Road Vehicles Traffic Control TRIZ Cases

Distance-to-destination trends over time trigger alerts only for true vehicle stuck conditions, reducing false positives and operator load.

Continuous policy-based monitoring aligns vehicle position and speed before enabling unsupervised ADS mode, reducing activation delays and rejection.

A shared vehicle control interface combines ADK and brake-pedal deceleration requests to keep autonomous mode control compatible and reliable.

A unified planner interface standardizes data streams and smooths primary-to-fallback control transitions in autonomous vehicles.

Visual AR guidance in a helmet HUD helps motorcycle riders receive critical driving information without relying on sound or vibration.

Integrated sensing and staged warnings help detect adjacent vehicles and lateral wind risk, with optional steering and speed intervention.

Precomputed occupancy scores guide fleet traffic planning to cut standstill time, avoid battery depletion risk, and limit real-time computing load.

Earlier collision checks are triggered when opposite-lane entry is predicted, reducing brake delay without raising false activation risk.

Weighted multi-zone radar and image sensing improves collision probability judgment under environmental noise and varying lighting.

Road type and lane shape data adjust lane-image count and layout, making vehicle surround displays easier to read in manual and automated driving.

Collision paths are ranked by impact speed, object type, and injury probability to choose a safer vehicle trajectory and trigger alerts for severe risk.

Roadside traffic data extends motorcycle detector range, enabling earlier safety actions that smooth speed changes and reduce rider shock.

Roadway sensor nodes let autonomous vehicles detect lane accessibility and adjust travel paths through construction zones without heavy marker processing.

Surrounding vehicle trajectories are predicted by driving mode and shared data, improving planning accuracy and reducing collision risk.

Judgment logic uses object position, reflection strength, and short detection time to suppress reflected rear cross-traffic warnings.

Real-time road, weather, and infrastructure data are filtered into restriction guidance so vehicles can limit automated driving only when needed.

Standstill-gated shift control lets a removable autonomous driving system change gears without causing unstable vehicle travel.

Pre-emptive transmission of driving instructions helps connected vehicles maintain safe operation when entering zones with weak or no network coverage.

A rising effort threshold keeps vehicle cooperation stable during changing traffic, reducing reinitiation and unnecessary steering or braking.

Quick resumption logic suppresses deceleration-end alerts when another trigger object is imminent, reducing driver discomfort during assistance transitions.

RTLS signals and fixed beacons let a materials-handling vehicle identify zone rules and adjust speed or operation in real time.

Vehicle-data-triggered lighting and sensors improve hitch area visibility and obstacle detection without continuous operation.

Hiding the set following-distance indicator during lane changes avoids lane-marking crossover discomfort and prevents unintended distance adjustments.

Real-time GPS tracking, digital trip records, and trip-specific tags streamline driver assignment while reducing transport errors and cost.

Avoidance and availability metrics use spatio-temporal occupancy grids to evaluate prediction models for safer, less restrictive autonomous driving.

During a lane change, steering assist is paused and then resumed before completion to cut manual driving time and reduce driver burden.

User proximity is confirmed during vehicle handover so completion alerts can be delivered and service interruptions avoided.

Map regions switch between local parameters and swarm behavior values to cut processing and data transmission while maintaining driving accuracy.

Predicting pedestrian behavior and signaling planned vehicle actions through eHMI reduces uncertainty and collision risk in traffic.

Predictive merge interference detection lets vehicles coordinate suppressing requests before lane entry, reducing collision risk and merge anxiety.

Notification history and planned routes are used to warn drivers only about unnotified accident-prone spots, reducing repetitive alerts.

Posture-based clearance updates help rider assistance judge slip-through space more accurately during lean turns and changing vehicle orientation.

Sensor and ML-based matching helps autonomous vehicles identify amber alert vehicles and report verified sightings for faster response.

Cloud-stored user settings are verified and transferred between vehicles to avoid manual setup while enabling secure personalization.

Blending digital twins with virtual and real traffic cuts autonomous vehicle test cost and risk while preserving realistic validation.

Backend-mediated control lets riders pre-set climate, media, and navigation functions securely before and during shared trips.

Successive ultrasonic echoes classify static vs. moving objects, improving pedestrian detection while avoiding heavy echo tracing.

By combining camera images, swarm data, and road-condition metadata, this case improves lane profile confidence for safer lane change decisions.

3D object data and split-region damage maps help steer the vehicle to reduce collision damage when obstacle type is hard to judge head-on or obliquely.

Bounding-box overlap rules automate cut-in labeling in vehicle data, improving autonomous driving model training on rare lane-entry events.

C-V2X alerts routed to the infotainment display warn drivers of approaching emergency vehicles when sirens or lights may be missed.

Nearby vehicles receive incident messages, activate visual warnings, and reposition to form protective barriers that improve roadside safety.

A negotiation protocol builds task consensus between AI-driven unmanned vehicles and human operators, improving action visibility and coordination.

A macro-micro control scheme splits high-level driving plans from specialized actions to cut computation time while maintaining reliable autonomous operation.

When onboard sensing cannot confirm a safe pass, an external center coordinates a nearby vehicle's lane change to clear obstacle passage.

Image recognition and speed measurement identify slow vehicles in fast lanes early enough to trigger warnings and reduce traffic disruption.

Segmented neural networks combine traffic light images with sparse maps to improve autonomous navigation accuracy without heavy map data.

UE status feedback lets the ADS avoid redundant emergency transmissions, cut energy use, and maintain reliable autonomous response.

Route-overlap analysis groups parking spaces by actual user use, helping autonomous parking avoid irrelevant spots and improve safety.

Real-time traffic light recognition and path deviation calculation let vehicles identify relevant signals without stored map positions.