External Condition Input Parameters TRIZ Cases

Predicts object trajectories and future intersections to guide vehicle actions, improving collision avoidance accuracy with lower planning overhead.

By waiting for a vehicle in the target lane, this control approach enables lane changes without dropping the preset driving assistance level.

Using CAN-network vehicle data, this case estimates tire lift and drag to detect aquaplaning onset without adding dedicated sensors.

Factor-graph SLAM fuses trip, infrastructure, and object sensor data to keep automated vehicle localization and maneuver planning consistent.

Predicted vehicle and object trajectories trigger timely safety prompts, helping drivers confirm hazards before potential contact.

Periodic control handover based on disengagement ratio and road conditions keeps autonomous vehicle operators alert and ready.

A margin-based switch between map and camera guidance helps detect road centerline distortion and keep automated vehicle control accurate.

Speed and steering are adjusted around lane obstacles to avoid side-by-side exposure with nearby vehicles and reduce occupant fear.

Adaptive steering suppresses lane-centering near obstacles, then restores lane re-centering after passing to avoid collision risk.

Two-level route optimization links full-horizon energy planning with local MPC speed control to keep vehicle velocity profiles smooth in real time.

Cut-in probability modeling helps switch follow-up targets and adjust acceleration to avoid collisions and unnecessary braking.

Camera-based cornering signatures are matched to safe curves with time-warped key points to quantify warehouse vehicle turning risk.

When road signs are obscured, odometry-based context and image data are combined to arbitrate a reliable speed limit for the driver.

Steering control deviations and load characteristics are used to estimate road friction more reliably across changing driving conditions.

Multiple divergence metrics and learned weights automate perception error evaluation, separating material from immaterial mismatches.

Preselected target gaps and adaptive speed trajectories help autonomous vehicles change lanes safely when immediate entry is blocked.

Machine learning segments terrain from route projections to suggest vehicle actions and accessory use for safer off-road driving.

A single corrector computes separate steering and braking setpoints to keep obstacle-avoidance maneuvers stable and on path.

A watchful-waiting communication period lets remote operators send a second determination when road conditions change, avoiding abrupt assistance loss.

Center-line positioning lets the vehicle choose forward or reverse parking to match user intent, even when nearby spaces are empty.

Sensors detect when ECO mode cannot deliver enough power for a high-acceleration traffic maneuver, then switch the vehicle to a higher power mode.

By tracking target vehicle lateral velocity, this case reduces unnecessary ACC braking when a vehicle is about to leave the lane.

Multiple sensor hypotheses and probabilistic parameter modes reduce orientation ambiguity and abrupt vehicle trajectory changes.

Prioritized air spraying keeps camera and LiDAR/RADAR sensors clear in rain, improving measurement accuracy for stable autonomous driving.

Steering angle and lane position are used to soften brake-release deceleration when the driver intends to pass an obstacle.

Pre-merger target selection lets adaptive cruise control follow merging vehicles with shorter spacing, reducing abrupt braking and traffic disruption.

Dynamic following-distance control detects electric mobility vehicles, expands the safety buffer, and warns drivers to reduce collision risk.

Road slope and curb detection guide wheel angle and fallback actions so autonomous vehicles can park safely under varied conditions.

Vehicle accelerations plus suspension and tire parameters estimate road IRI more frequently and at lower cost for wider maintenance planning.

Road curvature and vehicle speed are evaluated before a curve so deceleration can be confirmed or automated with less occupant intervention.

When offroad tires change wheel-based speed accuracy, GPS reference switching keeps vehicle functions reliable and prevents component damage.

Historical wheel angle data and bend road information adapt vehicle cornering control to user habits while improving consistency and safety.

Uses map height and curve data to suppress false marking-line deviation judgments caused by road slope in automated driving.

Real-time visual feedback from an ML model helps drivers adjust acceleration habits for better fuel efficiency without added driving complexity.

Driver grip state is used to vary curve-segment speed guidance and output information, improving relevance and control on successive bends.

Filters current and past sonar distance readings with sensor movement data to reject invalid values and avoid target switching errors.

Peripheral risk comparison triggers safety checks only when moving between different area types, preserving travel flow without skipping checks.

Separate in-vehicle and remote authorization checks cut authentication time and reduce information leakage during vehicle movement control.

Map-based stop prediction lets cruise control plan speed before temporary stops, improving driver comfort and energy efficiency.

Slip condition feedback adjusts the acceleration limit of a work vehicle to reduce slipping and keep travel stable on steep or slippery surfaces.

Adaptive curve-speed control changes target speed from steering wheel grip state, improving comfort and safety without unnecessary grip prompts.

A full-route reference profile guides nested local speed optimization, cutting computation while keeping road vehicle control smooth and energy-efficient.

Personalized lateral-acceleration thresholds predict uncomfortable curve loads and trigger warnings or vehicle intervention before cornering.

Intermediate target distances smooth acceleration and braking during lane changes, improving comfort in electronic distance control.

Sensors detect an approaching ramp, then active suspension shifts front and rear ride height to avoid body contact on steep climbs.

A traffic-aware time-gap control approach adjusts ego speed from surrounding vehicle count to mimic human driving without machine learning.

Road-type-aware two-wheeler detection limits manual takeover requests to shared roads, reducing unnecessary handovers in autonomous driving.

Adaptive delay thresholds and speed control help automated valet vehicles avoid collisions when network control information arrives late.

ST-graph cell segmentation and MPQP select viable gaps and speed profiles around dynamic obstacles with lower planning complexity.

A freespace planner and remote advisor interface help autonomous vehicles escape constrained poses with forward-reverse relocation paths.