Anti-Collision Systems TRIZ Cases

Early visual and auditory steering alerts help drivers recognize collision risk sooner and start evasive maneuvers before timing becomes critical.

Fixed roadside sensors augment truck perception along rail-parallel roads, improving event detection beyond onboard sensor range.

Automatic parking starts while the vehicle is moving, letting the control unit search for a spot, confirm driver choice, and guide parking.

Context-aware tailgating alerts use driver, vehicle, and road conditions to cut premature warnings, reduce stress, and improve timing.

Dynamic data containers allocate processing, memory, and bandwidth by app priority to deliver vehicle localization data efficiently.

Hazard data from the vehicle and nearby traffic is turned into an AR safety trajectory, helping drivers react faster in fault conditions.

Driver alertness and road context are used to time takeover requests and trigger evacuation deceleration when manual handover is refused.

RSS-based risk evaluation and sensor-fusion diagnostics help autonomous vehicles choose safe control plans and trigger MRM when recognition fails.

Compares important and model-contributing environmental inputs to judge whether AI-based vehicle control results are reliable.

Vehicle speed is used to estimate maximum road curvature and correct overestimated lane-line curvature for steadier steering control.

Dynamic blind spot calculation and curve-based tracking keep object motion prediction active on curved roads and through occlusion.

Selective behavior prediction in branching areas cuts compute load while improving collision avoidance and driving comfort in merges and roundabouts.

Multi-scale occupancy grids speed autonomous trajectory collision checks by matching cell size to object motion and size.

Elapsed-time logic lets an automated vehicle resume after short congestion stops without driver input, while requiring input after longer stops.

Relative speed and arrival-position checks suppress rear cross-traffic warnings for distant or non-approaching objects.

Configuration-based module switching lets drivers toggle lane warning and correction modes while simplifying installation across vehicle variants.

Graphic overlays on side-camera video help drivers judge approaching vehicles, expand blind-spot awareness, and improve lane-change distance cues.

Real-time GNSS and sensor fusion detects drowsy or risky driving early, enabling driver alerts and proactive accident prevention.

Forward simulation ranks collision, conflict-zone, and following-distance risks so autonomous vehicles avoid unnecessary braking while preserving timely responses.

Movement profile sequences compare traffic scenarios to remove redundant autonomous driving tests while preserving critical coverage.

Synchronized depth-range capture preserves sign text in distance images while reducing data volume and sensing power in vehicles.

Combining CNN image features with CAN data improves driving risk scoring accuracy while managing real-time assessment complexity.

Assistive steering torque is canceled at lane-change start or end based on rear-vehicle presence, reducing collision risk and driver interference.

Acoustic route estimation helps ALKS detect merging emergency vehicles early and trigger evasive actions to create a passage.

Multiple position checks within a time window and CNN classification reduce lane-association oscillation and automate lane-change annotation.

Abstracted sensor and ego-vehicle motion data feed a deep learning risk score and action generation flow for real-time accident mitigation.

Proactive adjacent-lane control avoids prolonged parallel travel, helping protect driver privacy and comfort during hands-free automated driving.

Lane-marking-based continuation control keeps eyes-off autonomous driving active when a surrounding vehicle departs the lane.

Scenario-based V2X warning logic uses lateral distance and relative speed to improve pedestrian risk assessment, including reversing vehicles.

Uses vehicle position and road structure rules to extract travelable areas from 3D point clouds despite guardrails, tunnels, and road width changes.

Sensor and V2X-based path prediction helps autonomous vehicles detect emergency vehicles early and change path and driving mode safely.

Sensor fusion detects when a lead vehicle is turning out of lane, delaying AEB activation to cut false alarms without losing collision avoidance.

Adaptive target-variable tuning helps autonomous vehicles choose more understandable maneuvers while balancing safety, comfort, and traffic interaction.

Free space corridors and backup goal points cut trajectory planning load while keeping vehicle guidance collision-free on curved roads.

Stored passenger data can be sent to responders after a crash and used to alert drivers when a child is left in the vehicle.

A state controller offsets projection range changes to keep the HUD virtual image stable and reduce driver discomfort during adjustment.

Segmented object and vehicle sensing cuts unnecessary stops while maintaining collision avoidance for autonomous vehicles in shared industrial zones.

Dynamic images of pedestrians, bicycles, and scooters help a vehicle predict motion changes and choose in-lane steering or braking paths.

Turn signal input temporarily suspends lane keeping control, preserving steering feel during manual lane changes and restoring automation afterward.

Graduated sound and vibration alerts adapt to speed, traffic, and lane quality to improve driver takeover timing without excessive disturbance.

V2X intent messages combine driver behavior and vehicle control data to plan local paths through sensor dead zones and avoid collisions.

Overlapping sensor views trigger synchronization only when object positions diverge, cutting processing load and flagging displaced cameras.

Gaze-based torque and timing thresholds help distinguish accidental from intentional steering input for safer autonomous-to-manual handover.

TTC-based lane safety evaluation guides minimum risk stopping to the safest lane, reducing rear-end and roadside collision risk.

Advance takeover alerts and driver-state checks let an automated vehicle decelerate or stop safely when manual driving is refused.

Correlating driver gaze with outward traffic events cuts false alarms and improves warning timing for unsafe driving conditions.

Camera-based intent detection helps autonomous vehicles predict cut-ins early and selectively allow lane changes to improve safety and traffic flow.