Scene Recognition TRIZ Cases

Sensor-driven lane penalties help autonomous vehicles bypass slow lead agents, balancing collision avoidance with travel time.

When some traffic lights are unclear, combined color recognition still triggers a red-light alert if the driver is not braking.

A dedicated road object model routes unknown obstacles to cropped-data analysis, improving AV classification and reducing navigation dead-ends.

Monitors LIDAR dirtiness over time and parameter change to flag abnormal states before sensor fouling degrades automated vehicle control.

When blurred lane markings make lane position uncertain, the controller cross-checks map and trajectory data to avoid prohibited control and apply valid adjacent-lane actions.

Preloading occupancy grid cells from high-density maps gives autonomous vehicles faster confirmation of static features and more accurate navigation.

Self-supervised occlusion labels from image data help detect hazardous obstacles without manual annotation or stereo cameras.

A moving lane change mark tracks surrounding vehicles to show when and where automated lane changes will occur, reducing driver unease.

An electrowetting lens lets an analog HD automotive camera vary field of view while preserving pixel density and avoiding costly digital links.

Replay-based offline tracking compares runtime and reconstructed object tracks to reveal phantom tracks, delayed detections, and safety gaps.

3D surfel maps and live sensor data help vehicles predict shape changes in dynamic objects and reroute around sensor occlusions.

A perception model uses LiDAR points, bounding boxes, and semantic labels to link vehicle appendages to parent vehicles for safer localization.

By testing multiple mounting assumptions against misalignment data, one sensor can identify its installed vehicle configuration and calibrate correctly.

Sparse camera-based road surface maps are densified with Markov random fields and neural networks for more accurate autonomous driving.

False negatives are grouped by duration and checked against class thresholds to flag impaired vehicle sensors before automated driving.

Fusing RGB, semantic, and radar depth cues creates a dense LiDAR view for more accurate 3D object detection without full LiDAR cost.

Multi-modal roof displays and speakers help autonomous vehicles recognize nearby intent and share situation cues to prevent unclear road interactions.

Existing vehicle cameras are reused to detect tailgating and trigger image recording, cutting hardware cost while improving detection accuracy.

Machine-learned construction signatures help vehicles detect work zones, predict hazardous situations, and issue timely driver alerts.

Perception-based interlocks coordinate doors, ramp deployment, and wheelchair securement to prevent unsafe exits in autonomous vehicles.

Remote vehicle sensor data reveals road signs and obstructions hidden from the host vehicle, enabling timely alerts and autonomous response.

Ranks detected objects by lane position and predicted ego trajectory to avoid 3D back-projection errors and secondary lane interference.

Optical flow and road-contact detection help autonomous vehicles distinguish drivable objects from hazards and avoid unnecessary braking or evasion.

Spatial checks on camera and depth data help distinguish real road signs from reflections, reducing autonomous driving navigation errors.

Multimodal driver, vehicle, and scene data reveal discomfort early, helping automated vehicles adapt driving style and avoid unsafe manual takeovers.

Expected map features are matched with live vehicle sensor data to detect degradation early and support safe automated driving.

Parallel scanning and staged summing speed multi-channel convolution for ADAS image analysis, improving object detection and vehicle control.

Class-specific embeddings preserve pedestrian, cyclist, and vehicle cues to improve trajectory prediction accuracy in autonomous driving.

AI cameras set non-recognition zones and wake only for relevant events, cutting parked-vehicle battery drain without losing detection.

Continuous lidar-camera feature matching checks sensor and pose accuracy during driving, reducing manual calibration effort and delay.

Predicts extra usable distance from a moving object's velocity so ADAS can stay active without relying only on stationary-obstacle limits.

By combining gaze direction with pupil distance, this case distinguishes road objects from in-vehicle indicators to trigger safer driving assistance.

Corrected vehicle and oncoming turning paths suppress inappropriate emergency braking when right-turn timing differs at intersections.

Road-surface cues such as tactile paving help predict visually impaired pedestrian behavior so mobile objects can adjust movement more safely.

Fusing mono camera images with LiDAR and coordinate matching enables real-time vehicle localization and distance estimation without costly GPS or stereo cameras.

Sudden distance drops between frames trigger inhibition of driving assistance when tailgate visibility causes false object position changes.

Brake-light response is used to gauge driver readiness before takeover, helping automated driving avoid unsafe handoffs.

Visible-light video and perspective transforms estimate vehicle distance without LiDAR, cutting sensor cost for real-time control.

Categorical interaction encoding groups road objects by type to predict future behavior accurately without losing scalability in dense vehicle environments.

Outside-sensor feedback adjusts vehicle height at platform stops to avoid obstacle interference while preserving easy entry and exit.

GPS-based classifier adaptation helps one lane detection pipeline handle regional road markings and weather changes for stable autonomous driving.

When obstacles hide crossing traffic, the controller sets trajectory-based risk areas and adjusts vehicle speed to avoid collisions at intersections.

Relative speed, distance, and map data let the vehicle infer oncoming lanes and keep driving assistance active when lane markings disappear.

Machine learning infers driver-specific following gaps from sensor, vehicle-size, weather, and traffic context to make ACC more comfortable and trusted.

A hybrid BEV pipeline combines geometric reprojection and learned sensor fusion to improve scene understanding without losing certifiable reliability.

A neural network classifies vehicle camera blockage type and degree to trigger cleaning only when needed, reducing false detections and waste.

Split calibration data lets a replaced in-vehicle sensor preserve detection accuracy without repeating vehicle-side recalibration.

A remainder explainer isolates harmful unexplained LiDAR returns, cutting labeling effort and latency while preserving object detection accuracy.

Camera analysis tracks a front vehicle's license plate height and distance changes to detect speed bumps when map data is outdated.