Scene Recognition TRIZ Cases

A driver assistance case that keeps lateral guidance by tracking a road user ahead when lane markings or standard conditions are unavailable.

Semantic behavior filtering removes low-quality agent predictions so autonomous vehicles can plan collision-avoiding paths with less compute and faster response.

Sensor fusion of camera and IMU data with anomaly scoring detects abnormal following vehicles while reducing trajectory-based false alerts.

Turn-signal-aware steering thresholds delay or block evasive control when a lead vehicle may change lanes, reducing false activation and discomfort.

Fuses HD map lane-link altitude data with sensors to verify valid contour regions and reduce object misrecognition on sloped roads.

Luminance dispersion around recognized objects helps mark road areas with scattered small hazards, improving skid-aware drive assist control.

Multiple ML models tag and filter autonomous vehicle sensor sessions, cutting annotation time while preserving training data quality.

Selective storage of low-confidence and error-prone sensor frames helps improve dynamic object recognition with less manual data review.

Directional overlay images are limited by display region and transparency to alert drivers to forward objects without obscuring the road.

Nearby vehicle images are sent to a server so train stoppage factors can be identified faster without relying on driver reports.

Risk areas and small-object distribution are used to score avoidance routes, reducing excessive steering while maintaining safe vehicle travel.

By checking exit passable conditions before requesting remote support, the vehicle avoids unnecessary operator binding in blocked areas.

Image analysis of a leading vehicle's license plate flags driver risk factors early and triggers automated evasive maneuvers to avoid collisions.

Map data and front-camera images are fused to identify the correct traffic light on curved approaches and support timely driver alerts or speed control.

Vehicle speed changes are correlated with road images to build ML training data for detecting unknown traffic objects across regions.

Priority grouping and lane-position sorting help autonomous vehicles display the most critical LiDAR-detected objects despite sensor errors.

Reduced yaw moment on roads without center lines helps suppress off-road lane departure while lowering driver discomfort.

Front-camera and map-based roundabout entry width plus other-vehicle passage time are used to set safer, smoother vehicle speed.

Context-specific planner tuning adapts autonomous driving trajectories to traffic scenarios, improving navigation and collision avoidance.

Machine learning interprets roadside arm gestures and distress signals so autonomous vehicles can honk appropriately and flag possible kidnappings.

An attention module isolates radar regions missing from camera or lidar views, preserving key signals while cutting object detection compute.

Neural segmentation and blockage classification trigger camera cleaning only when blockage type and severity justify it.

Cell-based LiDAR slope clustering filters objects behind boundaries, reducing false parking collision alerts and braking errors.

When a front camera fails during avoidance control, the controller switches from camera-radar fusion to LiDAR-radar fusion to maintain recognition accuracy.

Sensor-triggered surround-view trailer recording captures accident and theft evidence without continuous video storage overhead.

When paths intersect in narrow passages, sensor-based collision timing and rule-aware rerouting help autonomous vehicles pass obstacles safely.

Confidence-aware prediction heads and uncertainty-weighted loss improve autonomous driving perception reliability without a monolithic model.

Type-specific feature embedding preserves cues like body orientation and turn signals to improve road user trajectory prediction accuracy.

Multi-sensor roadway fusion updates a clothoid spline with Kalman filtering to maintain accurate course estimation despite noisy or failed inputs.

Thin-film side displays place rear camera views near traditional mirror sightlines, reducing driver adaptation and improving visibility.

Adaptive token halting lets BEV vision transformers drop irrelevant patches using context, cutting AV compute load while preserving detection accuracy.

Distinct speed alerts separate legal limit warnings from driver-set thresholds, improving notification timing and driver-aligned speed control.

When onboard sensors disagree or lack confidence, remote assistants classify objects through a GUI so autonomous vehicles can resume navigation.

Depth-based gating aligns multi-sensor data and deemphasizes occluded object points to cut fusion noise and improve perception accuracy.

Perception groups lanes and propagates sign meaning by lane attributes, improving lane-specific speed limit and rule interpretation.

When pedestrians or bicycles are detected on the road shoulder, projection control dims or stops road images to reduce driver distraction.

Surrounding-environment sensing adjusts gaze sensitivity and attention regions to cut false alarms during necessary driver glance shifts.

Separating road and sidewalk projection zones keeps vehicle guidance images clear during turns and on uneven surfaces.

Projected headlamp light patterns turn road obstacles into detectable shadows, improving night object detection for autonomous driving.

Camera-radar fusion with voxel-based 4D perception improves obstacle detection in adverse weather and supports collision-avoidance driving.

Automatic target switching between offset and front motorcycles reduces rider input while keeping zigzag platoons stable and spaced properly.

Trajectory-based pedestrian detection lets an electronic rearview mirror notify the owner and enable communication only after owner verification.

A wide-angle camera mounted on the cargo bed captures both rear blind spots and loaded material in one view, improving monitoring continuity.

A retractable vehicle access sensor extends only when objects are nearby, preserving detection accuracy while reducing dirt obstruction.

Previously tested occupied cells are flagged and skipped in occupancy grids, cutting compute time and power for parkable space detection.

Front guardrail detection is improved by filtering false radar targets from reflections and opposite lanes before generating guardrail patterns.

Obscured surround-view regions are rebuilt from other vehicle cameras and extrapolated image data to reduce shadow-induced distortion.

Spatiotemporal road-user graphs and weighted GNN inference improve pedestrian crossing prediction while limiting autonomous driving compute load.

Segmented frame analysis detects low drivable-area regions on unpaved roads and triggers timely danger warnings for driving assistance.

Person-region segmentation reduces false area-intrusion alerts from overhead fish-eye images while preserving broad detection coverage.

Conditional prior prediction lets autonomous vehicles anticipate object behavior, cutting abrupt path changes, compute load, and comfort loss.

By linking detected license plates to database geometry data, this case improves surrounding vehicle shape estimation despite occlusion and limited sensor views.

Distance and camera data let a work vehicle distinguish obstacles from harvest targets, avoiding unnecessary maneuvers and preserving work efficiency.

Sparse radar and camera grids are fused by a neural network to infer dense occupancy and free space without costly LiDAR.

When branch lane markings are missing, a virtual demarcation line guides trajectory generation for smoother lane changes and safer control.

Multi-modal camera, lidar, and radar fusion builds a synchronized 3D scene map to improve autonomous vehicle object detection reliability.

When autonomous driving functions fail, the vehicle selects an available minimal risk maneuver to decelerate, change lanes, detect a safe area, and stop.

Braking sensitivity is shifted by road curvature, steering, object position, and illumination to avoid false ADAS warnings near guardrails.

Image-based parking row recognition distinguishes parking lots from road markings to avoid unnecessary power reduction during pedal misoperation.

Hood- or fender-mounted cameras split forward and cross-traffic views to improve hazard detection while limiting retrofit complexity and alert delay.

A transformer with attention predicts multiple object trajectories directly from frame sequences, cutting tracking errors and resource use.

By comparing reflected-light and camera images, this case detects rain, fog, and vapor on vehicle sensors to preserve sensing accuracy.

Randomly scaled and cropped obstacle images train a model to handle obstructed traffic lights and reduce false positive classifications.