Control Devices TRIZ Cases

A control barrier function tunes deceleration from speed and target distance to deliver smooth, comfortable vehicle stops.

Nonconforming user inputs are checked against best-practice rules, then corrected or guided to cut wasted processing and reduce safety risks.

Rack force and slip angle are combined to estimate wheel grip early and reliably, improving torque distribution without extra hardware.

Isometric masking lets vehicles share training data for remote model syncing while preserving privacy and keeping distance-based model accuracy.

Trajectory planning uses hybrid modes and equilibrium states to let autonomous vehicles traverse unstable regions for agile, safer evasive maneuvers.

Interquartile filtering of longitudinal and lateral acceleration yields a mobility index that captures driving aggressiveness for maintenance prediction.

Dynamic scheduling based on sensor data arrival helps autonomous vehicles meet hard real-time windows and sustain operation during failures.

When ML-based vehicle control is active, operator alerts via display, sound, or haptics reduce misrecognition and improve awareness.

Sensor data and planned trajectories feed a prediction model to assess takeover risk and trigger safer vehicle control or driver prompts.

During lane change assist, the display matches nearby vehicle color and shape to real traffic, reducing mismatch discomfort and improving awareness.

Adaptive lane-centering prompts use lane context and driver response to guide safer vehicle positioning without constant distraction.

Directional arrows and steering images show when left or right lane changes are possible, reducing confusion and driver anxiety.

When a vehicle cannot pass an intersection area, the display highlights the stoppage cause and reduces occupant anxiety.

Direction-specific notification timing gives drivers enough time to confirm surroundings without reducing auto lane change opportunities.

Distributed redundant subsystems let an autonomous vehicle detect failures, stay operational, and minimize road risk without human intervention.

Iterative simulation tuning finds controller gains that account for vehicle feedback time delays, improving stability and control performance.

Location-based feedback warns drivers when expected autonomous assistance has not resumed on routine routes, reducing mode confusion.

Charged vehicles carry stored electricity from energy stations to unwired nano-grids, matching forecast and real-time demand without fixed lines.

Driver reaction history adjusts when driving assistance guidance appears, reducing repetitive alerts and lowering mental burden.

A backup control unit and sensor detect deviation from target values, isolate a faulty primary controller, and keep automated driving active.

Split sensor, processor, and power paths keep vehicle control operating through sensor or power faults for more stable navigation.

Occupant and position detection lets a shared vehicle control switch modes so one user can adjust settings without affecting others.

Factor graph bias estimation corrects probe trace and keypoint errors caused by road cross-sloping, improving vehicle mapping accuracy.

Records one continuous parking trajectory across manual driving and automatic parking, enabling convenient repeat maneuver playback.

AR/VR explanation mode lets drivers practice vehicle support functions on actual controls without affecting travel, improving feature understanding.

Automatic coupler verification, brake synchronization, and power integration help modular electric vehicles reconfigure safely and resist hacking.

User intervention records guide control model selection so mobile body commands better match driver preferences and reduce manual overrides.

Threat numbers and control barrier functions help ADAS avoid obstacles while reducing unnecessary brake or steering activations.

Dynamic weighting of track distance and overlap improves sensor fusion accuracy and reliability in low-speed merging and turning scenarios.

A central evaluation unit assigns activation states to customer vehicle functions, cutting coordination complexity and preventing control conflicts.

Smaller ACC interval proposals and inputs let occupants fine-tune following distance beyond coarse preset steps with less effort.

Priority-based arbitration selects among conflicting kinematic plans so autonomous vehicle actuators follow the intended motion without system interference.

Fleet use history along planned routes is aggregated to suggest driving assistance only where it is most useful, improving convenience and reliability.

Separating dynamic and gravitational acceleration from IMU data enables accurate vehicle roll estimation for better SLAM on inclined roads.

Multi-actuator drift control maintains a stable vehicle drift while interpreting driver inputs and correcting unsafe instability.

Mode-specific alerts handle ADK communication anomalies, warning during autonomous driving or blocking autonomous use in manual mode.

A split vehicle display shows parking target frames and hides the operation guide after input, preserving limited screen space without losing clarity.

Three coplanar coil groups actively stabilize an implantable blood pump rotor in axial and radial directions without extra magnetic assemblies.

Confidence thresholds and perception history stabilize lane-keeping assist activation, reducing unwanted switching and erroneous guidance.

Registers a driver-guided exit path instead of reversing the entry route, enabling more accurate autonomous parking exit.

Surrounding-aware driving control switches steering and speed modes only on occupant instruction, improving intuitive operation and safety.

Incremental parking-space recommendations cut user wait time by showing available options before all trajectories are fully calculated.

Maintains turn-support availability for a set time or distance after lane status changes, helping safer intersection entry with fewer driver actions.

Occupant-confirmed in-cabin simulation helps drivers practice rough-road mode activation without unwanted prompts, improving proper function use.

Preplanned lane-change and stopping strategies help automated vehicles handle highway emergencies with lower collision risk and stable control.

User feedback reweights legacy and new path models to cut intervention and reduce control nonconformity during autonomous driving updates.

Sensor-sector confidence guides which autonomous driving actions can continue after faults, avoiding abrupt driver handovers.

Adaptive distance thresholds trigger earlier display of critical nearby objects under vehicle position or timing conditions while avoiding excess alerts.

Status indicators shift to a more visible meter area during travel assist or expressway driving, improving visibility without adding distraction.

Restoring the prior driving mode from a second controller after restart helps avoid inappropriate control and maintain safe vehicle operation.

Multiple learned trajectory models are biased using sensor and map data to improve vehicle planning around objects and human driving behavior.

A pinion disconnect and bi-directional overrunning clutch maintain positive drive while letting wheels rotate independently during turns.

Directional arrows and a steering image show possible lane changes while keeping the display stable and adding audio when no change occurs.

Speed-aware locking and hall-sensor angle feedback return vehicle display screens to safe positions, reducing mistaken rotation and driver distraction.

A latching hook and actuating element secure the VAD battery-control connection while enabling deliberate release without accidental disconnection.

A sliding clamping member links one or two transmission gears to switch RC car gearboxes between 2WD and 4WD with less structural complexity.

Auxiliary HMI input adjusts calibration parameters in real time, letting drivers personalize vehicle responsiveness and stability without fixed factory tuning.

Operation and display areas shift with driving mode and steering position to improve in-car usability and prevent erroneous inputs.

Deviation between predicted and target trajectories triggers occupant alerts before autonomous maneuvers, reducing discomfort from unexpected vehicle behavior.

A conductive element near the connector bore pulls heat from high-resistance interfaces and spreads it through the header to prevent deformation.

Automated breakpoint selection uses slope and spacing constraints to build lookup tables for faster secondary safety verification in vehicle controls.

A wheel-end axial coupling shifts an inner drive gear to disconnect driveline components, cutting parasitic loads and improving fuel efficiency.

Different visual and audio alerts distinguish equipment faults from temporary auto-driving blocks, reducing user confusion and guiding action.

An aramid-reinforced delivery tube preserves curvature and orientation through the aortic arch to improve left ventricular blood pump flow.

A flat inlet guard and ceramic thrust bearing keep the blood pump shaft axially stable under pressure changes while maintaining smooth flow.

Segmented flexural rigidity helps an LVAD delivery tube cross the aortic valve and arch while maintaining tensile strength for implantation.

A thrust bearing in a distal housing constrains axial shaft motion under pressure changes, helping the impeller maintain stable blood flow.

A rotating magnetic field and discontinuous soft magnetic posts cut eddy-current heat, enabling a compact intravascular blood pump.

Integrated heating and temperature sensing in an LVAD cannula enables accurate continuous pump flow measurement without catheter-based dilution.

Control unit detects mode transitions and activates driving tasks via signals, resolving driver confusion from complex manual selection.