Road Vehicles Traffic Control TRIZ Cases

When low-speed automated maneuvering detects a collision risk, the vehicle brakes, changes direction, and reverses along its path to avoid impact.

By splitting a leaned corner into riding phases, the system improves traveling-state and skill evaluation accuracy without raising hardware load.

Real-time signal timing and vehicle movement analysis predicts whether a vehicle can clear a congested intersection before red, reducing blockage and delay.

Computer vision and vehicle signals predict neighboring lane-merge behavior, enabling real-time speed and trajectory control to avoid collisions.

Collision risk is estimated across vehicle position hypotheses to set the highest safe HAV speed with lower real-time computing demand.

Directed graph segmentation classifies critical traffic situations from vehicle environment data, improving scenario selection and simulation.

Camera and sensor data detect road changes and phone use, enabling timely driver alerts with lower cost than specialized monitoring setups.

A signal selection unit blocks unauthorized vehicle commands and passes forced control signals to prevent unstable anti-theft conflicts.

Radar or lidar position vectors reveal road flatness exceptions, enabling pothole and speed bump detection in low light and vehicle reverberation.

Time-series battery discharge logs reveal unusual electric mover usage, enabling earlier fraud detection in shared rental scenarios.

Map-based geofencing suppresses false in-vehicle speed alarms from confusing road signs while giving drivers early warnings before critical zones.

Semantic markers from rider photos or videos help autonomous vehicles identify precise, adaptable pickup points and reduce passenger confusion.

GNSS signal integrity assessment lets driver assistance adapt sensor weighting and maintain usable vehicle positioning when satellite data degrades.

Driver trigger requests near target points let the vehicle pass or stop at intersections when traffic signal recognition is uncertain.

Radar range and camera ground-point fusion estimate road elevation, improving vehicle navigation without relying on heavy map storage.

Warning obtrusiveness is adjusted from driver gaze, GPS, and motion data so missed speed-limit and road-constraint alerts are more noticeable.