Character And Pattern Recognition TRIZ Cases

Occupant identification and attribute matching tailor in-vehicle function introductions, reducing unnecessary guidance and timing playback for safer use.

Fused BEV heatmaps and statistical filtering improve autonomous vehicle lane tracking accuracy and reliability in dynamic environments.

On-sensor ANN inference cuts raw pixel transfer to the host, easing bandwidth and CPU load while supporting higher image frame rates.

Parallel neural network streams detect objects and predict behaviors at the same time, cutting latency for faster vehicle response.

Near-infrared cabin imaging detects occupant posture and triggers alerts for poor seating that may affect driving comfort and safety.

A shared machine learning model transfers route planning from higher-spec sensors to lower-spec vehicles, cutting re-adaptation effort.

Navigation and camera data guide lane changes near road diverging points, avoiding HD maps and lowering computing demand.

Statistical filtering and multi-head heatmap detection improve AV lane tracking accuracy under sensor failures and changing road conditions.

Predicting shoulder-entry or exit maneuvers without turn signals lets blind spot alerts intensify when a following vehicle creates collision risk.

Multi-camera gaze tracking infers driver intent to control in-vehicle AI functions without speech or button presses, cutting distraction and delay.

Machine learning combines acceleration, steering, and camera data to estimate driver cognitive and driving traits with higher accuracy.

Camera-based vehicle status images are sent to a user's terminal during unattended parking to reduce anxiety without interrupting automation.

Automated image analysis of thermal camera data detects brake disc hot spots and temperature evolution faster and more objectively than manual review.

When lane markings disappear, the system uses driver-guided centerline input to create a virtual lane and maintain lane keeping.

Road marking recognition identifies neutral zones and suppresses lane departure warnings or intervention to avoid excessive actuation.

Sensor-driven discomfort predictions for surrounding agents are aggregated to guide autonomous vehicle path planning when external agent data is limited.

By moving closer to a detected reference object, the vehicle improves nearby object recognition and updates the travelable area to avoid collisions.

Driver start-stop inputs are relayed through a vehicle control interface box so the autonomous driving system can track power state changes and avoid malfunctions.

A movable mirror-mounted camera shifts sensor or lens position to keep the driver's eyes and hands in view across seating variations.

Curvature-change compensation calibrates road line recognition during peak phenomena, helping autonomous vehicle travel control stay accurate.

Position and azimuth checks trigger parking assistance only near the taught route start, reducing delay and unnecessary user prompts.

Calibration-linked sensor graphs preserve spatial data across camera, radar, and LiDAR inputs while cutting grid processing overhead.

Separate sensor graphs joined by calibration matrices preserve cross-sensor spatial data and avoid grid-based information loss in perception.

Auto-labeling detects vehicle perception misclassifications in real time, while signature-based fixes correct them without neural network retraining.

Sensors identify loose cabin objects and trigger vehicle configuration changes to reduce airborne impact risks during collisions.

When lane markings are unclear, swarm data overrides conflicting sensor lane types to keep lateral vehicle control aligned and avoid oncoming-lane risk.

Real-time selection of effective lane markings improves lane change route accuracy when distant markings are unreliable or blocked.

A virtual center line between double traffic lines guides autonomous vehicles to the true lane center and improves driving stability.

Non-contact thermal imaging and motion analysis estimate occupant awakening level even when hand grip position changes.

When lane perception range shrinks, the controller moves the look-ahead point closer to maintain stable vehicle trajectory and delay driver takeover.

Graph-based message passing predicts relationships among detected objects, improving autonomous navigation response in complex environments.

Geometric road-feature data and camera pose replace pixel color input to build accurate HD maps with faster updates for vehicle guidance.

A combiner in laminated vehicle glass projects side-view camera images onto the windshield to reduce blind spots and sideways glances.

Fiducial lines guide a neural network to locate objects more accurately in real time while cutting computation and processing time.

Synthetic driver overlays on new vehicle interiors expand rare-case training data while reducing collection time and bandwidth use.

Selective load cycling with current, voltage, and temperature data trains models to predict vehicle wiring harness and electrical load failures early.

A receiver and processor compare recognized and reference areas to reduce or stop TOF light when concentration risk is detected.

Opposed front and rear light modules create overlapping side illumination to improve detection of lane markings and obstacles for ADAS.

Combining in-cabin camera images with microphone audio helps detect inattentive drivers in low light and trigger timely visual or auditory reminders.

Image-based hand and sensor tracking verifies that the driver, not a passenger, triggers safety measures while monitoring physical condition.

Onboard cameras verify hand, face, and sensor operation to control airbags or seatbelts only when the driver is fit to operate safely.

Combining driver state and environmental perception improves ODD compliance assessment accuracy and reduces unsafe ADS handovers.

Lane-based reference selection bridges map and past trajectory data to improve surrounding vehicle prediction accuracy for autonomous driving.

Swarm trajectory data is validated against sensed road boundaries to keep driver assistance available when lane markings are unclear.

Distance-based switching from isometric to top view keeps the trailer coupling visible during hitching and helps drivers avoid collisions.

Calculates non-overlapping cargo layouts inside a vehicle and shows validated loading configurations to cut trial-and-error and loading time.

Road reflection sensing lets the vehicle suppress projected road images when visibility drops or glare risks increase for others.

Real-time vehicle and occupant sensor data drives GUI updates and function changes to improve comfort, safety, and convenience.

A test projection checks projector suitability before road image rendering, preventing inaccurate signals to drivers and pedestrians.