Driver Input Parameters TRIZ Cases

Monitored driving parameters quantify autonomous-to-manual handoffs, improving takeover risk assessment, driver feedback, and liability analysis.

When a manual-transmission engine stalls, cancellation is delayed until vehicle stop so contact avoidance control can continue.

Threshold-based pedal input detection distinguishes erroneous acceleration from true takeover intent to avoid unintended autonomous driving release.

Passenger-specific motion sickness models use status and motion data to predict discomfort and guide vehicle control that minimizes nausea.

Machine learning maps driver history, trial speed data, and regional driving patterns into AV control parameters for personalized behavior.

Variable detection timing based on vehicle speed and assistance mode cuts false driver inattentiveness alarms without delaying alerts.

Driver grasp and consciousness sensing shifts collision avoidance timing earlier or later to prevent crashes while limiting driver discomfort.

Variable distance thresholds by front object type help curb mistaken acceleration and avoid unnecessary limiting control.

Engine torque is adjusted by braking mode and pedal input to avoid sudden power cut during brake override and keep deceleration smooth.

Real-time heart rate and stress data adjust vehicle acceleration limits to balance gradual pedal control with faster response and safer driving.

Preemptive vehicle posture control uses occupant motion sickness sensitivity to adjust target values before symptoms develop.

SWAN models driver trust from multi-modal time-series data so autonomous vehicle systems can adjust in real time with less frustration.

Adaptive braking changes target deceleration when an obstacle is detected, using accelerator input to better match driver intent and safety needs.

Adjusts distracted driving detection time by vehicle speed and obstacle margin to trigger more appropriate steering and braking control.

Phase-specific override logic suspends driver alerts on strong steering input but keeps collision avoidance active when contact risk increases.

Precomputed launch torque curves enable open-loop engine torque control for faster drag-style launches without wheel-slip feedback delays.

Gas pedal input is corrected with real-time acceleration feedback so the vehicle can better match a target car's behavior and keep safe spacing.

Partial vehicle control lets the driver be assessed against safety constraints before full ADS hand-over or automatic return to ADS.

Acceleration is suppressed after intervention stop cancellation to avoid sudden pedal-driven surges while restoring occupant vehicle control.

Configuration-based run segment selection focuses autonomous vehicle simulations on behaviorally significant scenarios, reducing wasted validation effort.

Delayed torque profiles smooth high-speed acceleration and deceleration changes to reduce passenger discomfort and motion sickness.

A linear performance index lets drivers interpolate between endpoint powertrain settings, adding flexible vehicle response without many fixed modes.

Mode switching from accelerator-based to target-deceleration control helps vehicles reach the needed speed for different deceleration targets.

A centralized vehicle manager arbitrates ADAS kinematic plans and selects stopped-state hold modes to simplify ECU interfaces and cut communication load.

When map- and environment-based path predictions diverge, a combined third path supports safe transition from hands-off to hands-on driving.

Unused vehicle features are offered across a driver's other vehicles based on actual usage, improving feature utilization without static assignment.

Tactile inputs from the steering wheel, seat, and seat belt predict driver takeover readiness for safer, smoother Level 3 handover.

Dividing the driver's viewing range into zones lets monitoring switch between face and eye analysis for more accurate distraction detection.

Future trajectory optimization uses trial wheel torques to unify steering and course control, improving stability, yaw response, and drivability.

Obstacle detection changes return-torque response so drivers need less force for avoidance steering while preserving normal steering guidance.

Brake override counts are used to tune deceleration sensitivity, aligning vehicle support control with individual driver behavior.

When autonomous driving fails, the diagnosis unit checks on-spot recovery and manual driving capability to guide restoration or towing.

External sensors detect visual and audible warnings, then trigger in-cabin alerts when insulation or distraction prevents driver awareness.

A controller opens the clutch in midair and matches wheel speed before touchdown to reduce drivetrain stress during off-road jumps.

Separate high- and low-level support controls cut switch count while preserving reliable activation and simpler driver operation.

Biometric checks, breath sensing, and in-cabin monitoring stop passengers from taking the sobriety test for the driver.

Driver attributes guide takeover alert timing and type, helping autonomous vehicles hand control back smoothly and safely.

Two-stage arbitration balances driver acceleration input with ADAS longitudinal acceleration limits for safer, more precise speed control.

An LLM-based ADAS voice assistant uses vehicle state and location data to answer driver questions quickly enough for safe in-motion use.

Driver posture is assessed during automated driving so alerts are issued only when manual takeover readiness falls below the allowed level.

A Doppler map predicts the angle-of-interest so radar processing can narrow steering vectors and obtain high-resolution DOA with less computation.

Partial vehicle control under PCS constraints lets the system assess driver readiness before full ADS hand-over or safe return to automation.

A split arbitration scheme traces which application drove a vehicle actuator request without assigning app IDs, avoiding rework when apps change.

A held ACC departure input lets drivers restart from a stop without waiting for gap timing, then cancels if the allowed time expires.

Estimated acceleration from vehicle speed lets the control unit cap motor torque for steadier performance across load and road gradients.

Adaptive audio feedback switches between continuous behavior-linked sound and segment reward cues to improve driving awareness without added distraction.

Sensor-based attentiveness monitoring adjusts in-vehicle XR content in real time to cut driver distraction and keep focus on the road.

When limp-home travel starts after DS-VCIB communication loss, the control program switches from autonomous to manual mode to prevent control interference.

Driver pedal operation is used to infer object recognition, enabling deceleration control without a camera and reducing vehicle cost and complexity.

A vertical TX and horizontal RX antenna layout with EBG patterns cuts direct coupling, suppresses grating lobes, and improves in-cabin detection.