Driver Input Parameters TRIZ Cases

Visual background changes on the in-cabin display signal steering or pedal intervention during autonomous driving without disturbing other occupants.

Pedal duration and pressure inputs replace screen operations, letting drivers trigger vehicle functions with less visual and manual distraction.

Brake load is split across motors and service brakes using power loss maps to cut total retardation losses and improve vehicle energy efficiency.

Separating longitudinal and lateral request arbitration simplifies multi-ADAS actuator control and helps maintain stable vehicle motion.

Multi-stage prediction uses vehicle and driver data to anticipate risky behavior and trigger graded countermeasures before danger escalates.

By storing the driver’s latest choice, the control logic avoids repeated prompts and activates assistance in line with prior intent.

A two-stage driver monitor separates drowsiness from electronic device use using facial and behavioral cues, reducing false warnings.

Active control of trailer steering and acceleration helps the trailer track the lead vehicle, reducing sway, harsh forces, and damage risk.

Control variables, deadbands, and offsets are adapted for inactive vehicle functions to block unintended activation while preserving flexible operation.

A vehicle controller delays propulsion torque until brake release is complete, cutting concurrent torque wear on brakes and drivetrain.

Two SoCs compare manual driving behavior with autonomous settings in real time to personalize vehicle control and improve driver trust.

Correlates in-vehicle sensor data to infer unstated occupant needs, then adjusts feasible vehicle operations to improve comfort and safety.

A virtual steering angle linearizes tractor-trailer steering control, improving trailer direction accuracy with simpler feedback logic.

Weighted driver inputs, hold time, and dead-time filtering estimate distraction accurately and trigger warnings without complex sensor setups.

Combining in-cabin abnormality sensing with external collision risk detection enables forced emergency stopping even from a disabled state.

Continuous sensor and model-based driving analysis detects unsafe behavior, estimates reaction time, and triggers timely alerts or control.

Lower-limit torque control stabilizes driving and braking forces at deceleration end, preventing hunting despite in-vehicle network delays.

Driver state assessment shifts collision avoidance timing earlier or later to prevent crashes without unnecessary driver discomfort.

A remote signal collection unit links vehicle trim peripherals to one ECU, cutting local processors, wiring, packaging pressure, weight, and cost.

Torque-to-acceleration matching updates vehicle range estimates for changing load and trailer conditions with more accurate real-time efficiency correction.

Permission-based downshift control secures drive torque in sport driving while avoiding sideslip-triggered shifts that disturb vehicle attitude.

Temporary screen controls and permanent buttons are coordinated to show user authority clearly and block unauthorized vehicle functions.

When one vehicle setting changes, linked equipment reminders help occupants adjust related settings and avoid missed comfort or function changes.

Pedal, steering, and lateral motion cues are used to detect intended lane changes and suppress unnecessary LDW/LKA intervention.

Geofencing, ACC status, and driver monitoring enable clear hands-on and hands-off transitions that reduce confusion and improve trust.

Predefined parking zones help autonomous vehicles find compliant pickup and drop-off spaces faster while following local parking rules.

Dynamic steering torque guides a manually driven vehicle back to its preset route while easing off when driver input signals an intentional turn.

Context-aware cutoff logic links safety reaction time to alarm timing, reducing nuisance warnings while preserving timely driver risk alerts.

Subtle seat, temperature, and cabin cues are triggered by disengagement likelihood to restore driver vigilance without adverse reactions.

Sensor fusion tracks driver physiology and driving behavior to flag sleep-related conditions and trigger tiered remediation alerts for safer operation.

Offline IRL and LSTM modeling recover driver-specific preferences to predict lane changes earlier and more accurately in mixed traffic.

Correlating steering wheel torque with depth-sensed hand positions helps detect driver spoofing without added hardware and improves safety.

Closed-loop longitudinal control offsets rapid brake-to-steer deceleration by coordinating propulsion torque and transmission shifts for a smoother feel.

Vehicle conditions and stored path data trigger timely reverse support suggestions, helping drivers avoid missed backing assistance without excess prompts.

Selective differential limiting is triggered only during yaw rate overshoot, improving turning stability without dulling steering response.

Driver gaze and hand monitoring guide automated driving level transitions, reducing confusion and improving takeover readiness.

A neural network predicts personalized HMI cues from traffic context and response history to maintain driver attentiveness with less distraction.

Start resistance and vehicle state are used to adapt automatic launch in lean vehicles, reducing posture instability and improving safety.

Multi-sensor checks of steering force, grip, gaze, and deceleration help switch from autonomous to manual driving only on intentional input.

Below a low-speed threshold, torque is blended from regeneration to traction using pedal and speed inputs to prevent jerks during forward-reverse shifts.

Driving speed and acceleration profiles are used to compare powertrain options by energy use and CO2 output for personalized vehicle selection.

Adaptive torque lowering switches between release modes using steering input and hands-on status to reduce discomfort and keep vehicles stable.

Redundant actuator sensing triggers autonomous vehicle control when driver input sensors fail, maintaining safe operation until recovery.

Power-loss maps guide brake load sharing across motors and service brakes to meet retardation demand with lower total energy loss.

Driver authentication retrieves stored assistance settings and shows them at startup, helping prevent misrecognized active functions.

Seat-position-based pedal correction eases pedal effort while reducing abdominal seat belt pressure for pregnant drivers.

Obstacle height, distance, speed, and accelerator opening are used to tailor engine torque suppression and reduce collision risk during parking.