Driver Input Parameters TRIZ Cases

Predicting second faults after an initial malfunction lets the vehicle warn the driver and limit speed or power to match reduced capability.

Sensor fusion shifts HUD position and color to match driver gaze and road context, improving visibility without obscuring the windshield view.

Abstract target behavior lets a vehicle central computer control diverse actuators centrally while reducing hardware complexity and easing standardization.

Counts repeated lane support interventions to detect driver unresponsiveness, then slows and recenters the vehicle to reduce lane-drift crashes.

Eye gaze and preview-point tracking let ADAS predict intended trajectory and align vehicle motion with driver intent for more natural control.

Collision alerts are adjusted during motorcycle following control, suppressing warnings during overtaking acceleration to protect rider focus and safety.

By classifying driver inputs and limiting acceleration while preserving braking and steering, this case helps prevent sudden acceleration accidents.

Different delay intervals after mode selection let drivers switch from unavailable automated driving modes before error messages appear.

Adaptive inter-vehicle distance diagnosis uses acceleration and deceleration inputs to judge driving skill more accurately and avoid false positives.

Camera-based occupant monitoring detects when both hands stay occupied during autonomous driving and prompts faster steering takeover.

Brake-release and assistance-request detection lets ACC restart smoothly from a stopped vehicle without accelerator input or extra switch steps.

Early self-steering gradient deviation detection triggers braking or torque intervention before vehicle instability causes major path deviation.

Driving style models guide PID-based speed control to cut abrupt acceleration and braking while keeping target speed response aligned with driver habits.

Steering angle deviation is used to detect instability early and trigger actuator intervention before a vehicle departs from its intended path.

Adaptive in-cabin charge alerts use A/B-tested display variants and user interaction data to improve response to range and charging notifications.

Bottom-layer fault classification enables self-recovery of minor video I/O faults, avoiding unnecessary AD function degradation and takeover.

Line-of-sight data disambiguates voice commands to identify the intended apparatus and reduce incorrect operation.

Different wait times based on mode availability reduce error messages and smooth switching between automated driving modes.

Real-time collision control compares impact angles and targets to execute the lowest-damage strategy for passengers and vehicles.

Unknown drivetrain loss torques are estimated from torque balance to improve starting, gear engagement, and gearshift control precision.

By separating main and non-main occupant voice and facial inputs, the vehicle avoids unintended route changes and follows confirmed intent.

When lane change intent is detected, control gain is reduced with lateral deviation to smooth steering torque and cut driver discomfort.

Adaptive acceleration thresholds rate aggressive driving and give real-time and trip-end feedback to cut energy use, wear, and stability risks.

Autonomous reversing starts only when towing speed and steering torque stay within limits, improving stable guidance of coupled vehicles.

Multi-modal occupant state estimation guides route and surrounding changes to match emotional and physiological preferences with less travel stress.

Driver-specific ADAS warnings are updated from identity and response patterns to improve attentiveness without relying on generic alerts.

Filter-based driver profiles adjust autonomous vehicle actions to match individual driving style while safety filters keep decisions within safe limits.

Recommended driver-assistance settings are split into auto-change and approval types, with approval screens held until the vehicle stops.

Driving ability tests set recommended assistance values for each registered driver, improving personalization and reducing accident risk.

Target motion parameters are corrected to fit actuator limits, helping multi-axis vehicle control maintain ride comfort and prevent motion sickness.

Facial-action detection and probability scoring trigger only effective alerts that help prevent autonomous driving control-ending actions.

Dynamic steering correction is reduced when driver input is detected, improving obstacle avoidance while preserving lane stability.