Instruments For Road Network Navigation TRIZ Cases

Area code updates after grid border crossings let EVs revise charging and V2G timing by regional fee policy to cut cost and smooth load.

Aligned camera images and motion estimation identify road conditions ahead, enabling vehicle control settings to adapt for safer, smoother travel.

Surrogate data from cameras, RADAR, and LIDAR helps autonomous vehicles infer hidden signal states and obstacles for safer control.

Threshold-based linear coordinate updates cut vehicle traffic data transmission, processing, and storage load while preserving map-aware tracking.

Filtered positioning data and segmented curve fitting reconstruct vehicle tracks more accurately when GPS signals are weak or unavailable.

Stable landmark maps and inertial data correct vehicle position in urban GNSS occlusion, reducing drift and unreliable map matching.

Dynamic route generation lets an autonomous delivery locker travel near a consignee's current location, cutting redeliveries in homes without delivery boxes.

Historical road risk and driver behavior are combined to rate segments, guide safer routing, and support insurance incentives.

Real-time sensor and navigation data detect hazardous roads and drowsy driving, enabling safer rerouting and risk-based insurance adjustment.

Separated AR guide elements move ahead of the vehicle to preview turns more clearly and improve route following for drivers.

Dynamic AR cues use ADAS and parking data to mark spaces, detect arrival, and switch guidance between forward and reverse parking.

By combining network, map, and sensor data, the AR interface predicts driving contexts and updates separable guidance objects in real time.

Previous-path lateral boundaries constrain each path update, reducing erratic vehicle motion while preserving smooth autonomous navigation.

When a requested stop falls just outside the service area, the system remaps pickup to a reachable in-zone location with map guidance.

Historical trip and lane data identify high-demand pickup zones and low-intervention roads for more profitable autonomous vehicle deployment.

Front vehicle trajectory and lane-number map data help identify the correct lane when white line types are unclear at branch sections.

Vehicle acoustic spectra and position data enable continuous road condition estimation without costly expert inspections or dedicated equipment.

When obstacles block sensor view, the vehicle shifts relative position to recover landmark detection and improve positioning accuracy.

Route planning adds penalties to off-path options so autonomous vehicles respect user-selected routes while still recalculating when needed.

Real-time health monitoring triggers AI route changes to the nearest qualifying healthcare facility for faster emergency response.

Vehicles pick the branch path with the smaller target angle to reach parking-lot destinations faster without prebuilt maps.

Driver readiness is judged before approving single- or multi-boundary lane changes, improving automated lane change safety and appropriateness.

Expected anchor-point timing is compared with actual detections to catch positioning errors in autonomous vehicles when GNSS or lidar is unreliable.

Time-stamped MAP messages add validity and confidence to node, road, and lane data so vehicles can use fresher road updates for routing.

Road marker state data lets vehicles self-diagnose magnetic sensors, cutting routine inspection and maintenance costs.

Map data embeds location-based driving automation restrictions, cutting vehicle-side ODD processing and improving automated driving safety.

Swarm road data is fused with camera lane detection and meta-information to create a mixed trajectory when markings or visibility are unreliable.

Dynamic risk maps and oversight-guided route selection help autonomous vehicles avoid high-risk road segments while meeting safety and compliance limits.

Historical vehicle connectivity data predicts route bandwidth by section, helping drivers avoid poor coverage for internet-dependent functions.

Stage-based HUD arrow cues and meter text show automated lane-change progress, helping drivers perceive control actions and intent.

Vehicle fuel or battery status is checked before parking starts, preventing stalls on long routes to remote parking spaces.

Multimodal rider input and NLU let autonomous vehicles handle destination changes, unexpected stops, and human-like in-cabin interaction.

Sleep and vehicle sensor data are combined to detect impaired driving and trigger alerts, alternatives, incentives, or warnings.

Landmark importance is used to trim map feature points, reducing memory load while preserving vehicle position estimation accuracy.

Precomputed constrainedness in 3D HD maps helps vehicles judge localization confidence in obscured areas while reducing real-time map processing.

Permission-based subscription keys let mobile apps access selected vehicle sensor data while reducing OEM connectivity, memory, and maintenance burden.

Calculates when wheel-driven charging of a towed vehicle is worth the added towing energy from counter-torque and extra load.

Speech-derived landmark cues let autonomous vehicles map verbal destinations to precise locations and calculate efficient routes.

Sensor-based accessory state detection lets mobile devices adjust operation by profile, reducing latency and unpredictable behavior.

Occupant behavior gathered before the main trip guides break stops, seat adjustments, and ADAS tuning to reduce fatigue and improve safety.

By predicting demand from vehicle location, battery state, and charging history, this case levels facility loads and avoids costly peak power.

Wireless workload offloading lets nearby autonomous vehicles share GPU and processor tasks, easing parallel compute and data transfer bottlenecks.

Sensor-guided lane positioning before a turn helps autonomous vehicles signal right-turn intent clearly in wide lanes while avoiding nearby objects.

When remote support fails, the vehicle switches to a route with fewer support-dependent positions to improve destination reachability.

A leader vehicle forms a virtual link to guide simpler follower vehicles, improving on-demand autonomy across varied routes at lower system complexity.

Cost-based reasoning lets an autonomous vehicle slow early for occluded pullover spots, reducing hard braking and passenger discomfort.

Combining image semantic labels with depth boundaries reduces shadow, noise, and false-point errors in environment maps for autonomous movement.

Real-time camera, LIDAR, and RADAR previews let riders assess pickup surroundings and switch to safer locations when needed.

Temporal roadway data is mapped to routing graph segments to adjust costs and connectivity, improving autonomous vehicle route predictability.

Sensor-based road segment similarity classification links segment types to risk profiles, enabling earlier hazard-aware routing and fleet decisions.