External Condition Input Parameters TRIZ Cases

Manual driving data yields a correction factor that reshapes autonomous vehicle trajectories to match driver style and improve ride comfort.

When obstacle avoidance pushes a vehicle toward an out-of-lane region, steering assist corrects driver over- or under-steer to prevent secondary collisions.

Predicted attainment time lets automated driving release or hold the lockup clutch sooner, boosting uphill driving force without unstable speed.

Dynamic sensor switching based on driving conditions reduces data interruptions and improves unsafe driver detection accuracy.

Uses heading angle and lateral position to set a new braking reference point for faster low-speed cut-in collision avoidance.

Slip detection adjusts acceleration limits during work vehicle travel to reduce route deviation and maintain stable movement on steep or slippery ground.

By estimating when a lead vehicle may cut out of its lane, the controller switches brake timing modes to improve collision prevention.

Candidate sample points and azimuth-based scoring let autonomous vehicles generate safe driving routes even when precise map data is unavailable.

Kinematic sampling fixes path points from state data to build smooth, collision-free autonomous driving paths without precise maps.

Dynamic speed-based thresholds track vehicles merging into the ego lane without relying on lane markings, improving detection in bends.

Dynamic load thresholds let a vehicle steering system detect overload risk and trigger mitigation, reducing over-engineering and cost.

When ACC detects a deceleration sign, this control logic lets driver throttle input override braking without causing unstable speed transitions.

A variable lap rate threshold adapts through a turn to cut false braking early and improve collision detection as the vehicle stabilizes.

A drivable-area distance loss trains the AV prediction layer to refine target-vehicle trajectories and reduce navigation and collision errors.

When lane changes are not possible, this case shows how a virtual line to the rear vehicle guides in-lane obstacle avoidance and speed control.

Multiple sensor modules fuse groove confidence values so ADAS can detect grooved roads and adapt steering for stable lane centering.

Sensors mounted beneath an autonomous vehicle detect small road objects early, enabling trajectory or speed changes to avoid damage and hazards.

Multiple radar sensors and surround cameras confirm objects in blind spots and rear zones, improving collision alerts while reducing false alarms.

Buffer zones, mode-switching zones, and dynamic lane markings coordinate AV transitions, merging, and emergencies on dedicated highway lanes.

Sensor fusion maps motorcycles, trailers, and stopped vehicles to roadway lanes so an autonomous vehicle can adjust its path lawfully and avoid collisions.

Predetermined operator input switches autonomous speed-hold control to powering or coasting, improving driver freedom and reducing energy use.

Compares following control modes using air resistance, road gradient, vehicle weight, and power use to choose lower-energy driving.

Asymmetric processor allocation cuts redundant lane-keeping sensor computation while preserving reliable decision-making and safety.

Adaptive speed bias and lane-change control uses adjacent-lane congestion regions to improve driving comfort and accuracy.

Accident-derived diagnostic patterns let vehicle AI check for hardware anomalies without redundant hardware, improving failure detection and safety.

Adaptive judgment conditions detect mistaken accelerator input during automatic acceleration or deceleration and limit unintended drive force.

Predicting pressure changes along the route lets autonomous vehicles smooth speed and torque changes to reduce altitude-related discomfort.

Vehicle-type-based distance correction helps adaptive cruise control avoid underestimating gaps to utility vehicles and trailers.

Adjusting front and rear brake force by vehicle speed helps a lean vehicle keep stable posture during cruise-control deceleration.

Adaptive anticipator tuning updates a vehicle bicycle model during turns to correct trajectory deviation while reducing feedback intervention and comfort loss.

A self-attention transformer associates lidar, radar, and vision detections to improve vehicle object tracking with less fusion overhead.

Sensors detect rocks propelled from the road so the vehicle can warn, slow, or increase following distance to avoid paint and windshield damage.

Timed traction and braking torque based on pitch and ride-height sensing suppresses resonance and maintains acceleration over repetitive undulations.

Regional vehicle data triggers low-viscosity oil recommendations only where mileage and environment indicate real fuel-efficiency gains.

Real-vehicle log analysis links simulation position errors to speed, acceleration, and steering states for more accurate driving validation.

Nonlinear pneumatic trail modeling estimates road friction from steering, torque, and axle load with higher real-time accuracy and lower compute load.

When branch connection length or curvature limits lane changes, the controller warns occupants early or slows the vehicle to a feasible speed.

When driver abnormality is detected, vehicle control switches braking from driver-reflected to driver-independent operation to improve safety.

Personalized driver profiles predict when manual or automated control is preferred, improving trust and use of vehicle assistance features.

Estimated entry and exit times let autonomous work machines receive conflict-aware intersection permissions that cut waiting and fuel use.

When camera and map lane markings diverge, trajectory estimation of nearby vehicles helps maintain automated driving control safely.

Adaptive safety distance lets automated lane changes continue when longitudinal guidance is active, reducing unnecessary aborts in traffic.

Road incline data tilts vehicle and road images to match actual slopes, reducing occupant strangeness and motion sickness.

Steering and brake assist are varied by detected driver recovery from distraction or closed-eye states to reduce post-assist driving risk.

When passing is predicted, the controller offsets the vehicle path while limiting lateral acceleration to reduce occupant discomfort.

Sensor and map-based crossbar pose detection helps autonomous vehicles identify the correct lane gate and time passage safely.

Sensor-based visibility assessment adjusts speed, spacing, and navigation so autonomous vehicles can handle low light and changing weather more safely.

A single park switch uses speed thresholds to sequence hydraulic braking, parking brake actuation, and park gear engagement while preventing slope slide.

By estimating vehicle pitch and roll from laser flight length, this case filters unreliable LIDAR data to improve road obstacle detection.