Character And Pattern Recognition TRIZ Cases

Seat-embedded or wearable electrodes track driver state in real time to detect fatigue, trigger alerts, adapt vehicle control, and support emergencies.

When fixed vehicle sensors are blocked, a motor-driven telescopic sensor extends to recover blind spot data and improve autonomous sensing.

A unified perception framework classifies lens, scene, and object anomalies so autonomous vehicles can trigger appropriate behavior changes.

Real-time sensor and remote vehicle telemetry help detect work zones, lane closures, shoulder closures, and speed limits for safer routing.

Models correlated augmentations as a population graph, using spectral contrastive loss to recover labels with provable accuracy.

An overlaid camera image and planned path let a trained network verify road alignment, improving autonomous navigation with sparse map data.

In-vehicle sensors and AI detect high driver emotion early, then trigger calming audio, pilot assist, or autonomous control to reduce crash risk.

Code markers and image-based positioning help autonomous valet vehicles find the correct service space despite misplacement and sensor faults.

Fusing map and perception data with Kalman filtering improves road curvature estimates when single-sensor inputs are obscured or incomplete.

Camera and map data predict road curvature to shape a target speed profile that smooths deceleration and limits lateral acceleration in curves.

Driving direction and braking status trigger the right vehicle camera, expanding video coverage without keeping all cameras active.

Suspension-based pitch control adapts vehicle angle to driver height and object elevation, improving visibility of both high and low targets.

Passenger presence feedback suppresses end-of-support notifications when the vehicle is empty, reducing annoyance while keeping exit hazard warnings active.

Dirt-aware point exclusion improves tow bar angle fitting in trailer camera images by removing rain, snow, or mud affected points.

A switchable sensor path lets one vehicle sensor set support both environment modeling and trajectory plausibility checks, cutting redundancy cost.

A liquid crystal rearview mirror hides the driver camera by switching to transmissive mode only during sensor row scans, reducing distraction.

Detection lines and corner points let the controller estimate road curvature and coordinate steering with speed for smoother lane keeping on curves.

Hierarchical driver assignment categories combine sensor and non-sensor data to link real-time safety events to the correct driver faster.

Operator-marked drivable areas cut perception load, helping autonomous vehicles plan paths across diverse environments with less processing.

When a rear attachment stays close during driving, the controller identifies a tow state and suppresses false rear collision alarms.

User devices supplement autonomous vehicle sensors with calibrated object recognition profiles, improving perception while reducing sensor cost.

Rear-facing exterior lights improve camera detection of the trailer coupler, enabling automated backing and precise hitch ball alignment.

Sensor-driven feature and lane topology estimation builds accurate vehicle road maps in real time without frequent HD map downloads.

Detects occupant medical emergencies and repositions seats or steering elements to expand exit space for rescue and emergency care.

Stored parking-distance maps are validated against current vehicle spacing to provide a faster, single-maneuver exit from lateral parking spaces.

Bird's-eye image transformation and observation-based trajectory correction improve vehicle guidance when lane lines are missing or unstable.

Angle-based light streak detection finds windshield streaks from scratches or unwiped areas regardless of wiper direction.

Odometry-based steering angle and velocity predict curve radius ahead, improving vehicle curvature control when lane markings are weak or absent.

Automated facial-expression analysis detects operator emotional states and triggers alerts or vehicle setting changes to reduce bias and delay.

Filters camera-detected lane segments by type, angle, and distance against map data to reject false lines and stabilize vehicle control.

Gradual AR image fade-out in a vehicle head-up display avoids abrupt disappearance and reduces driver annoyance during object guidance.

Sensors detect a wash facility, then the controller positions vehicle components and guides entry to reduce manual alignment errors.

Comparing convex hull features across LiDAR frames cuts false positives and computation when estimating whether an object is static.

Prioritized event scenes brief the driver before takeover, improving handover readiness while limiting delay and cognitive load.

Occupancy-grid and preliminary-path inputs predict future yaw and speed, cutting positioning load while preserving human-like autonomous driving.

A dual-lens optical assembly projects near and far views onto one imager, cutting camera count, bandwidth, and alignment effort.

Eye and head tracking predict motion sickness during visual tasks, enabling vehicle subsystem adjustments that reduce sensory conflict.

Directional warning sounds adapt to occupant gaze and hazard position to improve collision awareness without losing spatial perception.

Generative models flag anomalous sensor events before teleassist, cutting session frequency, duration, and communication cost.

Sensor fusion links construction markers into a connectivity graph, updating drivable surface and reference path through temporary work zones.

Road regions are gridded and weighted by unevenness so the vehicle can adjust speed before bumps, improving comfort and reducing wear.

Camera-based driver recognition triggers guided language selection and ADAS setup, helping unfamiliar drivers configure vehicles safely.

Automatic hazard light control uses speed-limit intervals and travelling angle detection to warn road users when heavy-duty vehicles operate abnormally.

Image recognition maps gestures to central control panel components, enabling remote vehicle control with less driver distraction.

A neural network predicts lane offset, angle, curvature, and curvature rate directly from camera images to cut processing load and improve real-time steering.

When drivers deactivate emergency stopping, driver monitoring can restore it during fatigue or incapacity to maintain functional readiness.

Image-based control lets passengers operate a vehicle central control panel remotely, reducing driver distraction and improving in-cabin flexibility.

AI and multi-sensor vehicle monitoring detects location, thermal, or noise changes in nearby tents or generators and alerts users early.

Gaze, device request, and UWB position data help distinguish the right in-vehicle media stream for accurate playback and sound control.