Character And Pattern Recognition TRIZ Cases

Predicted environmental states trigger sensor setting changes so autonomous vehicles keep inputs within range and avoid unusable data.

Sensor-based area grids score roadway segments by safety, convenience, and conditions to choose better AV passenger drop-off points.

Neural-network fusion of camera, lidar, and map data predicts changing stop locations at intersections for safer autonomous stopping.

Off-board sensor processing augments an ADS local world-view, improving environmental awareness without adding onboard power and hardware.

Driver field-of-vision data limits sensor scanning and evaluation to cut vehicle power use, computing load, and hardware wear.

A combined kinematics and dynamics controller maintains lane position from low-speed driving to high lateral-acceleration maneuvers.

Road surface images from door-side cameras let a driverless vehicle judge curbside stopping conditions for safer passenger disembarkation.

Low-power sub-short range active light sensing and sensor fusion improve lane boundary detection in adverse weather at lower cost.

Slit-enabled front bracket displacement adapts to windshield variation, preserving lens ventilation gaps and reducing mist risk.

Detected and predicted road friction refine following distance and speed alerts on slippery roads, improving warning and control accuracy.

A three-subnetwork HCNN detects lane locations and lane types from optical sensor images despite weather and debris interference.

A path prevention structure blocks stray reflected light in a vehicle cabin camera, reducing streaks and blown highlights for better face recognition.

Fuses LIDAR and camera data with lane-edge tracking and Kalman filtering to detect surrounding vehicle lane changes in congestion.

Auxiliary road marking availability helps intersection guidance adapt to vehicle size and oncoming traffic when GPS path generation is unreliable.

Camera-based AR overlays identify vehicle HMI controls and show live LED status explanations, reducing driver confusion and unnecessary data transfer.

Environment-based reset timing lets adaptive cruise control clear stored setpoints after longer stops while avoiding needless reactivation in stop-and-go traffic.

Real-time AR overlays link sensor-based load maps to camera-detected cargo, helping drivers balance vehicle and trailer weight without constant display checks.

Predefined camera ROIs align brightness and color before stitching, producing a uniform vehicle surround-view image with lower processing load.

Captured HMI images are matched with live vehicle status to overlay clear AR explanations for mechanical controls and LED indicators.

When one compound eye camera fails, the vehicle keeps automated travel using the remaining camera to recognize roads, obstacles, and vehicles.

Anonymized cabin monitoring transmits deformed or extracted passenger data only when no abnormality is detected, protecting privacy.

Cross-checking image streams from multiple AV sensors flags consecutive invalid frames early, helping isolate tampering before safety-critical faults spread.

An adjustable mirror keeps the driver's head in the camera's center view, preserving image resolution across varying seating positions.

Environmental sensors and map data help automated longitudinal control judge traffic lights and stop signs for safer, smoother urban driving.

When abnormalities arise on highways, the vehicle pre-identifies shoulders and merge points to choose a minimal risk stop that lowers collision risk.

A high-precision timing unit aligns autonomous vehicle sensors for simultaneous capture, improving multi-sensor data correlation and object detection.

Compares target speeds from driving-lane and adjacent-lane curvature to detect road branching and avoid unnecessary deceleration.

A detachable drone camera lifts vehicle vision above windshield and body obstructions to detect traffic conditions ahead more effectively.

Optical metasurfaces perform Fourier-domain CNN convolution with reconfigurable kernels, cutting power use while increasing speed.

Biometric and device-based driver classification enables or restricts ADAS, automation, and comfort features to match fatigue, intoxication, or skill level.

Calibration runs during driving only when processor load and environmental conditions allow, preserving precision without interrupting vehicle operation.

Sensors and AI controllers adjust stopping position to align with stop lines and avoid obstructing oncoming vehicles or pedestrians.

Map-based checks qualify autonomous vehicle camera signals by detecting corruption, sequence faults, and signature mismatches before fusion.

A stored virtual line is matched to detected parking boundaries to guide repeat parking with clearer alignment than simple physical markers.

Map-based imaging-area control adjusts vehicle position so a preceding vehicle does not block traffic light recognition or force an excessive gap.

A rotatable fin section and helical disk improve lateral soil resistance, speeding sign post installation without concrete.

Sensor analysis of snowy tire tracks helps automated vehicles judge slip risk and choose a safer trajectory when lane markings are hidden.

Segmented neural network layers cut retraining time and compute load while maintaining accurate in-vehicle safety event detection.

Region-specific vehicle models switch by location to improve perception and planning accuracy in unusual road layouts without one overfit model.

Thermal imaging and hopper temperature sensing detect a person early, trigger driver alerts, and shut down truck functions to prevent injury.

Image and radar data are collected before parking selection, letting a CNN find multiple usable slots in complex conditions and generate a parking track.

Fusing camera images with radar echoes improves drivable region detection when shadows, weather, or signal noise cause false alarms.

Wheel identification sensors estimate trailer wheelbase in multi-articulation combinations by locating active wheels and filtering unreliable measurements.

When cones or poles sit on lane lines, the assist path shifts inward using a virtual lane boundary to keep a comfortable clearance.

Camera feedback adjusts steering wheel height so the driver's full face stays in frame for accurate drowsiness and gaze detection.

Normal covariance filtering removes uncertain LIDAR points before ICP merging, improving 3D point cloud alignment and object detection.

A road-facing laser vaporizes or deflects water ahead of the tire to improve grip on wet, snowy, or icy surfaces.

Localized discriminator feedback replaces noisy global decisions, helping generator networks improve synthesized sensor data faster and more consistently.

An AR HUD separates back seat monitoring from driving data, letting drivers watch occupants, pets, and objects with less distraction.

A rearview mirror camera tracks driver and passenger posture and issues visual, audio, or haptic alerts when seating position becomes improper.