External Condition Input Parameters TRIZ Cases

Neighboring pixel data guides illumination power and integration time control to reduce saturation and improve vehicle ToF sensing reliability.

Alternating camera control between two image controllers preserves image quality, cuts power use, and supports fail-safe vehicle modes.

Predicted actor trajectories let the AV block conditionally disallowed maneuvers early, cutting planning load and improving route predictability.

During off-road driving, clutch temperature thresholds trigger driver warnings and engine torque limiting to prevent overheating and extend clutch life.

Real-time sensor and map fusion renders road curvature as a 3D lane view, helping drivers judge upcoming bends and needed intervention.

Radial acceleration measured inside tires links deformation to body motion, improving road unevenness estimation without vehicle accelerometers.

Road-shoulder stop selection and collision-risk checks guide a vehicle to a safe emergency stop as traffic conditions change.

Interior camera processing detects missing hands on the steering wheel near hazards, triggering alerts to prevent ADAS misuse.

Turn-path and predicted-obstacle risk zones help ADAS detect pedestrians more accurately during turns and trigger collision avoidance without extra hardware.

Mixed stereo, monocular, and predicted speeds stabilize 3D object tracking when raindrops or blur corrupt parallax data.

Sequential deceleration, low-speed pull-over, and stop control let a vehicle reach the shoulder with less occupant discomfort.

Lateral acceleration change and jerk define plausible vehicle body roll rates, reducing sensor redundancy and model complexity.

Detects which off-road wheels are spinning and guides traction aid placement, helping solo drivers recover from sand or snow.

When adjacent lanes are congested, the controller lowers target speed to cut relative speed and smooth ACC driving comfort.

Weighted perceived safe speed lets a vehicle pass pedestrians with balanced safety, comfort, and progress instead of stopping too conservatively.

Segmented crosswalk risk areas help automated vehicles control speed and steering around moving objects while reducing unnecessary braking.

Scenario-based reference path and offset control stabilizes lane-keeping on curves and straights, reducing over-correction and tracking anomalies.

Assesses driver incapacity and shoulder traffic before choosing a shoulder stop or lane stop to reduce collision risk during emergency stopping.

When a driver issue occurs, the planner checks shoulder safety conditions and stops on the shoulder or in-lane to reduce collision risk.

Body and driving scenario data set boundary conditions so automated vehicle control matches driver preferences while maintaining reliable guidance.

A drone sensing road surface, roughness, and incline ahead helps vehicle dynamics control adapt earlier on damaged, off-road, or low-visibility routes.

Driver intent detection and feedback update autonomous driving behavior to reduce conflicts, manual takeover, and repeated mode switching.

Precomputed safe sets let a vehicle controller reject commands that would block a future avoidance maneuver while keeping real-time computation low.

Manual driving trajectories are compared with sensed paths to update autonomous planning around driver habits without weakening decision reliability.

Precomputed backup trajectories let follower vehicles handle V2V loss or hazards safely while reducing onboard sensor complexity.

Driver activity and condition sensing tailor take-over timing, alerts, and content to improve readiness during autonomous-to-manual driving transitions.

Trailer motion is estimated from tractor signals and first-trailer position data to detect instability without sensors on every trailer.

Environmental sensing detects motorcycles near adjacent-lane convoys so trajectory ranking can avoid hard braking while preserving safety and comfort.

A vehicle selects the emergency notification direction from location and environment data, suppressing light elsewhere to conserve power.

Auxiliary sensors and actuators sustain autonomous driving after primary sensor abnormalities.

Camera and radar sensors determine real horizontal distance between vehicles and lane markings for precise detection.

Compensates steady-state lateral deviation on straight roads to eliminate swaying and improve self-driving stability.

A vehicle travel control apparatus adjusts drive amount limitation based on detected accelerator pedal operation state during automatic stops.

A data acquisition device maintains optimal positioning using integrated sensors to detect vehicle movement and terrain tilt.

A vehicle transport management device selects appropriate transport equipment based on acquired vehicle information to ensure optimal lifting and conveyance.

Controller adjusts collision avoidance assist timing based on driver visual perception and vehicle approach direction.

A passenger bus monitoring system detects vehicle proximity and issues warnings to maintain safe distances.

A collision avoidance control system adjusts transfer prediction times based on vehicle trajectory curvature to calculate precise vehicle courses.

A vehicle orientation control device adjusts drive and braking forces to stabilize the wheelbase during slope starts.

A vehicle control system adjusts cruise speed during cornering maneuvers using real-time steering sensor data.

Correlating radar and lidar returns detects fog density, enabling accurate distance measurement despite light scattering.

Vehicle control system adjusts steering gain based on detected acceleration rate and actuator position.

Computing system modifies vehicle operation instructions by calculating modification factors from user profiles to emulate specific driving behaviors.

GPS controller analyzes location and time data to detect operator intoxication, drowsiness, or fatigue without manual input.

A vehicle controller calculates lateral offset and overlap index to adjust avoidance control timing based on predicted object paths.

System generates synthetic training data by combining observed behaviors with rare events to improve model preparedness.

A vehicle control device shunts the host vehicle to a safe location when real-time environmental conditions deteriorate during automated driving.

A calculation unit adjusts the steering threshold for manual driving based on obstacle direction to improve operability.

A vehicle control system evaluates route topography to activate auxiliary braking devices for wear reduction.

A vehicle control system calculates correction parameters from traffic condition differences to stabilize speed.