Anti-Collision Systems TRIZ Cases

Lane-based target selection prioritizes nearer cross-traffic during turns, reducing false avoidance at multi-lane intersections.

Wireless ITS data predicts nearby vehicle positions early, enabling smoother automatic-to-manual steering handover and safer driver alerts.

Prestored drivable patterns and event-position exclusion areas let autonomous vehicles adapt to weather and road disruptions safely.

Pulsed magnetic marker fields and RF echo responses shape machine-specific safety zones that warn workers reliably while minimizing false alarms.

By detecting sporty driving during turns, the control deactivates automatic braking to avoid unwanted interventions and preserve driver autonomy.

Real-time ECU alerts use image and sensor data to judge lateral passing distance and help drivers overtake bicycles within local rules.

Road boundary data filters out off-road objects from vehicle alarms, reducing false alerts while keeping relevant hazard detection.

Adaptive rear collision alerts adjust thresholds by target distance and approach angle to cut false warnings from uncertain path prediction.

Face-specific marker groups encode direction and body size so nearby vehicle sensors can detect orientation, estimate distance, and predict actions.

Assesses driver consciousness and road conditions to time handover alerts and estimate safe recovery time for manual driving.

Shoulder-width-based thresholds improve driver distraction detection despite camera angle, focal length, and driver size variation.

Using radar signal-to-noise index, this case improves adjacent-lane vehicle detection to cut false alarms and missed blind spot alerts.

Combining map-based static paths with sensor-based dynamic boundaries enables smoother vehicle travel with lower calculation load.

Sensors measure oncoming vehicle distance and speed, then trigger visual turn cues that reduce driver misjudgment at crossings.

Measures cargo overhang and tailgate position to update vehicle dimensions, improving parking sensor accuracy and driver awareness.

Lead-follower trajectory prediction uses sensor state data to improve autonomous vehicle motion planning, spacing, and collision avoidance.

Direct sensor-to-HUD warning control bypasses overloaded vehicle control units to cut display delay and prioritize urgent driver alerts.

Maximum road curvature estimated from vehicle speed corrects overestimated lane-line curvature and stabilizes automatic steering.

Grid-based obstacle tracking separates fixed guard rails and tunnel walls from moving vehicles to cut false rear side warnings.

Multiple target motion patterns and probabilities refine collision risk estimation, reducing unnecessary steering and deceleration control.

Shared maneuver space lets autonomous vehicles park more densely in a row while preserving enough room for each vehicle to exit.

Automatic steering stays available when one lane boundary is partly undetected by assessing object motion on the dividing line and route existence.

Depth-guided ROIs and multi-exposure camera fusion reduce full-image search load while improving vehicle signal detection accuracy.

Camera, radar, and LiDAR results are selected by driving conditions to improve obstacle collision judgment on inclined roads and cut false alarms.

Adaptive voice dialogue switches lines from blinker, steering angle, and speed changes to improve driver awareness and affinity.

Adjacent-lane status is used to show executable automation levels and driver requirements, helping drivers make safer lane-change decisions.

Phase-specific damping is applied only during return steering, preventing lane-departure overshoot without weakening obstacle avoidance.

Camera images, direction cues, and distance estimates identify road segments and plan vehicle trajectories with less map data.

Gaze-based driver recognition changes risk alert intensity, reducing annoyance while keeping out-of-vehicle hazards visible.

Camera, map, GPS, and vehicle data are fused to keep vehicles aligned when lane markings are missing or hard to detect.

A rearmost-vehicle-first lane change secures destination-lane space for the platoon, reducing delay and maintaining stable group travel.

Precomputed trajectory options and amended speed profiles help sorting vehicles avoid collisions while sustaining high throughput.

Multiple cameras identify parking style and set focused monitoring areas, giving drivers relevant start-up alerts without manual input.

Lidar measures the rear end and front wheel or underbody to estimate vehicle length more accurately for front-following driving.

Correlating eye-movement and external object motion lets vehicles infer driver awareness at long range without costly high-precision gaze tracking.

Speed sensors and onboard cameras guide lane changes from deviation and heading angle, cutting controller load and positioning cost.

Driver observation before and after boarding is used to time takeover alerts so manual driving resumes only when alertness is sufficient.

Predicts how gaps between nearby vehicles will change during a lane change, enabling safer route planning from speed and distance data.

Trajectory estimation bridges sensor blind spots so driving support stays continuous and avoids abrupt support changes when objects reappear.

Fusing binocular stereo vision with millimeter-wave radar expands obstacle detection and enables timely vehicle braking or warnings.

Risk-based lane keep assist adjusts intervention thresholds from driver, vehicle, and environment status to improve comfort and consistency.

Critical alerts are moved to a dedicated display while other screens are suppressed, helping drivers notice warnings faster in multi-display vehicles.