Instruments For Road Network Navigation TRIZ Cases

Geometric anchors extracted from occupancy grids let vehicles localize without classified landmarks while cutting point-correlation workload.

Adaptive fusion parameters weight current and historical occupancy maps by motion and reliability to reduce boundary blurring around vehicles.

Automated real-time filtering and matching helps secure conveyance requests by preference while improving vehicle utilization and service quality.

Adaptive width control in HUD route guidance preserves perspective while expanding the faraway display area at larger map scales.

TPO complexity scoring tailors in-vehicle AR content to driving context, reducing distraction while preserving useful information.

Driver input signals update predicted routes before deviation, helping heavy-vehicle powertrains choose suitable strategies for upcoming road sections.

Beacon-guided alignment lets autonomous fleet vehicles dock accurately for unattended wireless charging, billing, and rapid return to service.

Pre-cooling the EV battery before reaching a charging station creates thermal headroom for higher DC fast-charging current and shorter charge time.

When traffic or weather delays arise, the vehicle detects a sleeping driver and reroutes automatically to cut travel time without waking them.

Fusing sensor and map distances helps automated vehicle guidance handle traffic lights more reliably in urban longitudinal control.

A navigation system estimates return energy and selects an efficient path to the base station, reducing random search time and battery waste.

Conformal prediction assigns lane priority from road descriptors, cutting map data burden while improving autonomous navigation safety.

Estimated task energy and route-based charger selection help agricultural working vehicles avoid unplanned charging stops and reduce driver burden.

ADAS-based AR cues update distance, angle, and driving direction in real time to guide accurate parking or charger alignment.

Preference maps and vehicle scores guide household parking and selection, promoting lower-emission vehicles and reducing overall impact.

Fusing GNSS and surroundings-sensor curvature helps distinguish parallel roads, improving vehicle road localization without extra hardware.

Early alerts identify unsafe autonomous driving zones so drivers can switch to manual control before entry and reduce accident risk.

A two-step alignment process fuses multi-vehicle radar data with video-detected road boundaries to improve lateral map accuracy for automated driving.

Predicts dynamic power sharing at nearby EV chargers using vehicle, battery, and operator data to improve routing, charging time, and payment access.

Safe deviation intervals amend self-driving car reference paths at curves to keep the vehicle within lane boundaries and avoid collisions.

Specialized reservation, routing, and maintenance coordination helps autonomous ridesharing fleets assign vehicles, price rides, and track vehicle status.

Routes are generated to include or avoid speed-restricted and pedestrian-friendly roads, giving users clearer choices that match travel intent.

Fleet-based road roughness maps let suspension adjust before rough segments, improving ride comfort while limiting EV energy use.

When a lead AV detects an unmapped road obstacle, it reroutes itself, warns following vehicles, and helps update shared map data.

By aggregating parking vacancy probabilities across nearby routes, this case helps drivers find parking faster and reduce congestion.

Adjusts infotainment UI translucency, size, or position near turns so mapping data stays visible and drivers can complete maneuvers.

Clusters bicycle probe points outside bike lanes into hazard areas so autonomous vehicles can warn earlier and adjust driving parameters.

Sensors verify package addresses, detect threats, and trigger alerts to improve delivery accuracy, vehicle security, and route efficiency.

Peripheral light cues and steering-wheel haptics deliver navigation guidance without screen glances, reducing driver distraction in changing light.

Balances walking time, in-vehicle time, traffic conditions, and user preferences to improve trip routing and lower emissions.

Vehicle sensors detect construction items and nearby traffic to pinpoint roadwork end points and enable timely driving mode transitions.

Dynamic road-segment cost updates disperse clustered autonomous vehicles, reducing congestion and improving fleet routing reliability.

Virtual-ray path planning uses vehicle dynamics and real-time environmental data to build smoother, obstacle-safe routes without onboard sensors.

Route recommendations combine past driving locations with vehicle state data such as tire and centrifugal force to match user preferences.

Adaptive route planning sets charging stops and minimum battery SoC so the vehicle arrives with the required remaining range.

Departure and destination data are used to infer route, speed, acceleration, distance, and tire wear without mounting vehicle sensors.

Combining attribute-based and sensor-based road maps enables lane-level route selection, removes irrelevant segments, and supports safer automated driving.

Continuous request analysis and pre-screening help secure conveyance services in real time while improving routing, dispatch, and vehicle use.

Nearby vehicle and infrastructure displays turn phone route instructions into real-world cues, reducing pedestrian disorientation in unfamiliar areas.

Preplanned branching routes let automatic parking switch to a new slot during the maneuver, avoiding screen input and reducing parking time.

Fade-out of the earliest vehicle plan makes display changes easier to notice while keeping scheduled actions easy to track.

Map and sensor data identify steep hills, adjust torque and braking, and trigger route changes or handover to prevent roll-back.

A vehicle runs a planned test route to verify sensors and map data, then selects an operating mode for safer, faster fleet deployment.

A nearby vehicle detects failed brake lights or turn signals, then onboard diagnostics confirm the fault and guide corrective action.

Stored forward-driving paths and weighted sensor, GPS, and image correction improve reverse vehicle positioning with lower computation load.

Sensor and location data trigger real-time parking listings and image overlays, reducing manual searching, delay, and distracted driving.

Weighted sensor, GPS, and camera feature fusion corrects stored forward paths for accurate vehicle reversing with lower compute demand.

Continuous speed and GPS validation makes telematic insurance activation enforceable and harder to fake after a crash.

Biometric sensing and AI route prediction help vehicles avoid stressful traffic and choose alternate paths that improve occupant health.

Fusing camera obstacle probabilities with radar time-of-flight data reduces blind spots and improves drivable area recognition.