Character And Pattern Recognition TRIZ Cases

Fusing distraction, hand-position, and reaction-time parameters helps estimate driver takeover readiness for safer automated-to-manual transitions.

Multi-orientation sensors maintain lateral vehicle position when forward sensor data is blocked, improving lane localization continuity and control.

Rear camera edge detection calculates tractor and trailer alignment, avoiding markers or laser sensors while handling varied drawbar shapes.

Clustering participants by trust dynamics across interaction phases improves human-agent trust prediction without requiring individual-level data.

A rearview mirror camera tracks driver posture against a stored head-position baseline and triggers alerts to reduce fatigue-related safety risks.

Moisture sensing and vehicle-status control block ramp deployment in bad weather and retract it during motion or reversing.

Modulated near-infrared illumination lets the camera separate driver eye and head signals from sunlight washout for reliable attentiveness monitoring.

Multiple tractor-mounted LiDAR, radar, and camera sensors maintain trailer-side coverage and accurate positioning even when tractor and trailer are misaligned.

A displaceable cargo-space camera cuts the need for multiple fixed cameras while improving object identification and positioning during loading.

ADAS and a learning model tune regenerative braking to driver habits, improving coasting feel, smoothness, and energy recovery.

Multi-sensor fusion and machine learning predict electric scooter motion so vehicles can issue timely alerts and avoid conflicts near schools.

On-vehicle mining flags ambiguous lane-marker images for labeling and retraining, improving model learning while cutting data transmission.

Sensors detect when passengers or cargo block rear or side mirrors, then switch to camera views and alert the driver to persistent obstructions.

Combining weak labels, sensor data, and simulation data improves vehicle behavior classification in unseen driving scenarios.

Map-layer localization compares LiDAR and visual sensor poses in a common frame to validate vehicle calibration during normal operation.

Adaptive mapping of boundary measurement points to spline samples improves free space approximation while reducing control points, memory, and compute.

Interior camera gaze tracking, odometry, and environment sensing are combined to confirm turn intent and avoid incorrect indicator activation.

Driver-state sensing lowers collision avoidance confidence thresholds when alertness drops, enabling earlier braking without broad false positives.

A prediction model forecasts future vehicle states on upcoming road sections so braking or steering can prevent stability limit violations.

Motion-triggered smartphone blocking cuts driver interaction, scores driving behavior, and sends real-time safety data to interested parties.

Images are captured just after wiper sweeps so multi-camera depth maps stay clear and consistent for vehicle control in bad weather.

Forecasts future vehicle states from road layout and dynamics to detect stability-limit violations early and trigger braking or steering correction.

A shared-backbone multi-task model improves real-world driver distraction detection by learning face, body, and landmark cues while reducing false alarms.

Triggered camera and sensor capture records safety violations in real time while limiting continuous video transmission and energy use.

Sensor-detected lane line types are checked against map data so vehicles change lanes only where current road markings still match.

Data in transport memory gets type-based access time limits and automatic clearing, balancing availability, memory use, and security risk.

A lower second camera detects stop lines hidden by preceding vehicles, enabling timely driver alerts near temporary stop signs.

A camera reads circular marker aspect ratios to locate mobile robots accurately without costly sensor arrays or machine learning.

Neural-network road type detection and database checks adjust minimum braking distance to cut unnecessary safety margins in autonomous driving.

Camera and ML analysis of facial expressions and gaze helps detect cannabis-impaired vehicle occupants and trigger safety alerts.

When camera lane detection becomes unreliable, stored images and CNN-LSTM prediction maintain stable lane-keeping steering.

FMCW LiDAR and neural detection track road wear reference lines to maintain accurate vehicle lateral localization when GPS or lane markings fail.

When map data conflicts with lane recognition, control mode switching and longer follow travel help maintain safe automated driving.

User-specific display layouts are retrieved from a server and adapted by vehicle type, avoiding manual re-customization in different vehicles.

Camera-guided steering and braking keep a vehicle on course and move it to a safe stopping point during blowouts and road changes.

Coordinate-transformed friction data is mapped onto camera or lidar perception data to create accurate road-friction training samples.

Maps external structures and device orientation into a visual spatial model for precise remote positioning and movement planning.

A proximity sensor and controller detect the driver's operating hand and gesture direction for touch-free navigation input with less driving distraction.

Maps vehicle perception data to measured road friction through coordinate transformation, creating precise training data for friction estimation.

Locally captured camera and lidar data identify lane segments and observables to update driving rules in real time as roads change.

An RNN trust model tracks short- and long-term driver trust states to predict take-over intent and adapt vehicle automation behavior.

Multiple onboard signals verify whether the operator is seated during machine operation, reducing false alerts and warning nearby people.

A generator-discriminator loop adapts sensor data across sensing conditions, cutting retraining time for autonomous vehicle simulation and deployment.

Road-surface detection suppresses wheel force ride-comfort control at rumble strips so vibration and sound still alert the driver.

Weak-link pruning splits multi-sensor track pairs into smaller clusters, cutting association workload in cluttered automotive scenes.

By splitting long-horizon paths into semantic mode segments, this case improves autonomous vehicle trajectory prediction for complex maneuvers.

A resonant AC drive boosts face tracking sensor output while duty cycle feedback holds target amplitude and cuts power loss in MR wearables.

Personalized external displays identify the approaching user and adjust information by distance to improve entry comfort and vehicle attachment.

Camera fault detection triggers a shift from low-workload automated driving to a higher-driver mode, helping maintain safe vehicle control.