Character And Pattern Recognition TRIZ Cases

Uses a domain classifier on convolution outputs to suppress domain bias and improve image classification across seasons, weather, and sensors.

By merging offset delimiting point sequences and applying rotational correction, this case extends lane-line estimation distance while maintaining accuracy.

An integrated rearview mirror camera and dual near-IR emitters simplify installation while adapting driver illumination for LHD and RHD cabins.

Joint training across distraction, mobile, face, and landmark tasks improves real-world driver monitoring accuracy while cutting false alarms.

Multi-focal image analysis detects shield contamination and triggers camera cleaning while supporting more accurate vehicle object recognition.

By comparing overlapping front and surround camera views, the system corrects dashboard glare and windshield fogging for clearer recognition.

A three-subnetwork HCNN improves lane line location and type prediction when weather and debris interfere with optical sensing.

A look-ahead lane ROI filters detected actors by relevance, cutting AV perception load while preserving motion planning accuracy.

Back-camera seat detection identifies when a mobile device is used from the driver's seat and restricts operation while allowing use in autonomous driving.

Driver gaze and sensor-detected candidate vehicles help identify the right lead vehicle at intersections, avoiding abrupt braking and mis-following.

Switching between sensor processing modes cuts LiDAR and radar noise in adverse weather, improving object detection for vehicle control.

Real-time lane segmentation updates nominal paths and trajectories so autonomous vehicles can handle construction, narrowed lanes, and missing markings.

A neural algorithm predicts future driving risk from current vehicle data, enabling warnings 3-4 seconds before critical situations develop.

Adaptive normal ranges from normal-travel road images help detect unexpected surface obstacles that fixed classifiers can miss.

Trigger conditions flag and synchronize vehicle sensor events for fast user labeling, cutting manual ADAS data review time.

Graph neural road parsing predicts drivable paths from topographical road segments, cutting map data volume and update burden for autonomous navigation.

One cabin camera tracks both driver facial state and powered window position, cutting sensor cost while enabling automatic comfort and noise control.

Detects when a leading vehicle blocks lane markings, then uses vehicle position and past line data to keep steering and speed control accurate.

Uses parametric target-lane modeling and local waypoint sensing to execute smooth lane changes without GPS or HD maps.

Combining road-image recognition with magnetic positioning elements helps autonomous vehicles handle curved tracks without GPS instability or frequent map updates.

Association-weighted radar measurements improve vehicle object state estimation, reducing parameter jumps and systematic deviations.

When passengers miss blocked external objects, gaze, gesture, and camera views can surface an unobstructed display automatically.

A sliding in-vehicle camera extends for driver monitoring and retracts during parking to protect privacy and save installation space.

Sensor fusion detects distracted driving and approaching incident-prone locations, then issues timely alerts to reduce crash risk.

Radar point cloud analysis classifies vehicle occupants by size and position, enabling automatic adjustment of restraints and audio limits.

Real-time vehicle data capture with trigger activation and external identification speeds emergency response while reducing occupant interaction.

Automated pull-out steering keeps the vehicle moving into the road region, reducing collision risk during handover to manual control.

Semantic map objects align camera and other sensor data to improve vehicle localization, calibration consistency, and tracking accuracy.

Aerial-view transformation and curve fitting improve lane line recognition accuracy under external interference in intelligent driving.

Adaptive visual and audio cues vary saliency by driver cognitive state to improve hazard attention while limiting false alerts and load.

Onboard cameras compare wheel images with baselines to detect alignment shifts, wheel damage, and post-obstacle issues in real time.

Segmenting LIDAR road point clouds into patches improves slippery surface estimation ahead for vehicle control and path planning.

Synthetic infrared training images let AI detect switchgear hot spots across breaker geometries without manual calibration or region setup.

Passenger seat counts and camera images are combined to quickly confirm whether the driver is in the driving seat when face detection is delayed.

Reflected ultrasonic signals and a neural network classify road surfaces at low cost, improving black ice detection beyond noisy friction estimates.

Probability-based terrain costs help autonomous vehicles distinguish path, non-path, and uncertain regions for better off-road routing in variable light.

Gaze, body movement, and phone screen state are combined to vary driver warnings, allowing navigation while flagging riskier mobile use.

Vehicle sensor trace and key points are scored against hypothetical lane layouts to map lane count, width, and orientation accurately.

Stereo image sequences and a neural network improve vehicle ego-motion detection, capturing small position and orientation changes beyond odometry limits.

Adaptive eyelid-threshold resetting after driving interruptions improves sleepiness detection accuracy without long detection downtime.

Parallel CNN feature extraction and attention-based queries predict vehicle speed at multiple time points with less delay than serial RNN schemes.

Continuous recording and path merging preserve usable forward segments, so assisted reversing can start later without extra manual setup.

Camera and millimeter-wave radar are combined to detect hidden or shifted vehicle occupants more accurately when the vehicle is stopped.

Automatic occupant identification and meeting handoff let vehicle users join scheduled sessions across platforms with fewer distractions.

Distributed zone ECUs share vehicle sensor recognition by driving mode, cutting power load and reducing single-point failure risk.

Adaptive control delays lane keeping reactivation near a second lane boundary, reducing steering interference during lane changes.

A protected cut-and-etch sequence exposes conductive pads in optical sensor fabrication while preserving pad integrity, yield, and reliability.

Roadside and vehicle sensing data are fused with confidence weighting to extend perception range and improve object detection for self-driving vehicles.

Alternating camera and sensor operation by road conditions keeps vehicle 3D mapping reliable in bad weather without constant radar processing.

Dual camera signal paths let cruise control keep monitoring the surroundings and switch modes automatically when one camera system fails.