External Condition Input Parameters TRIZ Cases

Sets the integral driving-force term from override demand so cruise control can resume without rollback when a wheel stalls at a step.

Inclinometer and ride height data shared over V2X lets following vehicles pre-adjust suspension for rough roads with better accuracy than cameras.

Sensor-based target state estimation and MPC let an ego vehicle imitate another vehicle without V2V links while staying within safety constraints.

Sensor-based control distinguishes cabin and road disturbances, adjusting suspension force to improve stability, comfort, and energy use.

Acoustic rainfall sensing sets handover timing from automated to driver control before rain degrades sensor reliability and vehicle stability.

Driver inputs during interrupted merging switch between stop and continuation control, improving lane-change comfort without fixed decisions.

ML infers sensor limit detection distance in bad weather, enabling safe speed limits and continuous adaptive cruise control.

Transport target detection changes the driving allowance degree, reducing unnecessary autonomous control when no occupant or baggage is present.

Audio-based road water estimation sets a safe vehicle speed limit before tire slip develops, helping prevent hydroplaning on wet roads.

LiDAR point clouds and predicted vehicle path reveal road obstacles early, enabling suspension adjustment for better comfort and stability.

A lane-change cost built from time-gap, time-to-collision, and deceleration helps autonomous vehicles choose safer target-lane trajectories.

Visual fiducials let an autonomous vehicle pair with a guide vehicle and switch to follower mode for safer, lower-cost surface street navigation.

When an icy road is detected ahead, the controller raises driver override thresholds and triggers risk minimization control to prevent skidding.

Prioritized far-, mid-, and near-field gap checks improve lane-change timing while reducing sensor processing load and missed opportunities.

Adaptive driver prompts vary timing, frequency, and method to start inertia traveling while reducing burden and preserving energy-saving opportunities.

Multiple ML-generated trajectories feed a vehicle planning tree, reducing rule-based computation and scaling better to new driving scenarios.

Map-based virtual lane boundaries let the controller finish branch-lane changes before lane expansion ends, even without visible markings.

Multi-threshold braking and steering preparation improves collision response when sudden obstacles leave no lateral path around them.

Preset driving parameter groups let autonomous control adapt to driver preference and road risk without heavy real-time calculation delays.

Stored road arrow data helps driving support retain accurate turn-intent judgment when faded or changed markings would mislead deceleration and warnings.

A virtual barycentric target lets ACC anticipate lane changes and merges, reducing abrupt speed corrections and improving driving smoothness.

Calculating lateral control time and collision overlap helps vehicles adjust speed earlier for more accurate side-approach collision avoidance.

Dual TTC evaluation helps brake control respond to lane-change deceleration risk while avoiding unnecessary interventions.

When one management device fails to respond, the moving object reroutes its support request to another registered dispatcher to keep remote support running.

Dynamic speed limits from remaining path and obstacle distance smooth auto parking maneuvers and reduce collision risk.

Road merge zones are split into safety, yield, and stop areas so autonomous vehicles can adjust speed for smoother, safer merging.

Anticipates crosswinds and road slope effects by planning a pre-deviation path that keeps vehicle lateral deviation within a threshold.

Pre-checking multi-lane-change feasibility lets the system avoid abrupt manual takeover and keep the vehicle on its target route.

Real-time understeer gradient updates keep a vehicle centered through bends despite load changes, reducing steering jolts and trajectory error.

Detects tire friction changes from tire parameters and calendar-based seasonal changeovers to keep vehicle range estimates accurate.

Periodic challenge-response checks gauge driver readiness for autonomous-manual handover, improving transition safety without continuous monitoring.

A rear camera detects tow rope tension or slack to trigger towing mode, limit speed, and prevent jerking loads during towing.

Corrected remote-control paths are checked against precision map coordinates to catch mismatches and keep autonomous vehicles on a drivable route.

Cached point clouds let a LIDAR processor recover pre-event environmental history when a trigger occurs, avoiding loss from overwrite.

Tracking nearby vehicles' lateral position and velocity enables earlier lane change prediction and smoother collision-avoiding trajectories.

Predicted motion and object type guide deceleration, avoiding unnecessary emergency braking while improving safety and ride comfort.

Adaptive time-gap and collision thresholds let an autonomous vehicle choose braking intensity after a cut-in to balance safety, comfort, and flow.

Adjusts deceleration on curved roads from the predicted vehicle course, reducing unsuitable braking when the path stays within the drivable area.

Predicting speed loss from relative speed and distance lets the vehicle block lane changes that would stall during nearby merging traffic.

Position probability distributions replace Boolean collision checks, improving AEB risk assessment under sensor noise and trajectory uncertainty.

Driver notifications, task verification, and timing thresholds enable safer automatic shifts from level 2 to level 3 driving.

Environmental parking spot selection uses temperature and sunlight data to protect EV battery range and passenger comfort.

Lane subregion classification guides steering or earlier braking to avoid multiple obstacles and reduce discontinuous control.

Adapts deceleration to low- and high-friction road sections ahead, reducing slip risk while improving vehicle stability near curves or stops.

Slope-based horizon segmentation cuts predictive control computation while improving energy-efficient vehicle speed, traction, and braking control.

Boundary paths and curvature limits enable real-time vehicle trajectory updates that stay continuous and collision-free under changing conditions.

Age-specific prediction models tailor ADAS control parameters to driver age and driving style, improving safety and performance.

Warning timing shifts with driver attention and preceding-vehicle deceleration to avoid late alerts and unnecessarily early warnings.

MPQP uses ST-graph cell segmentation and QP speed profiles to avoid local optima and handle dynamic obstacles in real time.