Road Vehicles Traffic Control TRIZ Cases

Projects crossing-vehicle warnings as simulated headlight beams so drivers can grasp vehicle location and direction faster.

Dynamic sound cues vary frequency, pitch, and direction to match surrounding traffic situations and improve acceleration or deceleration guidance.

Vehicles exchange position and object data to cut redundant sensing and computation while maintaining autonomous navigation reliability.

Historical lane-level driving data is mined into lane-change, speed, and path features to improve autonomous vehicle control safety.

Chronologically gated light pulses and sensor capture help detect rear objects, estimate spacing, and support safer lane changes and merges.

Radar feature-vector classification improves long-range route drivability checks by filtering distorted reflections and validating with short-range data.

Switching from separation steering to deceleration based on object path entry helps reduce vehicle collision risk with pedestrians.

Fusing V2X data from vehicles and roadside terminals with onboard sensing extends perception range and improves detection reliability in blocked scenes.

Predefined fallback tasks and trigger checks let an autonomous vehicle detect faults and drive itself to service or reconnection.

A mirror-mounted DMS camera moves with the mirror head, while image processing adapts to adjustment to keep driver monitoring accurate.

State comparison between onboard and remote vehicle assessments triggers alerts only on mismatches, reducing operator switching and delays.

Multiple sensor inputs and anomaly rules help autonomous vehicles detect malfunctioning traffic lights and navigate intersections safely.

Road-edge and side-road sign position checks help vehicles ignore irrelevant speed limits before updating control functions.

V2X-guided side mirror adjustment helps drivers spot crossing or merging vehicles during turns without manual mirror changes.

Dynamic lane-segment encoding and attention-based neural networks improve near-future road user intention and trajectory prediction for ADS.

Additional speeding alerts are triggered only when vehicle position and speed conditions indicate rising risk, improving reminders without constant annoyance.

Two repositionable vehicle sensors detect surround-view disturbances and shift position to restore reliable object detection without extra sensors.

Exterior lights show vehicle onboarding and connection status in real time, helping operators diagnose marshaling faults and avoid congestion.

Wireless comparison of nearby driving behavior guides a vehicle toward compatible traffic groups, improving comfort and localized flow matching.

Map-based speed limit data sets a legal operating speed for automatic lane changes, improving usability without violating road regulations.

When registered and detected parking spaces overlap, display priority shifts to the normal space to improve visibility and user selection.

Good-weather road video is matched to vehicle position and overlaid with nearby vehicle imagery to improve recognition in poor visibility.

A Frenet-frame space-time corridor with convex bounds cuts trajectory optimization complexity while improving obstacle handling and path smoothness.

A vehicle mesh network relays location data through proxy cars, keeping autonomous parking and summoning active where cellular coverage is unavailable.

Predicting swept area from vehicle geometry and sensor error helps keep articulated vehicles within drivable space during safe stop maneuvers.

When dust blocks LiDAR terrain sensing, the vehicle maps undetected zones and updates its route to avoid unnecessary stops and lost efficiency.

Region-specific ghost rules from trace data let autonomous vehicles predict habitual pedestrian and cyclist behavior beyond current traffic rules.

Environmental data adjusts lane-boundary debounce time so steering assistance activates sooner in clear conditions and stays reliable in poor visibility.

Cumulative multi-camera images are synthesized along the vehicle route to reveal blind spots and distant parking surroundings during long-range parking.

Static maps and nearby pedestrian velocities are combined to predict future pose and motion more accurately for collision avoidance.

External field monitoring verifies autonomous driving control commands and feeds back corrections to reduce collision risk.

Driver gaze and surrounding-scene recognition govern brake hold release to prevent sudden starts during two-stage stops at blind intersections.

Audio-based emergency vehicle localization and trajectory simulation help autonomous vehicles adjust paths safely without unnecessary stops.

Traffic light timing is shown through segmented vehicle lights, helping nearby road users improve flow without disrupting legal lighting functions.

Edge nodes index and enrich vehicle sensor data locally, cutting cloud transfer volume, communication bottlenecks, and compute load.

Sensors detect nearby walls and trigger alerts or automatic stopping to hold a preset parking buffer and prevent vehicle contact.

Estimated arrival times for the ego vehicle and circulating traffic enable earlier roundabout entry decisions and timely deceleration or waiting.

TTCD cluster position in a lane guides autonomous vehicles to bypass, change lanes, brake, and adjust speed with greater safety.

Temporal sequence correlation from angled photoreceptors filters road-surface interference for accurate vehicle speed and sideslip detection.

Compares own-vehicle and circulating-vehicle travel times to judge roundabout entry early enough for deceleration or waiting.

Obstacle trajectory matching to lane center lines identifies passable intersection lanes more accurately than traffic-light logic alone.

Remote map data on nearby dynamic objects lets vehicle safety systems lower activation thresholds earlier in poor-visibility collision scenarios.

Automatically generated behavior data trains a DNN to predict world-space trajectories, reducing labeling effort and smoothing control across vehicles.

Real-time advisory speed control uses traffic and environmental data to balance legal limits with safer, smoother vehicle flow.

When no space is available beyond an intersection, the controller triggers a low-lateral-vector lane change to keep the vehicle from getting stuck.

Separates mixed vehicle audio into elemental sounds, redacts speech, and localizes critical signals for safer autonomous driving.

Surroundings-based lateral and longitudinal control assists lane merging while safety checks and redundancy improve automated driving reliability.

Aggressive driving is reported only after checking whether the first vehicle was following traffic rules, reducing false alerts to agencies.

A dynamic second follow-gap buffer smooths ADAS acceleration while preserving the driver-set gap in unstable traffic and cut-in events.

Mixed-resolution short- and long-range trajectory candidates cut vehicle control computation while preserving precise, comfortable path updates.

Vehicles are assigned parking positions by autonomous movement level, separating similar capabilities to improve lot flow and space use.

Predicts sidewalk-to-roadway entry risk for pedestrians and bicycles to adjust speed, steering, and warnings for smoother hazard avoidance.

Fusing multi-sensor evidence into layered road grid cells improves roadway modeling accuracy while quantifying uncertainty for path planning.

Predicting obstacle-vehicle intent and convergence timing helps autonomous vehicles choose yielding or overtaking strategies at intersections.

Prioritized sensor data from networked collection agents cuts bandwidth load while preserving precise road mapping and AR driving guidance.

Sensors and staged nozzles meter a fixed wash-water volume into the truck barrel, protecting concrete consistency during cleaning.

Staged sound and vibration alerts help drivers regain control safely in autonomous vehicles by adapting takeover timing and intensity to road conditions.

Adjusts pedestrian risk regions using sidewalk and roadway width so vehicle speed and steering stay safe without excessive caution.

Detects private-public road transitions and adds traffic data so automated driving can continue safely without abrupt stops.

External computing and sensing guide parking maneuvers, cutting onboard hardware cost while enabling collision-aware safety measures.

Wireless activation signaling links warning systems to RSUs without a traffic signal controller, enabling solar- and battery-powered deployment.

When large vehicles block side views, exterior displays relay sensor-detected pedestrian warnings to following drivers to help prevent passing collisions.

Uses trained inverse radar sensor models to assign occupancy probabilities despite indirect reflections, improving obstacle and free-space detection.

Grouped representative trajectories cut combinational load, enabling real-time multi-vehicle collision-free avoidance maneuvers.