Anti-Collision Systems TRIZ Cases

When failures or route deviations occur, onboard control assesses risk and executes steering, braking, or lane changes to restore a safe state.

Wheel-level brake pressure is updated from collision risk after braking to add steering force and improve emergency obstacle avoidance.

Comparing object states and image labels across camera and laser sensors helps flag faulty readings and maintain navigation confidence.

Expanding the collision prediction region during detected U-turn maneuvers improves warning timing while limiting false predictions in normal driving.

Time-to-collision based switching between emergency lights and turn indicators clarifies lane-change intent during minimal risk maneuvers.

Sensor points within an expected wheel zone are fit to estimate wheel angle, helping autonomous vehicles predict heading before turns.

Symmetrical transmit switching and reference-time beat synthesis compensate Doppler phase shift without sacrificing virtual array resolution.

Rear-sensor data from vehicles already in a roundabout is shared to calculate safe entry timing and warn incoming vehicles of collision risk.

Stored situation-to-assistance mapping lets the vehicle propose the right driving aid from detected conditions, reducing driver selection burden.

When failures or route deviations arise, onboard control triggers braking, steering, or lane changes to reach a safe state and resume driving.

Presents risk-factor cues before hazards become apparent, helping inexperienced drivers focus on likely danger areas without excessive alerts.

When failures or path deviations occur, the MRM controller steers, brakes, or accelerates the vehicle into a safe state and resumes driving.

Stepwise driving assist reduction uses lane disappearance detection and steering grip confirmation to improve driver handover reliability.

Pre-lane lateral movement closes passable gaps during lane changes, helping block motorcycles or bicycles from slipping between vehicles.

A scene interaction graph and implicit latent variables improve multi-actor trajectory sampling while preserving scene-consistent forecasts.

Adaptive safety envelope calculation uses vehicle speed and braking limits to prevent following-distance violations during lane changes.

A virtual line between adjacent parking markings improves parking type recognition and avoids angle-to-perpendicular misidentification.

Sensor-guided MRM control handles failures and path deviations by selecting steering, braking, or acceleration actions to return the vehicle to a safe state.

Limits offset control during no-monitoring autonomous travel to cut troublesome lateral acceleration while preserving safety distance.

Dwell-time checks across pre-warning and trigger zones help lateral collision avoidance intervene at the right time and cut false alarms.

Advance section alerts and driver-state-based timing help wearable terminals support smooth manual takeover in mixed automated driving routes.

External V2X content reshapes virtual object fields so vehicles can drive more robustly despite occlusion, weather, and noisy detection.

When normal driving rules become unsafe, this controller uses environmental assessment to trigger emergency evacuation travel and avoid hazards.

Limits driving force when acceleration is mistakenly pressed after autonomous braking, preventing sudden vehicle movement and renewed collision risk.

Preplanned minimal risk maneuvers let a vehicle detect hazardous events, execute safe steering or deceleration, and resume driving after risk is cleared.

Camera and radar fusion identifies nearby cut-in vehicles and triggers braking or steering to avoid close-range lane intrusion collisions.

When obstacles obscure sensor coverage, the vehicle adjusts position, speed, or sensor orientation to improve visibility without breaking safety or traffic constraints.

Prompting nearby vehicles before a lane change enables feedback-based prediction and adaptive control to avoid failures and safety accidents.

Driver confirmation motions help suppress unnecessary vehicle alerts while route computation improves driving assistance accuracy.

Multiple lateral ultrasonic positions are trilaterated into reflection-point pairs to classify object height and reveal shaded obstacles.

A moving AR indicator shifts position and size to keep obstacle direction and distance clear within a limited vehicle display area.

Lane-boundary position sensing limits motorcycle auto cruise in unsuitable lane positions, improving stability and rider safety.

Real-time speed and position prediction helps drivers judge overtaking windows more accurately, improving passing safety, smoothness, and fuel economy.

Asynchronous pixel events capture flicker from traffic and vehicle lights, improving object detection speed and accuracy beyond frame-based sensors.

Real-time image-based parking adjusts start points, candidate slots, and steering to avoid obstacles and reduce failed parking attempts.

Coordinated lane changes during shared-route overtaking let vehicles switch platoons without braking delays, reducing energy use and traffic disturbance.

A precomputed speed plan delays remote control requests until blind spots clear, giving operators decision time while avoiding sudden deceleration.

Remote teleoperations adjusts a driverless vehicle's driving corridor when low-confidence road events disrupt autonomous navigation.

AI analyzes vehicle sensor data to choose braking, steering, and acceleration sequences that avoid crashes or minimize injuries when impact is unavoidable.

Alerts are tailored to each driver's visual cognitive ability, reducing overload while improving response to detected road events.

Map fusion with tile and vector data resolves radar level ambiguity on multi-level roads, improving object tracking for autonomous driving.

A single rearward camera feeds split left, right, and center views to reveal side-lane blind spots while avoiding extra cameras and displays.

Speed and azimuth checks against a following object help radar-based driving assistance reject ghost targets without missing real approaching vehicles.

When merging is difficult, waypoint selection shifts toward the branch line end edge to create space for following vehicles and ease congestion.

By classifying blind spots as dangerous or safe and presuming latent obstacle regions, this controller improves vehicle safety beyond sensor range.