Driver Input Parameters TRIZ Cases

Tailored lane-merging acceleration profiles adjust to driver tendency and flag pedal deviations to improve merge safety and comfort.

Adaptive motorcycle following control switches overtaking assistance by rider intent and status alerts to improve stability and drivability.

Seat and backrest thresholds govern autonomous mode, restrict unsafe adjustments, and speed manual takeover when control must return.

Obstacle-aware driving force suppression manages sudden accelerator input during autonomous-to-manual switching to reduce misoperation risk.

Sensor-based emotion detection tailors driving assistance and alerts when excessive driver emotions threaten safe vehicle control.

Parking-lot-aware collision control adjusts braking and drive suppression after sudden pedal input to cut secondary impacts without unnecessary braking.

Short- and long-term gaze tracking helps detect inattentive drivers, escalate warnings, and trigger safe ADAS disengagement.

Independent safety control evaluates autonomous driving kit performance from traffic participant and occupant data to catch abnormalities and prevent collisions.

Independent left-right wheel force correction uses yaw moment and steering ratio control to preserve steering effectiveness at high lateral acceleration.

Operator risk notifications relax activation conditions or raise avoidance control to keep safer margins from nearby obstacles.

Activation time and collection mode are used to estimate waste weight without extra sensors, reducing measurement cost and delay.

Tailored pacing models use sensor, traffic, and driver data to re-recommend refused assistance functions when need is high enough.

Multi-axis acceleration sensing estimates passenger comfort in real time and alerts drivers to adjust driving style during trips.

When in-car conditions trigger cautious driving, external mode notification helps nearby road users understand the behavior and avoid collisions.

A reduced torque gradient during clutch closing limits speed mismatch, cutting overload and heat in vehicle starts from standstill.

State-of-health checks block unsafe autonomous driving and trigger operator handover when vehicle control systems degrade.

State-of-health estimation blocks unsafe autonomous mode activation and triggers operator handover when vehicle control faults are detected.

Occupant-aware speed control slows a mobile object before roadway-to-sidewalk entry, improving transition safety without unnecessary road-speed loss.

Offline IRL and LSTM training recover driver preferences to predict lane changes earlier in mixed traffic with human-driven vehicles.

Detecting post-ignition acceleration without pedal or torque input identifies towing and blocks ADAS braking or steering actions.

Separate internal and external HMI termination with escalating alerts helps autonomous vehicles hand control back safely during faults.