External Condition Input Parameters TRIZ Cases

Sensor fusion updates vehicle width for trailers and extended mirrors to warn of side impacts and avoid costly mirror damage.

Verification data from multiple vehicles is used to validate updated autonomous driving models before broader fleet deployment.

Calculates when ACC should start speed adaptation so target-vehicle matching stays perceptible to drivers without unnecessary braking.

Injected right-of-way labels let an autonomous vehicle planning stack learn yield-or-assert behavior for safer, less hesitant road interactions.

Real-time tire vertical load prediction guides torque vectoring to prevent repeated wheel slip and roll overshoot during turns.

By evaluating lateral offsets of two preceding motorcycles, the control system picks the right target vehicle for smoother, safer group riding.

A vehicle master device synchronizes program rewrite progress between center and in-vehicle displays to prevent inconsistent status updates.

Maintains collision avoidance control when a moving obstacle is briefly obscured, enabling faster braking after reappearance.

Weighted wheel speed, GPS, and driveline inputs improve low-speed vehicle estimation accuracy despite axle lash and sensor faults.

Precomputed intersection visibility maps guide autonomous vehicle positioning and driving behavior when traffic lights or signs are occluded.

Road gradient and traffic-aware speed adaptation helps vehicles prepare earlier for exit lane changes and avoid missed gaps in dense traffic.

Timed visual and audible alerts detect prolonged hands-off driving and high lateral acceleration to guide safe return to manual control.

Before autonomous driving starts, the controller checks wiper, headlight, and air-conditioner modes to avoid visibility loss and handback.

When nearby vehicles behave differently, the ego vehicle tracks speed from a second target while keeping distance to the first to avoid sudden maneuvers.

Driver awareness and intended-path tracking help lane support avoid false interventions while correcting unintended lateral deviation.

Sensor fusion estimates trailer and payload parameters, then updates control confidence to adjust speed and turning during autonomous towing.

Sensor-based lateral object distance triggers selective acceleration limits, improving ride comfort and safer passing near pedestrians.

Adjacent-lane vehicle group modeling with ARX prediction improves lane-change timing, traffic flow, and driving safety.

When a limit situation triggers a minimum risk maneuver, hazard lights stay active until the vehicle stops, blocking unsafe user override.

Weather, wiper, and road data are combined to cap vehicle speed on slippery roads and reduce accident risk in rain or snow.

Speed-based steering limits balance front and rear wheel angles to reduce sideslip and keep four-wheel steering stable across vehicle types.

Combining reflected and transmitted radar waves improves surrounding vehicle recognition accuracy and collision detection in ADAS.

LSTM-based slip estimation filters noisy wheel, torque, and acceleration data to improve off-road vehicle speed accuracy.

Variable spacing between trajectory points helps vehicles track curves and changing road conditions with better control accuracy and stability.

Real-time rear-traffic assessment lowers cruise minimum speed when safe, improving downgrade energy savings without disrupting traffic.

Predicting whether a stopped vehicle will remain stationary helps autonomous vehicles overtake safely, reducing unnecessary braking and congestion.

Dual-range GPS and camera detection enables smooth deceleration and centimeter-level stopping at stop signs beyond camera-only range.

Predictive BoS hazard classification uses lead-object motion and road context to set risk zones and constrain AV speed smoothly.

During ACC, the controller increases headway and limits acceleration based on remaining fuel and destination distance to cut fuel use.

Decoupled longitudinal and lateral MPC planning improves trajectory feasibility, comfort, and stability in dynamic traffic.

Adaptive pedal-to-torque mapping increases deceleration near lead or neighbor vehicles, easing one-pedal driving stress during lane changes.

Digital map curve analysis identifies local maxima and interpolated bend speeds so vehicle control stays within lateral acceleration limits.

Spatio-temporal road regions cut maneuver planning complexity, enabling real-time lane-change and lane-keeping control in automated driving.

Preplanned backup trajectories let follower vehicles maintain spacing and stop safely when V2V links fail or hazards arise.

Transfers cabin setting preferences between vehicles by learning outdoor conditions and transient climate changes to keep user comfort consistent.

Acceleration control is adjusted to vehicle capability and road conditions to limit overshoot, fuel loss, and ride discomfort.

Image-based lane detection sets and filters merge position candidates in two-lane merging to improve safe, accurate vehicle merging.

A waiting-time and plate-based arbitration rule lets automated vehicles resolve equal-road crossings safely without vehicle communication.

Combining load sensing with dual-polarized radar, this case estimates wheel force capability from normal force and road friction for safer motion control.

Dynamic distance bounds use relative vehicle speeds and deceleration assumptions to reduce adaptive cruise control collision risk.

When onboard perception fails, a nearby mobile guides motion planning with external movement data so the impaired unit can reach parking or maintenance safely.

Epistemic uncertainty is checked at each traffic prediction step so behavior planning uses only reliable forecasts, improving safety and reducing compute load.

A self-learning ADS adjusts driving parameters by scenario and user feedback to personalize control and reduce manual intervention.

Mode-specific control algorithms match follow and cruise conditions to improve longitudinal driving efficiency, safety, and ride comfort.

Backscatter change detection uses polarimetric radar to trigger physical road friction probing only when conditions shift, reducing wear and discomfort.

Sensor-based motion planning adjusts lane change, slowing, or stopping by unknown object size and position to protect safety and traffic flow.

Dynamic tire rolling resistance from pressure, temperature, and tread depth corrects vehicle range estimates as tires age.

Traffic-rule categories and weighted risk scoring help autonomous driving choose the least harmful action when violations cannot be avoided.

Predicted vehicle states are checked against safe sets to block control commands that would prevent a future situation avoidance maneuver.