Anti-Collision Systems TRIZ Cases

Camera and sensor overlays create a realistic bird's-eye parking view, helping drivers reach target positions without misleading distortion.

Temporal and spatial execution windows keep inter-vehicle maneuver messages valid during negotiation, improving coordinated driving reliability.

By detecting whether a vehicle is on-road or off-road, the control logic suppresses false proximity alerts and keeps warnings relevant.

Uses vehicle and passer-by load-state evaluation to guide driving behavior that reduces mental and physical strain across traffic.

A focused adjacent-lane detection zone helps driving assistance spot crosswalk blind-spot obstacles and trigger timely alerts or deceleration.

Coordinated speed limit control resolves conflicting vehicle restrictions around detected objects, preserving workability and obstacle safety.

A simple minimum-speed threshold lets lane change control accelerate or decelerate safely at low speeds while reducing processor load.

Episodic blind-spot video overlays help drivers judge adjacent vehicle distance and position more clearly during lane changes.

Personalized risk estimation aligns operator habits with a target driving style, improving behavior planning accuracy and traffic safety.

Tire condition data is converted into driving control parameters, improving automated vehicle controllability without heavily increasing control-system complexity.

Time-stamped comparison of vehicle and roadside sensing data detects sensor tilt and operational errors without slow moving-object filtering.

A driver-walked path captured on a mobile device lets the vehicle verify and follow a precise trajectory with less manual intervention.

When vehicle sensors disagree beyond a threshold, this control approach selects a reference value to keep cruise and braking responses accurate.

A scope manager adjusts ADAS perception, planning, and control by driving risk, enabling graded alerts, warnings, and vehicle actions.

An independent secondary controller checks localization, object prediction, and planned paths to catch errors and trigger safe vehicle maneuvers.

Preview distance adapts to lateral deviation and speed to stabilize steering effort and reduce off-tracking in heavy-duty vehicles.

Multi-resolution grids sized to object dimensions speed autonomous vehicle trajectory collision checks while preserving validation accuracy.

Risk-based automated driving adjusts target speed and track to match traffic rule intensity and time-dependent restrictions.

Context-aware sensor fusion and remote road data help vehicles anticipate hazards while cutting false warnings and processing load.

Buffered target trajectory points help a vehicle avoid shortcut paths on curves and maintain followability across speeds.

A pilot vehicle digitally tethers a driverless truck, sharing longitudinal control to keep target clearance with lower onboard autonomy cost.

Camera and sensor inputs track hand and foot positions to adjust driving assistance when driver attentiveness changes.

Capability-based intersection control gives priority to vehicles with stronger avoidance or impact-reduction functions to cut collisions and unblock traffic faster.

Dual tilt sensors compare vehicle body and sensor housing orientation to detect axis shift and preserve peripheral monitoring accuracy.

Fused camera, LiDAR, and RADAR data assess swaying or encroaching semi-trucks and trigger lane, speed, or alert responses.

Fuses sensors and high-accuracy maps to judge short-gap consecutive lane changes and clearly notify drivers when autonomous execution is possible.

Location-based speed control adapts to driver monitoring duty, improving convenience while maintaining reliable autonomous vehicle operation.

Delaying rear-area alert judgment lets the vehicle verify object tracking near sensor edges and cut false warnings from adjacent-lane traffic.

Modulated light worn by pedestrians or cyclists lets vehicles identify vulnerable road users earlier and issue timely driver alerts.

Adaptive delay timing after a canceled lane change proposal balances execution reliability with missed opportunities based on relative speed thresholds.

Dedicated lanes organize autonomous vehicles into fixed-slot platoons, coordinating lane changes and speeds to raise throughput and cut delays.

When a connected trailer blocks rear radar coverage, the system detects the pattern and shifts warnings or sensor reliance to maintain merge alerts.

Combining AI-based control with traffic-rule algorithms helps autonomous vehicles keep natural driving behavior without sacrificing reliability.

Integrated control flows and logical constraints automate safety rule generation for complex multi-vehicle autonomous driving scenarios.

Shared road environment data is ranked by reliability so autonomous vehicles can update routes and driving control as conditions change.

Filters rear-approaching vehicle alerts by checking for non-target vehicles in the intervening road region, reducing false warnings.

Shared cloud prediction coordinates leading and following vehicles to avoid obstacles with less onboard sensing and tighter free-space planning.

Camera and millimeter-wave radar data are fused to correct object position and shape recognition errors for more reliable collision avoidance.

Vertical-angle LIDAR logic separates overhead signs and bridges from road obstacles, cutting false positives in driving assistance.

Adaptive acceleration caps by driving level reduce occupant uneasiness during level 3 automated speed control.

Time-to-Exit and lateral overlap change rate help judge rear lane-crossing vehicles and suppress unnecessary driver warnings.

A central computer routes area-specific emergency and travel updates to mixed-manufacturer digital signs for real-time local messaging.

Continuous weather, road, time, and location checks adjust lane-assist debounce time to improve availability while limiting false detections.

Pre-checking automatic and manual driving behavior against road rules enables smoother mode switching with fewer sudden vehicle actions.

Real-time shoulder congestion detection lets the vehicle suspend or resume automatic branch-lane changes for smoother route following.

Camera and radar fusion predicts nearby collisions, warns the driver, and shifts steering or braking authority when driver input is detected.

Operational constraints are relaxed on predefined transit paths, then restored with preventive action if the vehicle turns toward manual work areas.

Multiple collision risk coefficients guide deceleration strategy selection to avoid forward crashes without raising rear-end risk or discomfort.

Horizontal and vertical vehicle motion data are clustered and classified by a CNN to map relevant road surface objects for warnings and automated driving.

An imaginary vehicle target lets the controller adapt to fast-approaching traffic and avoid unnecessary acceleration during lane joining.

A simplex controller switches between advanced and baseline driving control using safety rules that can be logically proven for collision avoidance.

A Navigator model precomputes auxiliary reasoning from full sensor data so a Driver model can issue fast vehicle control with less delay.

Real-time traffic status shifts driver takeover timing before an autonomous driving section ends, improving transition safety and comfort.

Road-shoulder vehicle detection curbs lane changes and adds occupant notification to keep automated maneuvers smooth and secure.

Cost-based MDP planning selects pre-chosen lane-change policies across multiple gaps, improving robustness while limiting computational load.

Maintains tracks through mapped occlusion zones by predicting object position and adjusting uncertainty to reduce collision risk.

Driver gaze and vehicle risk level govern when wake words can be omitted, keeping voice control convenient without reducing road attention.

Rear-mounted sensors detect following vehicles and closure rate, triggering lights or alarms to help prevent truck trailer rear-end collisions.

Early visual and auditory alerts prompt evasive steering sooner, allowing smoother assist torque and more reliable collision avoidance.