Anti-Collision Systems TRIZ Cases

Pressure and shear sensing turns surface contact into stable motion commands, enabling intuitive multi-user control of a moving body.

Acoustic and light sensors compare waveform signatures to identify approaching emergency vehicles and alert drivers despite signal variation.

Classifying traffic situations into high- and low-risk accident patterns improves autonomous driving risk assessment while avoiding unnecessary simulations.

Axial misalignment data lets radar instantly distinguish true and aliased target directions without relying on tracking history.

Adjusts target following distance from lane-specific blocking area to avoid false alarms and reduce driver fear during highway assistance.

When a lead vehicle runs close to the road edge, control logic tracks it if no obstacle is present, preserving smooth traffic flow without canceling assistance.

A central server fuses vehicle and environment data to detect road hazards in real time and trigger driver alerts or vehicle control.

When parking position estimation is uncertain, the vehicle lowers its speed limit to follow the trajectory with less passenger discomfort.

By comparing target vehicle length at multiple time points, this case corrects tracking-point shifts and avoids premature rear collision warning cancellation.

Image-based splash detection estimates splash depth and lane risk to guide vehicle maneuvering and windshield clearing.

Adaptive in-cabin grip prompts vary image prominence by driving state to reduce annoyance while improving manual takeover response.

Trajectory-based filtering isolates road actors that can intersect an AV path, cutting perception load while improving collision prioritization.

Travel planning and rule-based verification reduce blind-area entry situations, improving moving-object detection and traffic flow.

Detector-based control identifies non-priority lane intersection approach and blocks entry to avoid secondary collisions in priority lanes.

Two pre-trained models fuse vehicle position and velocity data to improve lane-change prediction accuracy and reduce erroneous braking risk.

Predictive travel profiles and 3D object behavior feed a risk map that helps automatic driving choose safer, smoother trajectories.

Adaptive preview distance and flow-field steering improve heavy-duty vehicle path tracking and lateral stability without gain scheduling.

When a traffic light is hidden on curved or obstructed roads, oncoming vehicle data helps estimate signal color and adjust drive output for eco-driving.

By grouping nearby vehicles and weighting their motion states, this case infers lane position when markings are unclear in dense traffic.

Lane-boundary position data widens a motorcycle's forward detection angle during automatic deceleration to improve obstacle detection near adjacent lanes.

When obstacles obscure sensor coverage, the vehicle selects a less-obscured field of view while staying within traffic and safety constraints.

Trace-derived rule data helps autonomous vehicles predict habitual road-user behavior beyond current regulations for safer trajectory planning.

Environmental sensors derive and share vehicle rule charts so nearby vehicles can compare traffic rules and avoid mismatches that raise collision risk.

Turn signal status, lane offset, and turn distance are combined to detect wide turns early so ego-vehicles can avoid collisions.

A sealed diffuser housing integrates a mirror blind zone indicator to improve driver visibility, reduce bright spots, and block water ingress.

By fusing V2X and on-board perception, this ADAS case detects hidden braking earlier and improves collision avoidance in obstructed traffic scenes.

Deceleration continues until user crossing intent and reliable signal-state data align, preventing unsafe re-acceleration at crossings.

Forward trajectory recording and vehicle-specific reversing corridors help trailer combinations avoid side obstacles without trailer sensors.

Adaptive ADAS warnings use driver state, driving behavior, and external monitoring data to reduce false alerts and driver distraction.

Grouped sensor detections identify regular roadside barriers early, cutting perception data load while preserving reliable object classification.

Driver observation before and after boarding is used to time manual takeover requests based on alertness, reducing handover risk.

Camera, radar, and V2V data estimate approaching vehicle arrival and pedestrian presence to warn drivers when an intersection turn is unsafe.

Short magnetic PINGs and RF ECHOs create shaped safety boundaries that protect workers while reducing false alarms in complex industrial areas.

When a lead vehicle moves off, object detection in a defined front area keeps the vehicle stopped to avoid collisions during automatic restart.

Traveling information changes the meter display pattern so speed remains visible when the steering wheel blocks the driver's view.

At low vehicle speeds, holding lateral deviation while adjusting deceleration helps avoid obstacles with less steering wheel movement and discomfort.

Separate prediction models let autonomous driving plan for target performance while independently monitoring other road users for safety.

By comparing available and required kinematic maneuvering capacity, the vehicle triggers braking or steering before projected paths intersect.

Switches ADAS control to a ride-comfort trajectory when conditions change, reducing lateral acceleration and jerk from curvature-aware path planning.

A negative-positive lens pair uses spacing and facing-surface curvature ratios to stabilize focus and correct aberrations across temperature changes.

Scene-specific AI model selection cuts computation load while preserving accurate dangerous scene prediction during vehicle driving.

Permissible-action guidance helps autonomous driving users know which non-driving actions are allowed while reducing unsafe behavior.

Mode-specific steering input thresholds improve override detection and enable smoother switching from assisted to manual driving.

Dual fusion paths compare raw and object data to diagnose faults and support safe vehicle control handover when sensors or fusion blocks fail.

A shuttle and curbside lift platform setup helps mobility disadvantaged pedestrians cross safely within signal time while detecting vehicles and obstacles.

Predicted path and transverse distance checks suppress false door-opening alarms when a passing object is blocked by a nearby stationary object.

V2V data from vehicles on the set path enables lane-level location and more precise recommended paths with lower communication load.

Turn-based warning zone adjustment tracks both vehicle and trailer paths to avoid false collision alerts in adjacent lanes.

Predicted bus stop time and sensed road hazards drive rear alerts that discourage unsafe overtaking and reduce accident risk.

Adaptive blind spot alert control shortens calculation time and raises speed thresholds during overtaking to reduce false alerts at higher speeds.