Controlling Traffic Signals TRIZ Cases

See how a home automation system integrates weather data with barrier and appliance control to

Retro-reflective markers let the controller verify restraint engagement in real time, improving ride safety checks and throughput.

Maintains roadside vehicle assistance during faults by keeping signal reception, data processing, and transmission active with error-tolerant architecture.

Quantile regression predicts minimum, likely, and maximum traffic light switch times, giving vehicles more reliable SPaT data for safer control.

Roadside units use V2X messages to adjust beam intensity, range, and high-low switching, reducing glare and improving safety.

Sensor-based crosswalk lighting reads pedestrian intent and vehicle behavior to signal safe crossing conditions and reduce traffic hazards.

RF-tagged electronic footwear enables blind-corner pedestrian detection and sends audible, visible, or tactile collision warnings.

Wireless vehicle and infrastructure signals trigger lace-motor tactile alerts in electronic footwear to warn pedestrians at blind corners.

Coordinates autonomous and non-autonomous vehicles through sensor-driven commands and feedback to improve traffic synchronization and roadway safety.

Emergency vehicles can power traffic light control boxes through a cable connection, keeping signals operating during outages and storms.

A confidence-based fusion of map data and onboard image analysis improves speed limit detection when signs are obscured or road data is outdated.

Camera-based traffic light state prediction and trajectory relevance reduce map data burden while improving autonomous navigation accuracy.

Switchable metasurface and corner reflectors raise radar visibility of low-RCS hazards at intersections, improving detection in adverse weather.

User driving profiles set speed, route, and style parameters, while real-time user condition data lets autonomous operation adapt when preferences no longer fit.

Networked footwear tags warn pedestrians of vehicles at blind corners or behind obstructions, reducing line-of-sight detection misses.

Color cues on road signs and traffic lines guide vehicles directly on the route, reducing screen checks and route-matching errors.

Camera-derived free-space markers update road models with only relevant changes, improving autonomous navigation accuracy without heavy map data.

Risk-area notifications narrow speed, acceleration, spacing, and lane-change limits so automated vehicles can plan safely with lower processing load.

A pole-mounted pressure plate lets pedestrians, wheelchair users, and cyclists activate crosswalk signals without hand contact or false wireless trips.

Pedestrian signal sounds and location cues help mobile objects infer go or stop states for safer control in non-road areas.

Real-time event data lets traffic devices, vehicles, and mobile systems coordinate signal changes and distracted-driving responses to improve road safety.

A control board in each signal head switches battery power to a flashing red LED during AC loss, avoiding dark intersections after cuts or theft.

Electrical connection changes reveal potential breaks in curved traffic displays early, prompting users before display failure affects driving safety.

Predicted link quality guides platoon speed and spacing changes to cut fuel use while preserving safety margins and communication reliability.

Camera-based extraction of traffic lights, lane marks, and free spaces cuts map data volume while preserving autonomous navigation accuracy.

A mixed-integer controller coordinates CAV motion and smart traffic signs to guide MCVs, cutting travel and idling time while improving flow.

Lane-wise probe data and Time-Space Diagrams help identify the least congested toll lane for navigation or semi-autonomous control.

Proximity-based footwear control triggers wireless alerts and authentication with vehicles, security systems, and home devices without manual input.

Smart contract rules turn IoT vehicle feeds into priority scores, enabling co-located vehicles to reposition and clear a priority lane.

Queue prediction and signal timing data guide vehicle speed to cut stop-and-go driving, lowering urban energy use, emissions, and delay.

Predicted downstream traffic and V2I speed advisories help vehicles avoid red-light stops, cutting braking, idling, energy use, and emissions.

Location-aware footwear uses wireless alerts and secure authentication to help users identify and interact with nearby vehicles.

Vehicle behavior in V2X messages is checked against SPAT data to score signal reliability before traffic-light-based actions are used.

Location-aware footwear triggers visual or audible alerts as a vehicle nears, helping drivers identify riders in crowded pickup areas.

Tracks nearby user terminals as companion pedestrians and adjusts crossing assistance when one moves to the opposite side of the road.

When visual signal reading is limited, auxiliary pedestrian cues such as alert sounds help mobile bodies detect stop or go states in non-road areas.

Vehicles are grouped by waiting time in sidelink road control sections to balance traffic flow and equal driving opportunities.

A roadside device schedules passing sequences for multiple vehicles, cutting onboard game calculations in narrow lanes and dense traffic.

Switchable metasurfaces let radar traffic signs reflect or absorb signals, improving intersection collision avoidance in poor visibility.

Automated footwear alerts use proximity-based location tracking and wireless signals to help drivers identify riders in crowded pickup areas.

Location-aware footwear controllers trigger alerts and external signaling when a user nears a remote node, improving hands-free interaction and safety.

Location-aware footwear control triggers light, audio, or haptic alerts and authentication when the user nears a vehicle or access node.

Dynamic vehicle weighting in a geo-fenced bottleneck coordinates speed and state control to ease congestion and clear priority vehicles.

Camera-based lane-mark detection compares expected and actual lateral distance to guide steering while reducing map data and onboard processing.

By extracting lane marks, arrows, traffic lights, and free space, this case cuts map data volume while improving autonomous navigation accuracy.

V2X and onboard sensors detect red-light violators beyond line of sight and warn cross-traffic vehicles of collision risk.

Real-time telematics alerts nearby vehicles to abnormal traffic conditions while balancing safety monitoring with battery-saving data transmission.

RF-tagged electronic footwear warns pedestrians of vehicles beyond blind corners by using infrastructure or vehicle prompts instead of line-of-sight sensing.

Predictive onboard sensing generates virtual safety messages for occluded or non-connected vehicles and pedestrians at intersections.

Gesture recognition linked to stored user profiles adapts vehicle functions for more intuitive, reliable, and personalized in-cabin control.

Queue-aware launch timing uses vehicle position, queue length, and situational data to reduce stoplight delay without undermining driver trust.

RF-tagged footwear links with traffic infrastructure to detect pedestrians in obstructed areas, reduce false negatives, and trigger timely warnings.

A centralized scheduler reroutes autonomous vehicles to event locations, improving image coverage when cameras are occluded or fail.

Dedicated roadways, area controllers, and vehicle assignment logic increase urban transit capacity while cutting congestion, wait times, and cost.

Dispatched UAVs mark a truck maneuver zone in real time, warning approaching vehicles to reduce collision risk and traffic disruption.

Camera and radar roadside sensing adjusts green-light duration to match real-time junction flow and reduce congestion from fixed cycles.

Concentric LED arrays shift colors gradually to give drivers advance visual warning of upcoming intersection signals.