect vehicle control.

Threshold-based trajectory sharing cuts vehicle coordination data traffic while keeping collision-free maneuver planning stable in dynamic traffic.