External Condition Input Parameters TRIZ Cases

Future kinematic plans let a vehicle manager pre-command slow actuators, cutting response delay and improving acceleration, braking, and ride comfort.

An in-path band with yaw rate and lateral velocity thresholds improves imminent collision indication accuracy while reducing false positives.

Redundant VCUs with separate sensors detect failures and switch autonomous vehicles to pull-over, immediate-stop, or gentle-stop modes.

Selective caching of object trajectories preserves planning context through occlusion and intermittent detection, improving AV path decisions.

Model-based detection of crosswind disturbances lets existing vehicle sensors adapt steering gains in real time to reduce drift and improve lane centering.

Sensor data is reduced to key roadway support points where control-relevant changes occur, cutting optimizer load for faster vehicle control.

Combining sensor, map, and nearby-actor velocity distributions helps AV perception estimate uncertain road-actor states and avoid unexpected maneuvers.

Applying drive torque against braking torque restores tire grip and vehicle orientation during downhill sliding on low-μ roads.

Road-shape-aware thresholds improve lane change prediction for nearby vehicles, reducing false detections on curves in automated driving.

Shadow-track trajectory planning helps autonomous vehicles handle occluded minor-major intersections using commit regions and post-encroachment time.

Segmented vehicle motion corridors use map and sensor data to cut trajectory evaluation load while preserving collision-free autonomous travel.

Variable steering correction ramp-down uses target vs actual control mismatch to avoid early lane-keeping exit or unnecessary control.

A recorded exit path plus a generated joining path lets automated parking begin from different vehicle positions without manual repositioning.

Sensors detect construction objects and trigger a switch from automated to manual driving to reduce collision risk near work zones.

A rider-assist controller suggests setting changes only when a motorcycle is stopped or likely to stop, improving operability and reducing display interference.

Multi-step cut-in prediction adjusts warnings and distance control to driver state, reducing abrupt braking and collision risk.

Wheel well sensors track tread depth and road conditions so the ECU can adjust torque and driving modes for better traction and tire life.

Event timelines built from vehicle logs correlate repeated faults to pinpoint root causes faster and cut autonomous vehicle debugging time.

A graph neural network encodes object and scene context into nodes and edges to predict realistic, non-overlapping trajectories with lower compute.

When a nearby vehicle cuts in, the controller restricts lighter automation modes and shifts driver task level to maintain safe vehicle control.

A hybrid graph with interaction prediction and GNN trajectory modeling improves long-horizon motion forecasts in dense traffic.

Turning-state target selection prioritizes likely collision threats in front-lateral sensing, reducing processing load and stabilizing braking or warning control.

Latency-tagged vehicle data guides server selection to balance edge and cloud workloads while keeping driving assistance responses timely.

Weighted switching coefficients blend longitudinal, lateral, and suspension controllers to keep autonomous vehicles on track in dynamic road conditions.

Velocity and acceleration scoring helps a host vehicle predict oncoming passing intent and avoid unnecessary braking near roadside obstacles.

Balances passenger discomfort and speed-profile feasibility by iteratively adjusting comfort limits during autonomous vehicle interactions.

Two speed thresholds create warning hysteresis, preventing repeated ON/OFF alerts near the limit and reducing driver discomfort.

Adjusts regenerative or hydraulic braking curves as brake disc friction changes over distance, keeping pedal-stroke deceleration consistent.

At speeds below 5 mph, derating tire cornering stiffness above zero keeps dynamic vehicle motion prediction accurate.

Dual speed thresholds add hysteresis to vehicle warnings, reducing alert chattering near the speed limit and improving driver comfort.

Directly using vehicle overlap degree improves lane change destination prediction while reducing model inputs and processing load.

Threshold-based withholding suppresses redundant vehicle safety messages when maneuver predictions already cover current state, easing channel strain.

Sequentially prioritizing driving support functions lets the control unit skip later condition checks, cutting in-vehicle processing load and waste.

Preview road data and control barrier functions reshape steer commands to keep autonomous vehicles within lane boundaries.

When multiple drivable routes appear, steering assist is suppressed until driver intent is specified, reducing branch-point steering burden.

Road-model assessment of obstacle size, spacing, and vehicle speed decides when to bypass in-lane obstacles or change lanes safely.

Poor-visibility detection limits adaptive cruise control to constant speed, reducing unsafe acceleration and driver anxiety in rain, snow, or fog.

Cached point clouds are retrieved around malfunctions or accidents, preserving recent LIDAR history for fault diagnosis and condition reconstruction.

Telematics-based scoring compares autonomous driving algorithms on real trip performance and flags when vehicles should switch to safer software.

Uses lateral acceleration increase rate to warn of likely lane departure earlier, while adapting alert levels to driver steering input.

By predicting when an adjacent vehicle will merge behind, the ego vehicle speeds up to ease the lane change and reduce distance variation.

Precomputed minimum lane-indicator distances improve lane width detection on curves, supporting safer lane changes and parking.

Nearby driver-assisted teleoperation helps autonomous vehicles handle abnormal incidents faster without relying on costly central teleoperation centers.

Historical usage and location data guide partial subsystem shutdown in heavy-duty vehicles to cut energy use and avoid excess wear.

Real-time orientation sensing disables vibration on steep grades, preventing rollover and sliding while preserving autonomous compaction.

Prioritized camera views follow operator attention direction and scope to cut remote driving workload while preserving critical visual coverage.

Dynamic gap and speed control lets crossing vehicle platoons pass intersections safely with less braking, lower energy use, and higher flow.

Planned trajectory segments are checked against predicted road user behavior, then annotated with reactions to improve safety with lower real-time computation.

Historical usage and location patterns guide partial subsystem shutdown timing in heavy-duty vehicles to cut energy use and avoid excess wear.