Anti-Collision Systems TRIZ Cases

By analyzing assistance execution and driver behavior, the control logic adjusts warning levels to cut false alerts and improve road safety.

Parameterized trajectory planners let vehicles share compact maneuver inputs instead of full paths, extending prediction horizon with higher accuracy.

When visibility is blocked at an intersection, velocity control uses right-of-way and occlusion checks to stop safely or maintain a safe speed.

Real-time distance and urgency display helps crossing drivers judge an approaching vehicle at unsignalized intersections and avoid misjudgment.

A Hidden Markov lane model combines current object state with transition history to improve lane assignment in intersections and lane changes.

Steering assistance is relaxed when lateral obstacles may push a pedestrian into the lane, improving collision avoidance without frequent unnecessary maneuvers.

Variable alert intensity based on blind-spot location helps maintain driver attention while reducing habituation to frequent warnings.

Abstracted or encrypted driver monitoring data preserves privacy rules while supporting safe handover from automated to manual driving.

Early occupant alerts and entry-time priority control help vehicles avoid meeting head-on while entering and passing on narrow roads.

Position-sample vicinity detection cuts false bicycle alerts and server load while delivering timely collision warnings without line of sight.

When lane-change speed is too low, the controller accelerates to a minimum safe speed using traffic data to reduce collision risk.

Steering-angle-based object position correction helps blind-spot radar avoid false lane-change warnings while maintaining detection reliability.

An inductively coupled trace loop senses actual laser diode current onset, reducing transistor jitter errors in LIDAR pulse timing.

Advance guidance on restricted automated-driving sections helps users prepare for manual takeover and reduces transition risk.

Environmental sensors detect imminent collisions between road users, enabling an autonomous vehicle to create an evasive path without direct communication.

Distinct alarm forms for one-side versus two-side obstacle proximity help drivers judge vehicle centering in narrow spaces.

By estimating zones where stopping would block other mobile objects, the controller plans smoother passing-by without sacrificing collision avoidance.

A closely spaced negative-positive lens pair uses refractive-index temperature coefficients to stabilize focal position and correct aberrations.

Relative velocity and proximity filtering removes fixed rotor points from warning targets, reducing unnecessary mobile warnings.

Calculates a collision range instead of a single impact point to suppress braking when deceleration would raise collision risk.

Misrecognized lanes and limited avoidance space trigger staged braking and steering preparation to preserve control margin during obstacle approach.

Differential brake pressure steers vehicles without automated steering along fixed depot paths, cutting manual intervention and retrofit cost.

Object clustering triggers a limited-capability caution mode that slows autonomous vehicles and restricts maneuvers in crowded areas.

Nearby manual-mode vehicle alerts help re-engage autonomous drivers so they can prepare for timely manual intervention in unpredictable traffic.

Dual target-acceleration maps raise low-speed ACC output to overcome torque shortfall and deliver smoother restart response.

Dual same-object regions raise obstacle reliability by separating stationary and moving detections for more accurate braking control.

Prediction error between expected and actual road images reveals overlooked hazards and enables targeted driving prompts without overloading the driver.

Cross-over distance replaces steering angle to cancel turn indicators reliably during lane changes, improving driver assistance and safety.

Inserted AI-only frames let one display serve people and autonomous vehicles while reducing image misrecognition and accident risk.

A single rear camera and mirror-integrated display crop and enhance lane views to cover side blind spots without added cameras or mirror complexity.

Driver sight line feedback suppresses unnecessary vehicle hazard alerts while reporting obstacles and rear risk targets the driver has not noticed.

When an emergency vehicle approaches, the system restricts driver content during autonomous cruising to restore attention and reduce response delay.