External Condition Input Parameters TRIZ Cases

Adjacent-lane arrival time is checked before approving a platoon exit, helping vehicles overtake safely and avoid traffic disruption.

Rearward radar tracks trailer oblique angles in real time, enabling ECU control to detect sway early and improve towing stability.

Time-segmented reference curves and lateral offset prediction cut planning complexity while avoiding moving-actor conflicts.

Environmental data triggers temporary convoy gaps so nearby vehicles can merge or exit safely without breaking coordinated driving or causing extra braking.

Heatmap confidence from radar tensors captures valid hits missed by peak thresholds, improving point clouds and object detection.

Ahead-of-jam detection lowers target speed before congestion, cutting unnecessary acceleration and fuel use while keeping safe following distance.

Speech and facial baseline analysis helps detect health status changes early and trigger timely alerts or medical intervention.

Scene-specific prediction models improve preceding-vehicle state estimation at intersections, enabling more accurate fuel-saving speed control.

By predicting potholes and manholes from sensor and shared road data, the control system smooths rides and reduces vehicle wear.

A transition-demand speed limit gives drivers more takeover time during autonomous driving, reducing risk in sudden handover situations.

Selected peak and neighboring radar bins form feature-rich point clouds that preserve object detection accuracy without sending full tensors.

Air suspension, tire pressure, and wheel speed data estimate axle loads in motion, enabling remedial actions for unstable heavy truck cargo.

Integrated in-vehicle sensors assess driver readiness, vital signs, and collision severity to support safe autonomous handover and emergency action.

Multiple maneuver hypotheses are matched to sensor-based vehicle history to predict turn intent early and assess collision risk.

Driver-specific following distance is learned from historical behavior and conditions to balance cruise control safety, comfort, and energy use.

Driver feedback reshapes overtake thresholds to improve lane-change timing, safety, and fit with individual driving preferences.

Situational context, occlusion maps, and ODD constraints help detect prolonged vehicle immobility and trigger timely remedial action.

When pre-processed sensor data fails to arrive, the controller switches to raw data processing to keep autonomous driving running.

Roadgraph and sensor-aware ML estimates when nearby vehicles may go straight from turn-only lanes, enabling earlier corrective driving actions.

A speed- and steering-based turning alert zone extends object detection beyond blind spots to help prevent collisions during vehicle turns.

Sensor-based driver and environment monitoring enables timely control handoff between human and autonomous driving to prevent unsafe operation.

Weather-aware self-driving vehicles rank nearby shelters, assess cover quality, and relocate before hail to reduce body damage and repair costs.

Stored parking-position data helps a host vehicle predict nearby parking intent early and avoid blocking sudden parking maneuvers.

Variable longitudinal acceleration uses lateral and longitudinal ranges around a leading vehicle to smooth lane-change response and reduce jerky driving.

Predicted motion and safety levels filter path-relevant dynamic obstacles, reducing unnecessary braking while preserving stable collision avoidance.

Multiple mobile spotters share trailer backup control to improve blind-area coverage and avoid collisions in tight parking spaces.

When slip is detected on low-friction roads, road wheels oscillate around the current steering angle to restore traction without moving the steering wheel.

Laser-brazed filler locks a vehicle camera lens barrel after active focus and alignment, cutting adhesive curing, tolerance stack, and cycle time.

A vehicle stops before the stop line, creeps forward to reassess blind intersections, and proceeds only when the forward situation is safe.

Separates turn-driving parameters by traveling and oncoming lane sections to improve intersection behavior and safety evaluation.

Road-data and sensor-based deceleration sets target speed before a curve, improving turning stability and reducing understeer.

Recursive least squares updates understeer and tire stiffness estimates from onboard sensors, improving lane centering while towing.

Infrastructure-based stop-time prediction shifts available vehicle sensors to low power only during longer stops, cutting battery drain and avoiding rapid switching.

Location-based failure handling lets an autonomous vehicle keep operating in restricted areas under monitored safety conditions and exit safely.

Environment sensing and pedal-release input let the vehicle take over speed and distance control, easing driver workload in traffic.

A correction constant adapts target steering angle to zero point shift, reducing transverse position errors as vehicle conditions change.

A piecewise speed profile and lane-change decision logic help autonomous vehicles pass shoulder vehicles with safer clearance and less delay.

Driver fatigue and road conditions are used to shorten or extend hands-off steering warnings, improving timing in hazardous scenarios.

Sensor-driven fuzzy inference turns distance and relative distance into smoother speed control and lane change decisions for autonomous driving.

Anticipating adverse traffic changes, this control case limits acceleration to cut deceleration energy loss while preserving vehicle following.

Camera and radar data are fused to detect rear vehicle behavior, assess collision risk, and guide steering or speed avoidance.

Rational Bezier path planning uses map-based grid lanes and turning-radius control points to guide safer autonomous left turns at complex intersections.

Adaptive gas nozzle velocity is selected from detected debris type to keep autonomous vehicle lidar windows clean with lower energy use.

Multiple sensors compare load height, ground reference, and overhead clearance to warn drivers or trigger braking before low-clearance collisions.

Preprocessed obstacle data is spoken when mirror gaze stops, cutting warning confirmation delay and improving driver awareness.

Adaptive throttle and braking modes smooth vehicle speed control, reduce vibration, and avoid complex ESP hardware.

Path-based hard-brake speed limits help self-driving vehicles handle occluding objects and avoid hidden pedestrian or bicycle collisions.

Stable steering data is captured and reused to constrain steering angle and speed during automated lane changes and turns, improving vehicle stability.

Differentiated hands-off, eyes-off, and mind-off warnings help transfer control safely and trigger minimum risk maneuvers in autonomous driving.

Occupant activity and biometric feedback are used to adjust speed, acceleration, and distance settings for more comfortable automated driving.