External Condition Input Parameters TRIZ Cases

Segmented assistance systems evaluate action frequency to resolve the contradiction between measurement precision and device complexity.

Generates fallback trajectories using second uncertainty distributions for safe stopping when localization improves.

A prediction system combines direct sensor data with indirect historical indicators to estimate future target object movements.

Polynomial curves create smooth acceleration transitions in adaptive cruise control, eliminating abrupt jerks during mode switches.

Stationary objects transmit non-movement data to calculate vehicle speed, resolving urban satellite signal blockage errors.

A control device switches automated driving vehicles between scheduled controlled routes and unscheduled non-controlled paths.

A vehicle control device sets lane change parameters based on recognized lane types.

Relocating detection to roadside infrastructure eliminates complex vehicle sensors, reducing fuel waste through proactive speed guidance.

A speed controller segments vehicle standstill into dynamic and intermediate phases to maintain driver awareness during automatic restarts.

Segmented boundaries trigger fail-safe actions when deviations occur, resolving the trade-off between control simplicity and adaptability.

A driver attentiveness game system engages operators through interactive stimuli and input analysis to maintain focus during semi-automatic driving.

A dual-network in-vehicle architecture connects autonomous driving components via separate communication paths.

A steering wheel contact detecting unit uses embedded electrodes to identify driver hand presence and enable rapid operation mode transitions.

Processor compares theoretical and measured lateral acceleration to generate an error term, adjusting ADAS control limits when deviations exceed thresholds.

Occupant identification systems retrieve stored driving restrictions to enforce safety parameters without manual passenger input.

A control device evaluates relative speed and acceleration to activate a target object display only when imminent distance control intervention is required.

A backup control unit adjusts vehicle commands via a predictive model, correcting steering deviations to maintain stability when primary systems fail.

A vehicle control system uses imaging devices to detect driving surface types and automatically configure subsystem modes.

A vehicle speed control system applies positive and negative torque to wheels based on target speed values.

A vehicle path prediction method parameterizes a model using current position and direction indicator activation data to estimate future driving trajectories.

A vehicle control apparatus calculates requested acceleration using feedback and feedforward signals to execute following travel.

A controller calculates expected driving routes using vehicle dynamics models to detect following-route deviations from planned paths.

A method determines vehicle-specific friction coefficients using quality characteristics derived from standardized maps.

A measurement space region adjusts its bottom surface position based on inclination data to define obstacle detection zones.

An autonomous vehicle system identifies target stopping locations by analyzing request features and road conditions to execute speed-reducing maneuvers.

A unified classifier predicts driving paths using standardized features, reducing development effort while maintaining accuracy.

Autonomous vehicles emit and detect gestures to coordinate motion paths, resolving right-of-way conflicts without human intervention.

A controller adjusts deceleration assistance timing based on road grade to match driver expectations.

A vehicle braking method determines road friction by inducing controlled slip on a single wheel.

A collision alert system generates takeover messages by comparing autonomous and manual driving trajectories.

Drive-control maintains battery storage margin during running periods so regenerative braking effectively converts kinetic energy into stored electrical power.

A driving control apparatus adjusts override thresholds based on environmental conditions to manage driver intervention during automated lane keeping.

A vehicle control system records critical driving situations during a learning phase to generate personalized warnings.

A driver assistance system processes forward and side sensor data to predict collisions and output steering signals for avoidance maneuvers.

Segmenting the estimator with dual forgetting factors resolves the trade-off between computational complexity and fast-changing crosswind estimation precision.

A vehicle display controller switches visual details to notify occupants of driving assistance availability before automated driving begins.

A remote parking control apparatus uses a dynamic soft button on a display to manage vehicle movement and speed via touch gestures.

A vehicle travel control apparatus generates target acceleration values using sensor and communication data to manage host vehicle motion.

A vehicle control apparatus determines interacting vehicles and acquires occupant emotions to generate weighted average control feature parameters.

Dynamic confidence thresholds adjust for traffic density, preventing premature deactivation of automated driving modes.

A terrain assist apparatus controls vehicle speed and wheel slip via an electronic control unit to extract vehicles from sand.

Electronic control unit monitors bend angle and driving space to prevent collisions during reverse maneuvers.

Driver information system provides advance manual control handover notifications based on road section appropriateness.

Dynamic chassis control adjusts suspension and drivetrain parameters for winding terrain.

A speed planning system generates tapered speed limits for autonomous vehicles to control trajectory candidates.

A target path setting unit corrects lateral position based on lane width to stabilize vehicle trajectory.

Electronic control unit limits output torque to prevent unintended acceleration at low speeds.

Neural network determines rolling limits using road geometry and vehicle data to resolve reactive control trade-offs.

A method issues takeover requests when vehicle communication links fail.

A vehicle motion control system calculates longitudinal acceleration command values based on curve shape and vehicle position to adjust deceleration profiles.