Scene Recognition TRIZ Cases

A leading vehicle camera feed is overlaid with a 3D vehicle model to restore distance cues in fog and reduce the vacuum effect.

Early V2X obstacle alerts and onboard sensing enable smoother merge deceleration, reducing sudden braking near main-road entry points.

Surrounding-object recognition delays fluid spray or switches to air cleaning to keep vehicle sensors clear without contaminating nearby people or vehicles.

Moving visual objects into upper or lower peripheral display regions helps remote drivers sense vehicle speed without blocking the central surround view.

Ramp-history and vehicle-following cues help distinguish limited-access highways from local roads when slow traffic obscures speed-based detection.

Edge-based zone processing separates vehicles from shadows and corrects camera misalignment to cut false lane-change warnings.

A drivable-area model flags road-surface objects outside known classes, improving autonomous driving without heavy manual annotation.

Radar reflections are matched to virtual tilt boxes to estimate motorcycle lean angle and support accurate, non-distracting hazard alerts.

By narrowing image analysis to door candidate regions, the vehicle detects open doors faster and navigates safely with less visual data processing.

Distance-labeled camera training lets a monocular model classify following gaps and keep a suitable distance without extra sensors.

Size-based filtering and spatial clustering help LiDAR detect and track small traffic equipment that standard object pipelines often miss.

Adaptive Kalman weighting helps a following vehicle track a turning or decelerating lead vehicle more smoothly while maintaining safe distance.

Sensor-based control sets overtaking speed from obstacle motion, lane encroachment, and lateral gap to reduce discomfort during biased driving.

Superimposed warning-object icons let users judge real-space distance in a vehicle camera view and set warning thresholds more accurately.

Multiple camera positions turn curb and raised-object height detection into a simpler 2D geometric process with distortion correction and lower compute load.

Image-based child risk assessment uses height, age, attributes, and position to trigger timely autonomous vehicle collision avoidance.

Influence scores and presence duration filter sensor-detected objects, cutting 3D map generation cost while preserving AV simulation accuracy.

Road marking recognition corrects GPS drift in complex intersections to generate accurate autonomous driving trajectories.

Custom priority settings let vehicle occupants change object warning distance thresholds, reducing unnecessary alerts while preserving timely warnings.

Critical image regions are processed at high resolution based on surrounding context, improving object recognition speed and vehicle control timing.

Maintaining occupancy probability in shadowed grid cells prevents false unoccupied readings and improves low-latency drivable space detection.

3D road models fused from camera, lidar, and radar help autonomous vehicles decide when to stay in lane or follow freeway exits.

Adjusts gaze time and line-of-sight criteria to detect insufficient forward recognition when nearby vehicles stop or decelerate.

Combining LiDAR point clouds with camera factor queries helps autonomous vehicles distinguish vegetation from true hazards and avoid unnecessary braking.

LiDAR points projected onto camera images help distinguish vegetation from vehicles and pedestrians, reducing unnecessary braking.

Vehicle usage data is matched to stored driver profiles to automate cockpit user recognition without manual credential entry.

Behavior prompts and sensor-captured actions replace passwords or QR scans to recognize authorized vehicle users faster and more accurately.

Rotation-invariant local binary patterns group object views across orientations, improving fisheye camera classification accuracy with shorter feature vectors.

A moving lane change mark tracks surrounding vehicles to show where automated merging will occur and reduce driver unease.

Secondary determination uses confidence thresholds and past image results to reduce over-detection and stabilize autonomous vehicle control.

A gate-mounted camera captures and stores underfloor images during gate descent, avoiding dedicated underbody cameras and added mechanisms.

Visual alerts in rearview mirrors prompt drivers to verify backup camera hazards, improving reverse object detection without adding complex monitoring.

By extending obstacle-free distance with moving-object velocity, this case keeps ADAS active while maintaining safer spacing.

Actual and predicted road-user trajectories are compared to detect map and prediction errors before they degrade autonomous vehicle planning.

Trajectory-prediction control merges lane-change decisions with planning to cut complexity and improve smooth obstacle avoidance on snow and ice.

Fusing camera objects with radar or lidar overlap in a common coordinate system improves vehicle object distance and tracking reliability.

Front and rear camera comparison identifies cargo dropped from a moving vehicle and alerts the driver and nearby traffic.

Object and precipitation sensing pre-adjust wiper speed before nearby splashes or sudden rain changes reach the windshield.

Projected road light patterns and shadow analysis improve night object detection for autonomous driving without extra lighting modules.

ML confidence scoring and dead-zone coverage help separate adjacent objects and cut false or missed detections in autonomous driving.

A front camera keeps the traffic light in view at the stop point, helping drivers accept autonomous intersection positioning.

Dynamic pursuit poses decouple lateral steering from fixed spacing, helping follower vehicles track leaders when lane markings are weak or absent.

Fusing map position, camera sign reading, and driving context helps ISA avoid wrong speed limits in rural roads, obscured signs, and map errors.

A neural network predicts occupancy probabilities for multiple agent types and future times in one pass, improving dense-scene accuracy and speed.

Object recognition on non-displayed camera regions lets the vehicle display expand to include relevant nearby objects before they are missed.

Direct perception-to-control driving uses multimodal encoding and lane-level maps to cut error accumulation and reduce map dependency.

Camera and LIDAR data replace high-precision maps to segment lanes and choose lane-change or main paths in real time.

Graphic road-user and trajectory cues help drivers understand automated steering behavior and hazards in complex traffic.