Character And Pattern Recognition TRIZ Cases

Portable trailer and toolbox modules solve surveillance compatibility and deployment limits with standardized mounting, cooling, and power integration.

By linking interior gaze sensing with exterior scene changes, this case refines driver attention assessment in unusual visual conditions.

Neural networks fuse lidar, camera, and map data to localize changing intersection stop points and update shared maps for self-driving vehicles.

Neural networks estimate a vehicle's future path from road images, improving ADAS response in dynamic scenes with shadows and moving traffic.

Sensor-based driver attention detection shortens or extends handover alerts during autonomous level changes to reduce annoyance and keep engagement timely.

By linking interior and exterior sensor changes to virtual focus points, this case improves driver gaze prediction in complex road scenarios.

Observation data and a state model are combined to estimate steersman cognitive load more accurately for safer transportation.

Baseline object detection distances are compared with live sensor readings to adjust autonomous vehicle control under sensor error and weather changes.

Camera panning and image-based trigonometry estimate trailer length, maintain road view, and detect nearby collision risks.

Pre-trip route analysis identifies available driving support functions and tailors guidance to driver use history to reduce redundant alerts.

Cross-checking map-based and sensor-based lane tracking detects guidance errors and triggers timely driver takeover on multi-lane roads.

Gaze checks at traffic lights reveal driver confirmation status before irritation shows in vehicle behavior, enabling earlier assistance adjustment.

Integrated wheel sensing uses radar, imaging, and alcohol detection to trigger color warning lights when driver or vehicle abnormalities are detected.

Continuous road recognition data is used to rate map reliability before route deviation, improving timely autonomous driving decisions.

Adaptive lateral control adjusts trajectory parameters to limited road visibility, helping vehicles stay within lane limits.

Two-stage neural training combines object-detection pretraining and reinforcement learning to improve autonomous driving reliability with lower complexity.

Image-based driver monitoring detects drowsiness and distraction from facial cues, then issues alerts to mitigate unsafe vehicle operation.

An in-cabin camera and neural network classify driver drowsiness from image sequences, enabling vehicle alerts that improve driving safety.

Driver behavior is learned to estimate acceptance of assistance, so vehicle support control intervenes mainly when risk and receptivity align.

Camera and LiDAR scores are fused by separate neural networks to keep external object identification stable when one sensor fails.

A dual-model trajectory pipeline corrects candidate paths with scene analysis to handle obstacles and improve autonomous driving accuracy.

Cuts vehicle object-detection load by omitting classifier computation at low speed unless exceptional situations require full processing.

Sliding-window histogram checks verify image correspondences with fewer memory accesses and less computation on high-resolution images.

Historical driver reactions guide ADAS warning type and intensity, improving alertness in critical situations without relying on fixed alerts.

Similarity scoring between camera images and LiDAR point clouds preserves object recognition and driving stability when one sensor is erroneous.

A projected laser line and camera classify pothole severity from roadway beam distortion, improving low-visibility hazard alerts.

Photorealistic 3D traffic scenarios are perturbed to create impact and near-impact cases, expanding rare-event data for ML training.

Touch and gesture input shift a selectable camera image region, replacing mirror actuators to cut cost, wear, and complexity.

A switchable reflective-transparent mirror replaces motors to speed side-mirror adjustment, reduce wear, and preserve visibility in low light.

Stationary cabin image analysis confirms infant presence in a child seat and cuts false alarms from door-based left-behind detection.

A single light source serves TOF object recognition and optical proximity detection, cutting components while controlling emission safely.

A camera-guided cockpit folds the steering wheel and reshapes the touchscreen to save cabin space and limit driver distraction.

Side-mounted mirror cameras detect lane lines from a lateral view, improving autonomous lane centering in traffic and reflective road conditions.

Scale-aware depth and pose learning estimates multi-camera extrinsics from image sequences and velocity, avoiding manual calibration and extra sensors.

Eye movement baselines help detect diminished driver control more accurately than breath tests, with less inconvenience and harder circumvention.

Excluding steering wheel and ornament regions from cabin camera data prevents false face detection and improves occupant recognition accuracy.

Lowering the acceleration limit when a stopped vehicle has a large steering angle reduces shaking and improves ride comfort.

An oblique camera behind the rearview mirror balances driver and passenger coverage while improving light transmission despite mirror adjustment.

Multiple correlated and de-correlated signatures improve road element classification accuracy while limiting runtime complexity.

Driver gaze and steering input are checked before control handover, reducing unsafe Level 3 transitions when autonomous driving fails.

Past recognition positions from another driver are used to time gaze alerts so important roadside features are less likely to be missed.

Front-mounted cameras and image recognition detect hazards ahead of side mirrors and warn drivers about road risks in the vehicle's path.

Embedded seat electrodes and wearable sensing predict driver alertness in real time to trigger alerts, adapt vehicle control, and aid emergency communication.

Cross-correlated camera and lidar object queries improve 3D bounding box tracking accuracy in complex vehicle scenes with moving objects.

Driver and surroundings sensors verify signal status and driver gaze before automated vehicle launch, improving comfort without weakening safety.

Auto-labeling and signature-based correction fix vehicle perception classification errors in real time without retraining or changing the neural network state.