Road Vehicles Traffic Control TRIZ Cases

Verified cloud profile transfer lets drivers carry settings across shared or private vehicles while keeping access secure and automatic.

Software-defined speed limiting replaces fixed hardware with user-set preferences, adaptive updates, and space-saving vehicle integration.

Predicted passage and target reaching times let the controller end crossing deceleration early when the vehicle can clear the intersecting region first.

Displays the angle gap between vehicle direction and parking-space orientation to help drivers align properly and fit within the space.

Restricting preliminary braking and steering actions on curved roads improves control margin while reducing false alerts from uncertain lane recognition.

When a rear vehicle approaches from behind, the control keeps lane departure prevention active despite turn-signal blinking to reduce close-pass collisions.

A mediator coordinates concurrent assignments from different service entities so one autonomous vehicle can combine routes, cut downtime, and use capacity better.

Dynamic pickup time and location updates trigger automated transport relocation to nearer event access points and available parking.

Trajectory comparison by vehicle category helps an autonomous vehicle keep safe distance and respond to emergency or non-compliant traffic.

Credential-verified pull-over access lets law enforcement request an autonomous vehicle stop and enter it without weakening security.

Adaptive driver alerts combine sensing and mobile-body communication to warn of shielded oncoming motorcycles without excessive annoyance.

Video-based context checking filters false speed limit signs and shows drivers the valid limit for current road conditions.

Navigation-path-based range control limits HD map dynamic updates to relevant roads and lanes, cutting bandwidth use and transmission delay.

Color-based deceleration thresholds help driving assistance avoid unnecessary braking in yellow-light dilemma zones while preserving safe stopping.

Wireless traffic light signals and camera input let ACC switch between cruising and stop control to prevent signal violations.

A removable plate frame combines NFC, GNSS, cellular, and motion sensing to track multiple vehicles with lower installation effort and power use.

Priority-based handling of AVDS data lets urgent driving information reach user terminals first, reducing delays in critical situations.

Local ML predicts future vehicle states so servers receive only essential usage data, cutting traffic while preserving digital twin accuracy.

Marker-based localization uses unique and anonymous parking-lot markers to maintain precise vehicle positioning where GPS is blocked.

Priority-tagged AVDS messages let critical driving alerts reach the user terminal first, reducing delays in emergency response.

Sensors detect sirens, lights, or signals from emergency vehicles early and warn drivers through in-vehicle displays and speakers.

Selected vehicle camera images cut communication and processing load, helping remote driving control stay stable when control-value delivery is delayed.

Cabin noise frequency and sound pressure are used to tune speed-limit alerts so drivers hear warnings clearly without constant loud buzzer output.

When side tracks are blocked, the controller shifts from steering avoidance to staged braking and driver alerts to preserve response margin.

During remote parking exit, the vehicle adjusts its stop position to the selected door and phone location, reducing user setup and access burden.

V2V blinker-state sharing improves collision prediction around the vehicle and cuts unnecessary driver alerts during lane-change risk assessment.

Selective sidewalk video alerts notify remote operators when pedestrians are detected, improving blind-spot safety without constant video display.

Predicted vehicle tracks and evaluation distance help detect intersection collision risk even when a risk position is not identified.

Magnetic markers with RFID-defined connection points let buses switch between dedicated and general lanes without rebuilding fixed guidance routes.

Manual driving trajectories are converted into control-point knowledge so vehicles can generate turning tracks where autonomous driving previously stopped.

Battery reserve thresholds govern on-board equipment activation to prevent EV battery drain while preserving driving range and task capability.

Variable AEB thresholds based on prediction accuracy enable earlier gentle braking while avoiding false activation and sudden stops.

Region-based forgetting rates retain critical obstacle history while clearing stale data, improving occupancy maps for moving object path planning.

Sensors and onboard control detect passenger medical emergencies, alert responders, route the vehicle, and send location and medical records.

Relative speed and separation distance are combined into continuous hazard measures that reveal vehicle behavior risk before collisions.

Curve-based lane markings in the cabin explain automatic deceleration before a bend, reducing occupant anxiety and preserving lane visibility.

Vehicle position and road condition data are combined on a server to identify road surface changes more reliably for road management.

GPS, live traffic light status, and machine learning recommend cruising speed to cut stops, wait time, and fuel waste at intersections.

Probabilistic occupancy and collision risk assessment helps autonomous vehicles cross parking lot intersections safely without unnecessary stops.

SLT obstacle constraints turn dynamic tracks into station and lateral limits, cutting planning complexity while improving AV avoidance accuracy.

Classifying remote vehicle operation requests by context and operator capability improves response time and handling reliability.

When a designated parking space is occupied, the vehicle searches for another available space and parks automatically without user intervention.

Time-series travel data is split around cognitive-load events so steering evaluation stays accurate without interfering with normal driving.

Pre-start detection of raindrops or dirt on in-vehicle sensors helps avoid automated valet parking interruptions and missed obstacle detection.

Pre-acquired curve boundaries guide an automated vehicle to stop before entry or after exit, reducing rear-end collision risk during failures.

Turn-signal-triggered time and distance gating limits reverse travel checks to likely scenarios, reducing errors from sign symbol misrecognition.

Machine learning detects oversized and other special vehicles from connected data, then sends nearby AVs targeted maneuver warnings.

Priority blind spot areas let vehicles request only the most useful V2X object data first, speeding decisions for drivers and automated driving.

Conditional control rights let recipients reposition an autonomous delivery vehicle when fences, guardrails, or slopes block package access.

Bayesian road user intent prediction extends collision warning beyond short-term detection, helping vehicles react earlier in dynamic traffic.