Instruments For Road Network Navigation TRIZ Cases

Potential energy grid mapping and time-domain filtered parallel tracks help vehicles avoid slight lane intrusions without lane changes or stops.

Driving complexity prediction schedules call windows and durations so vehicle occupants can communicate without creating unsafe high-load moments.

Real-time Kalman filtering and trip-history models improve EV range accuracy under changing conditions, reducing range anxiety and poor charging decisions.

Different course overlays show whether an autonomous lane change ends in a displayed lane or continues beyond it, improving passenger understanding.

Predicted route travel times let the vehicle trigger or abort exhaust regeneration automatically, cutting fuel use and limiting driving impact.

Adaptive control parameters matched to road type help automated driving continue through alleys, city roads, and other non-average environments.

Clusters vehicle paths to exclude abnormal or unlawful trajectories, then averages the dominant class for a more reliable autonomous driving reference.

Matches EV charging stations to user parking preferences and charge intent, improving recommendation accuracy without excessive system complexity.

Crowd-sourced EV energy data is linked to road segments in the cloud to reveal usage patterns, support prediction, and improve trip efficiency.

Area-based parking guidance shows multiple candidate spots during search travel, then enables precise target position selection with less user burden.

Routes vehicles to roads near the destination with multiple accessible vacant parking spaces, improving parking success without long detours.

Pre-junction traffic assessment guides autonomous-to-manual handover at lane merges, reducing sudden driver demands and confusion.

Fleet trip data and ML predict candidate lane performance, helping autonomous vehicles route around poor visibility, traffic, and obstacles.

Ground-corrected satellite data and road-surface images improve coordinate estimation, enabling more precise autonomous vehicle navigation.

When a preceding vehicle crosses lane markings, control shifts from unreliable camera input to map-based lane data to keep self-driving stable.

Modular transfer learning and latent task mapping expand autonomous-agent ODDs with less labeled data while limiting negative transfer.

Exterior lights, sounds, and motion cues help riders find the assigned autonomous vehicle and avoid entering similar fleet vehicles.

Real-time obstacle sensing shifts passenger pick-up and drop-off points to safer entry and exit locations near the intended site.

Unique temporary signals let users identify the correct driverless vehicle at pickup without exposing personal or destination information.

Multiple route options are compared using trip data, roadway fit for driver assistance, and incident risk to recommend a safer trip path.

Adaptive predictors update route energy use during driving from load, speed, and past consumption to improve EV range estimates.

Maps uncertain ADS safety policy parameters to suitable locations for targeted observation, speeding safety proof and reducing development time.

Centralized junction sequencing uses road topology and vehicle status to cut conflicts, gridlock, and waiting in autonomous traffic.

By matching visible traffic signs with mapped signs, this case cuts fusion workload while improving assignment certainty for real-time vehicle positioning.

Preplanned detour paths help autonomous vehicles stay stable and avoid arrival delays when navigation routes fail or roads are blocked.

Predicted object paths reshape the drivable area so an autonomous vehicle can plan smoother obstacle-avoidance trajectories in real time.

A corner-activated handwriting panel separates scrolling from character input and keeps the list visible for lower-distraction driving.

Aligned multi-sensor 3D maps detect object presence changes, helping autonomous vehicles update navigation data for safer operation.

Motion planning feedback adapts driving maneuver decisions using distance, acceleration, and cost evaluation for more effective vehicle control.

Nearby vehicle poses are used to reconstruct lane geometry and match maps when LiDAR or RADAR is blocked in dense urban roads.

Groups users by pickup conditions and assigns vehicles with optimized pickup points to cut travel cost and improve ridesharing convenience.

Location-based user authentication lets a parked vehicle un-park to a personalized boarding spot based on weather, history, and cargo needs.

Ranks alternate routes by driving difficulty so inexperienced drivers can choose easier paths as the system adapts to skill growth.

Ranks parking routes by aggregated vacancy probability, helping drivers find parking faster with less distraction and congestion.

Route-based charging plans use telemetry and charger data to keep EVs operating continuously while lowering cost per distance and battery wear.

Road surface images replace noisy inertial sensors and GPS to estimate vehicle direction and position accurately indoors.

Real-time opportunity charging along fixed routes keeps EV battery charge in optimal bands, cutting cost per distance and battery wear.

Forecasted battery power and past charging records are used to route EVs through charging facilities that remain reachable en route.

Real-time route and charger data guide on-route EV charging to preserve battery life, cut energy cost, and reduce oversized battery needs.

Predicted demand, charger availability, and energy pricing guide fleet vehicle recharging to cut empty mileage and keep vehicles ready.

Obstacle data across nearby lanes is used to generate real-time target lines from lane geometry and safe passing width for autonomous vehicle control.

Real-time dispenser pressure status guides hydrogen vehicles to stations ready at reference pressure, avoiding refill standby time.

Pre-displaying non-superimposed navigation cues before HUD overlay switching reduces driver confusion when the vehicle view angle is limited.

Waypoint screening narrows candidate map points by distance, velocity, direction, and connectivity to place obstacles more accurately in semantic maps.

Maps traffic constraints into time-based configuration space to find safe lane-change gaps and reduce trajectory search complexity.

A confidence-based control flow detects rider readiness, selects a pickup point, and routes an autonomous vehicle with less user input.

Local route calculation in autonomous vehicles cuts central network traffic, while beacon redirection preserves coordination under poor connectivity.

When projected vehicle information overlaps driving guidance, visibility control reduces one image so the driver can recognize assistance cues clearly.

Affinity-based map indexing organizes geographic data hierarchically to enable progressive route guidance through intermediate municipalities.

A dead reckoning navigation system updates heading using inertial sensors and discrete position fixes for reliable indoor tracking.

A multi-sensor fusion method calculates measurement confidence using inertial and wheel speedometer data to improve positioning accuracy.

A network intermediary processes vehicle operational state data to resolve the contradiction between service coordination quality and system complexity.

A mobility assistance platform matches users with companions based on specific needs to facilitate multimodal personal transportation.