Instruments For Road Network Navigation TRIZ Cases

Lane-level congestion detection and entry-route guidance help vehicles change lanes earlier near intersections and forks to avoid violations and accidents.

A cost-based lane model combines lane basins with dynamic object costs to select smoother autonomous vehicle trajectories with less computation.

Compares sensed lane lines with map lanes and switches trajectory references by nearby vehicle recognition to improve long-range lane determination.

Stored teacher routes are corrected using sensed obstacles and curvature limits, then displayed for approval to improve autonomous parking accuracy.

Route selection uses segment travel deviation and speed-adjustment flexibility to hit target arrival windows while reducing idle time.

Camera images of offset road-edge objects are converted into free-space boundaries to guide safer vehicle navigation without complex sensor arrays.

Candidate routes are scored by segment energy use and updated with real driving data to ensure arrival with a planned energy reserve.

Environmental sensing adjusts self-position estimation parameters in real time to keep mobile body localization accurate and robust.

On-demand autonomous tour routing matches pickup locations to pre-stored multi-stop tours, improving passenger comfort and trip flexibility.

Trip data weighting separates friction, HVAC, air resistance, and regenerative braking to predict battery vehicle range more accurately.

Predictive routing sends low-speed unmanned vehicles to side paths so faster vehicles can pass and work site congestion is suppressed.

Correcting one-sided road marking mismatches with map-based self-position checks helps stabilize automated driving and reduce lateral swaying.

When a scheduled charger becomes unavailable, the route is updated to a vacant supply facility to avoid EV travel disruption.

Emergency mode detects a pregnant occupant, guides the vehicle to a suitable hospital, and relaxes acceleration limits for faster arrival.

Map-linked access keys, sensor rules, and task triggers let autonomous vehicles adapt to specific areas without custom navigation software.

Relative object-to-landmark positions let vehicles share usable control data despite outdated HD maps and mismatched map systems.

Repeated multi-sensor perception and mode-based route planning help autonomous vehicles handle unexpected road abnormalities when detection fails.

Deep learning infers vehicle control variables from multi-sensor data to analyze air resistance and friction for more precise autonomous driving.

Precomputed stop-time and speed-based power maps help electric trucks choose routes that cut energy use and extend driving range.

Sensor-map position checks validate GPS time before clock updates, limiting drift and preserving radar and laser measurement accuracy.

Real-time queue estimates combine station, vehicle, and navigation data to cut hydrogen refueling travel, wait, and fill time.

A controller matches stranded EVs with nearby vehicles or home charging options using SOC, location, route, and availability data.

Preplanned switching between inconsistent road maps lets the vehicle follow separate driving plans and maintain stable, accurate control.

A light guide in a standing windscreen display keeps vehicle information in the driver's forward view, reducing eye movement and fatigue.

Maneuver-specific trajectory weights and latent vectors cut route-planning complexity while preserving obstacle-aware driving route coverage.

Map-matched telemetry, GMM, and lane-point clustering improve lane line location accuracy for autonomous vehicle navigation.

Iterative routing and procurement optimization links EV charging demand, external sources, and parked batteries to lower enterprise electricity cost.

Randomized vehicle location data enables nearby EV charging station selection while protecting driver privacy and supporting real-time charging.

Required and arrival battery levels are calculated for each travel leg to recommend station adjustments that avoid overcharging and unsafe discharge.

A lane-level route graph combines map and sensor data to guide autonomous highway exits when lane connectivity is incomplete.

Directly matching radar samples to transformed map landmarks improves vehicle positioning in GPS-denied areas while reducing point-cloud processing.

Route-aware OTA control downloads data in stable sections and pauses before poor coverage, cutting restart traffic and wasted communication.

Destination cues appear only when visibility conditions allow, then shift focus and size to avoid blocking the road scene near arrival.

Independent trailer propulsion, steering, and ADAS control let a wagon-style trailer track the lead vehicle path while reducing load and range loss.

Map-based comparison of signal locations with camera-detected lights helps vehicles distinguish the correct traffic signal at night.

CMOS camera guidance calibrates new trailers, plans an obstacle-free path, and automates steering for safer hitching and maneuvering.

Aggregated trip, service, and sensor data reveal fleet-specific driver fatigue patterns and trigger timely alerts or break recommendations.

Charging reservation data at the destination is used to adjust route stopovers and charge amounts, reducing range anxiety and excess charging.

Animated collision-target icons use color and count to make route accident risk easier for drivers to recognize at a glance.

External wireless brake sensors track pad wear and temperature without disturbing brake integrity, enabling predictive maintenance alerts.

Stable landmark reference maps let vehicles correct inertial drift and reduce image-matching load when GNSS is blocked or inaccurate.

Fuses camera, sensor, and map outputs to decide when obstacles should trigger navigation changes and when no adjustment is needed.

Region-specific frame rate and pixel decimation settings improve road line detection in tunnels, rain, and mixed driving modes.

Personal settings stored in each digital key let temporary drivers use preferred vehicle functions while restoring the owner's setup after use.

Combining SD navigation routes with HD lane centerlines cuts map-processing load while enabling precise path generation and obstacle-aware lane changes.

Synchronized data from moving and roadside IoT objects improves autonomous vehicle deep learning while reducing processing delays in navigation.

Real-time parking lot density and reservation data are combined to guide vehicles through low-traffic routes to reserved spaces and exits.

In-vehicle passenger tracking guides AV routing and seat alignment to enable safer curbside pickups without hazardous street crossings.

Adaptive judgment range shortening on curved roads improves map-consistent lane line recognition and avoids autonomous driving mode downgrades.