Instruments For Road Network Navigation TRIZ Cases

When sensor faults persist, the vehicle lowers autonomy and limits operation using intended sensor output and compensating data.

Human-guided sensor interpretation helps autonomous vehicles follow failsafe trajectories through crowded urban roads with fewer abrupt stops.

Unique location IDs replace raw coordinates so autonomous mobile units can share route and space-time region data across devices.

Route segments and vehicle-specific parameters are used to modify driving paths in real time, cutting energy use while meeting time needs.

Compares mapped sensor expectations with actual readings and error sources to estimate drivable areas and hidden objects using lower-resolution sensors.

Road gradient analysis and towing-load comparison help EVs choose towable routes and avoid overload, overheating, and accidents.

A unified temporal occupancy flow graph captures vehicle and lane interactions together to reduce bias and improve ego-vehicle prediction accuracy.

By reversing before arrival when the user and target compartment are on opposite sides, the vehicle improves package access at pickup and delivery.

Road-section carbon calculations turn vehicle driving data into low-emission route guidance while avoiding heavy real-time processing.

Camera-based route planning tests candidate vehicle actions against accident liability rules to block risky maneuvers and keep navigation scalable.

Scenario-specific control modules model sensor uncertainty and external object interactions to generate real-time vehicle actions in complex networks.

Directional arrows let a narrow-FOV vehicle HUD indicate edge traffic signs and target paths without a larger, costlier display.

Position and light sensing automate vehicle curtain opening before tunnels and lighting changes to improve visibility, safety, and convenience.

Switching between detailed and simplified nearby-vehicle views helps balance occupant awareness and information overload during automated driving.

A gridded lane-change map with potential-function weighting generates and smooths multiple paths to balance comfort, time, and energy use.

Road-surface normal vectors from map landmarks correct pitch and roll on slopes, improving feature matching and reducing estimation load.

Real-time road-map mismatch monitoring rechecks lane assignment to keep vehicle positioning accurate and stable without RTK.

Conditional on-road charging suppresses wireless power reception when user fee, battery, or CO2 preferences are not satisfied.

Real-time road-segment risk scoring helps semi-autonomous vehicles choose safer routes and supports mode-based insurance coverage updates.

A navigation algorithm uses current battery charge and partial range to suggest charging stops only when needed, cutting extra stops and charging time.

Precomputed route options add fueling stops based on fuel level and preferences, cutting user input time and processor power use.

GPS trajectories, map matching, and road features are combined to build accurate lane centerline networks with lower equipment and processing burden.

Pre-collected ethical preferences and activity-aware routing let autonomous vehicles avoid interruptions and choose crash actions without real-time input.

Aggregated probe data and road-condition signals generate segment-level speed profiles that guide autonomous vehicles through hazards, traffic, and weather.

Compressed geospatial playback turns dense vehicle telematics into mobile-friendly trip maps and heat maps for spotting driving patterns and risks.

Adaptive driver assistance filters corrective guidance by skill rating and planned trajectory to improve performance without overwhelming the driver.

Fusing camera, GNSS, IMU, and lidar data builds 3D terrain maps that improve vehicle steering, traversability prediction, and field protection.

Early takeover alerts use route data and hands-on-wheel checks to prevent abrupt lateral control deactivation in critical driving situations.

Personalized routing, comfort controls, and safety checks help assistance passengers travel more independently and safely.

Replans parallel parking paths by adjusting S-shaped and forward or backward moves to handle changing surroundings without aborting parking.

A multi-corridor route model combines lane segments with perception data to choose real-time lane changes and avoid route failures.

Images from an external device are converted into destination options and route guidance, reducing manual entry time in vehicle navigation.

Predicted stopover loads guide trailer battery charging, balancing regenerative propulsion support with auxiliary power availability.

Bird's-eye image processing turns pixels into spatial graphs and vector maps, cutting manual HD map work for autonomous vehicles.

By comparing current and next road attributes, this case isolates key lane start and end points for faster, more precise vehicle control.

Multi-source GPS stop and transition data reconcile planned and actual delivery timelines, improving route updates and cost estimation.

Real-time symbolic vehicle displays preserve relative position accuracy, helping drivers judge lane changes and avoid nearby collisions.

Dummy charging routes let EV schedulers model SoC recovery during the day, improving route assignment, utilization, and emissions.

Predicted paths and object-specific cost data help an autonomous vehicle plan real-time motion, keep safe distances, and avoid collisions.

Stored entry feature points let a vehicle estimate an exit region and generate an automatic path when no parking frame or target object is detectable.

Pattern checks across lanes and traffic signals detect map errors and auto-generate internal intersection lane data for faster AV map updates.

Sensor data and ML let autonomous vehicles detect pickup queues, choose entry timing, and avoid blocking other road users.

Multiple lane-specific filters and ML-based selection improve vehicle pose estimation when GNSS and sensor data are noisy.

Real-time comparison of expected and measured vehicle fuel use flags abrupt deviations and helps attribute extra consumption to faults or driving behavior.

Historical charging success and failure records are used to rank EV charging stations, helping users avoid unreliable stops.

Proposed trajectory overlays let remote operators guide autonomous vehicles past construction, poor markings, and stranded vehicles with lower latency.

A scenario generator and constraint solver align multi-object constraints into consistent trajectory decisions for safer autonomous vehicle navigation.

A planned straight-then-turn path lets the vehicle scan a sensor blind zone before turning, reducing collision risk from unseen objects.

Real-time sensor selection and measurement-rate control maintain trajectory quality while cutting energy use and processing load.

Controller advises operators on fuel-efficient climate modes using real-time sensor data, resolving the trade-off between occupant comfort and aerodynamic drag.

Segments roadway networks into discrete links to merge traffic and rainfall data, reducing model complexity while improving prediction accuracy.

Acceleration data from portable terminals estimates crowd density in restricted areas, eliminating expensive camera infrastructure.

A mapping application integrates ride service APIs to provide multi-modal travel directions and visualize options on a digital map.

Hatching and crosshatching overlay partially downloaded map regions to signal ongoing data transfer, resolving user confusion caused by network lag.

Mapping points of interest on a virtual plane prevents tag overlap in augmented reality views.

Segmenting continuous driving data into event-based frames reduces monthly usage to under 7 MB while maintaining accurate machine learning scores.

Segmented toolbar interface resolves precision and comfort trade-offs in aircraft cockpit navigation displays.

A retrieval apparatus calculates attribute word scores to prioritize narrowing options.

A controller requests exclusive occupation rights for path sections via wireless communication to coordinate autonomous vehicle travel.

Direct wave communication between vehicles shares location information without a centralized service center, reducing operational costs and network fees.

Image processing generates scalable maps to resolve the trade-off between wide satellite coverage and fine indoor location accuracy.

Precomputing cost-to-go values across a routing horizon enables real-time route guidance for autonomous agents without recomputing paths after deviations.

Reinforcement learning enables autonomous vehicles to adapt navigation through experience-based scenario models.

A positioning device combines autonomous sensor data with map references to estimate vehicle location.

A processing system removes moving object data points from laser sensor readings to generate accurate 3D roadway maps.

A radio key transceiver enables wireless vehicle-to-X data exchange using existing hardware infrastructure.

Remote operators steer autonomous vehicles through unmapped zones, collecting sensor data to map the region while avoiding obstacles.

Aggregating aircraft state data determines runway coordinates, resolving outdated database issues.

A vehicle headlamp system directs light onto specific road signs based on navigation data to enhance driver visibility.