Instruments For Road Network Navigation TRIZ Cases

Pre-set passenger ethics and activity-aware routing help self-driving vehicles avoid travel disruptions and act automatically in critical scenarios.

Clustered road surface profiles and terrain-based offsets correct GNSS drift and improve lane ordering on complex multi-lane roads.

Timed confirmation or travel-status notifications help occupants verify the intended route during hands-off automated driving and avoid route deviation.

Route calculation uses preprocessed road lighting data and user brightness limits to improve comfort and guidance reliability.

Adaptive waiting based on vehicle scene and network prediction cuts offline synthesis frequency and preserves voice response quality.

Recorded human driving data and passenger presence guide lane planning that adapts to vehicle capability for safer, more comfortable autonomous driving.

Vehicle location and orientation traces from multiple cars let a central system reconstruct parking lot shapes and aisles more accurately.

Monitors power demand, airflow, and door-open duration to alert drivers and help protect cargo conditions while reducing energy waste.

Risk zones built from multiple virtual vehicle positions make hazards easier to perceive and help drivers react sooner.

Position-based lane width lookup tables give autonomous vehicles flexible spatial constraints for smoother lane changes around obstacles.

A driverless taxi tracks the rider's mobile position to shift pickup in real time, cutting wait time while preserving pickup reliability.

Unused onboard compute from autonomous vehicles is predicted and pooled to distribute requests with lower latency and better resource use.

An independent speed profile check compares route-based target speeds to catch optimization faults and keep vehicle energy use efficient.

Calculates driving and air-conditioning power together, then shows reachable stops and nearby charging spots before battery range falls short.

Sensor feedback corrects recorded parking paths so repeated automatic maneuvers reach the intended space position more accurately.

Lattice road data and crossable dipole graphs simplify road-lane mapping for more accurate autonomous driving plans.

Connected vehicle and IoT road data are combined to detect hazards and trigger coordinated illumination that improves visibility for drivers.

Combining map, V2X, and sensor sources lets one localization service support multiple vehicle apps with lower processing, memory, and bandwidth use.

Microphones replace costly rain sensors by classifying windshield rain sounds and weather data to drive wiper speed automatically.

Trip schedules are revised by locking fixed stops by vehicle attributes, allowing new destinations without breaking operational rules.

Adaptive sensor selection distinguishes sleeping from abnormal driver states while reducing unnecessary monitoring in sleep-permitted automated driving.

Calculates each EV's travelable distance and guides the group through reachable charging stations to avoid battery depletion and dispersion.

Camera images are transformed to a top-down view and matched with aerial maps to achieve lane-level vehicle positioning beyond GNSS accuracy.

Stored manual driving experience helps autonomous control handle more road situations and reduce unexpected cancellation events.

Fusing camera, sensor, and GPS outputs with sparse maps cuts data load while preserving autonomous navigation accuracy and safety.

Cloud routing allocates restricted lanes and stops to non-priority vehicles while avoiding conflicts with priority vehicle routes.

A map-based reachable range display turns remaining mileage into a visual travel area, reducing manual estimation during route planning.

Adaptive LiDAR road-line range selection captures enough broken or curved line data to improve vehicle position estimation accuracy.

Human-guided lead vehicles help autonomous transport services handle uncertain urban routes with safer passage and less braking.

Validated technician commands let roadside assistance safely change an autonomous vehicle's state when it cannot continue without human help.

DNN-based LiDAR point classification and curve fitting improve landmark detection and localization precision on curved roads.

A vehicle-linked point platform combines driving and smart-device activity data to reward eco-driving, health, and social contribution actions.

Sparse road-segment maps preserve autonomous navigation accuracy while cutting storage, processing, and data transfer overhead.

When charge runs low, route-side battery depositories let a delivery robot swap packs instead of returning home, cutting travel, time, and power use.

Real-time and historical road-segment data predict autonomous driving levels, helping drivers compare routes by automation coverage.

Fusing sensor data with mapped lane geometry improves target vehicle lane assignment in complex road scenarios with limited sensor view.

When a driver remains unresponsive, the vehicle switches to autonomous travel, alerts nearby traffic, and heads to a suitable medical facility.

Accelerometer and gyroscope patterns classify vehicle entry side to distinguish drivers from passengers without in-vehicle hardware.

Cloud ML models combine high-fidelity simulations and telematics to configure heavy-duty trucks for route-specific performance and emissions compliance.

Historical route-segment data and real-time battery sensing predict energy demand and flag maintenance before remote machines run low.

Ultra-wideband ranging and angle detection locate a future passenger in crowds, helping the vehicle choose a precise pick-up position.

Advance display of planned vehicle actions and behavior options reduces driver anxiety and supports smoother autonomous-to-manual takeover.

By fusing onboard sensor data, the vehicle detects icy road conditions early and adapts braking, traction response, and routing.

Adjusting HUD white-light color temperature against headlamp output preserves virtual image visibility and reduces driver eye fatigue.

Road-surface sensing and virtual image plane correction keep HUD AR graphics aligned on sloped roads and prevent surface penetration.

Recorded human driving data and self-aware vehicle capability parameters enable adaptive lane planning that better matches real road behavior.

Digital-map localization and cross-correlation keep a vehicle in lane through curves when markings are missing or unreliable.