External Condition Input Parameters TRIZ Cases

Multi-stage scoring links driving load, elapsed time, and sightline search behavior to separate distraction from disease-related abnormal states.

Constraint-based trajectory selection helps autonomous vehicles handle turning pockets, crosswalks, and keep-clear areas to prevent intersection gridlock.

Graph-based actor and lane modeling improves autonomous vehicle trajectory prediction by preserving map structure and propagating interactions.

Environmental scoring helps a driving assistance controller learn recurring deceleration points faster while avoiding one-time braking errors.

Steering support activates only when inattentive driving causes unintended lateral deviation, reducing unwarranted lane-keeping interventions.

A local controller combines site rules, third-party constraints, and user preferences so unoccupied autonomous vehicles can navigate compliantly.

Obstacle classification and target lateral positioning help vehicles pass street-parked cars at intersections without unnecessary stops.

By appending features from neighboring radar tensor bins to point clouds, this case improves object detection and tracking without sending full tensors.

Default wrappers automate cloud resource provisioning for AV services, reducing manual setup while keeping deployments scalable and consistent.

Distance-specific risk areas help mobile objects use nearby target data better and generate safer trajectories with lower collision risk.

Precomputed contingency homotopies let autonomous vehicles react faster to unexpected agents while keeping trajectory planning robust.

A probabilistic tire force-slip model with confidence intervals helps vehicles adapt traction control to mud, gravel, sand, snow, and other changing surfaces.

A calibrated speed threshold model limits towing speed to prevent trailer sway, understeer, and oversteer while reducing stopping distance.

Pre-registering a lane-changing preceding vehicle lets ACC keep smooth following control and avoid unnecessary acceleration, braking, and discomfort.

Selective channel connection in a vehicle diagnostic gateway cuts unnecessary protocol state maintenance and improves multi-protocol communication stability.

Sensor-based gaze tracking adjusts cruise control and speed limiter compensation to match driver distraction, reducing unintended braking.

Waypoint interpolation with heading and curvature data smooths low-speed vehicle steering, improving comfort and reducing steering wear.

Parallel risk monitoring combines sensor status and data consistency checks to trigger warnings and independent remedial actions in autonomous vehicles.

Layered LiDAR point filtering separates raindrops by intensity and distance, improving object recognition in rainy autonomous driving.

Predictive feedforward and MPC account for speed changes on road curves to keep vehicles centered and prevent oscillation or lane departure.

Virtual hazard constraints help vehicles limit lateral and longitudinal path deviation when sensors, maps, or conditions reduce trajectory precision.

A decoupled control mode gathers operating and user feedback data for automated driving and parking without triggering unsafe vehicle guidance actions.

Component-level latency models help autonomous vehicles simulate candidate paths more accurately for safer real-time trajectory planning.

Virtual boxes and tracking histograms improve LiDAR-based lane identification and vehicle heading estimation for more stable object tracking.

Vehicle traces clustered through lane gates replace manual zero-traffic curation, producing more reliable autonomous driving trajectories.

When a driver problem is detected, the controller identifies non-controllable points ahead and plans a safe stop location before them.

Collision-point prediction and adjustment control help autonomous lane changes avoid road users when weather or visibility weakens reliability.

Adaptive preliminary deceleration and steering improve frontal collision avoidance when lateral escape space is limited and conditions change suddenly.

Real-time vehicle height sensing is compared with obstacle clearance to prevent overhead collisions caused by loads or passenger protrusions.

Predicted and actual vehicle positions are compared to validate tracker trajectories and flag collision-checking model drift.

Target steering is recalculated from current stabilizer coupling state so the vehicle can keep its collision-avoidance trajectory and stability.

When onboard driving logic cannot resolve cases like unprotected turns or temporary obstacles, remote operators guide the vehicle using shared sensor context.

Sensors estimate load and ground profile so the tractor can cap travel speed automatically, reducing accidents without heavy driver training.

Independent PAC and SAC channels score planned vehicle trajectories with a shared safety metric to cut common-cause errors and preserve ADAS availability.

Sensor data is used to detect driver uncertainty in unfamiliar traffic environments and adapt assistance to reduce distraction and improve safety.

When a primary vehicle sensor fails, hierarchical fallback control adjusts deceleration by cut-in vehicle position to avoid harsh braking and collisions.

Environmental complexity is assessed at candidate takeover locations so drivers regain control in lower-complexity traffic and road conditions.

Map-based route control adjusts turning radius and speed by section load and slippage index to curb wheel slip without unnecessary slowdown.

Hybrid probabilistic models combine car-following, lane choice, and visibility constraints to predict vehicle behavior more accurately in adverse weather.

A wide-angle camera and AEB are combined to detect A-pillar blind spot obstacles during turns and trigger steering-aware braking.

A dual planner cuts trajectory reaction latency by 70-80 ms, enabling faster evasive maneuvers when road user behavior changes.

Multiple model-based wheelbase estimates are combined to improve multi-axle vehicle control accuracy, stability, and maneuverability.

Route segments are prioritized to prompt driver assistance activation where it is most useful, improving adoption while limiting distraction.

Environment recognition distinguishes roadway from sidewalk travel and sets speed by zone and course width to improve mobile object safety.

Hazard-based two-wheeler assistance adapts to rider braking, steering, or acceleration input to avoid interventions that conflict with intent.

Updating reference positions during autonomous straight travel keeps tractor guidance aligned despite satellite position drift over time.

Camera and sensor thresholds keep the selected lead vehicle during lateral motion, reducing ACC speed oscillations without missing lane changes.

Actual coasting speed is used to update rolling resistance and compensation force, improving eco-roll and cut-off speed prediction for lower fuel use.

A learned vehicle-gap preference is used to derive acceleration and braking targets, reducing preference-learning complexity across manual and automated driving.

A separate gap planning module finds lane-entry gaps early and feeds path constraints, cutting computation for autonomous lane changes.

Real-time weight estimates let powertrain, brakes, and suspension adapt to load changes for more uniform commercial vehicle response.

When radio interference shortens smart key detection distance, assistance modes are limited dynamically to keep remote parking available.

An integrated control unit links vehicle seat modules with VR equipment to synchronize image playback and seat motion over wireless communication.

Detecting rear-lane vehicle deceleration reveals yielding intent, allowing safer lane change entry control and reduced occupant anxiety.

A drivable space consumption ratio helps autonomous vehicles assess adjacent-lane risk and adjust driving behavior before conditions become unsafe.

Ahead-of-vehicle grade detection and target-speed deceleration reduce jolts over curbs and other abrupt roadway grade changes.

Sensor-based risk assessment combines TTC, timegap, and relative speed to switch between coasting and braking without false hazard responses.

Multiple driving modes and self-learning adjust lane changes, speed, and following distance to match user preferences in autonomous driving.

Iterative filtering removes inconsistent map curvature pairs so vehicles can manage bend speed smoothly and avoid unintended acceleration.

Periodic hands-on and surrounding-check alerts keep drivers engaged during automated steering while adapting reminder timing to reduce discomfort.

Lateral accelerometer data from successive turns reveals trailer load shifts early, helping reduce accident risk and vehicle wear.

Sensor-based guidance calculates door swing radii and vehicle spacing to help park without adjacent door contact while preserving usable clearance.

Predicts minimum following distance from preceding vehicle size to time coasting, avoid close approach, and cut drag-related energy loss.

Target speed is limited by stopping distance, curve radius, and road friction to avoid unseen obstacles beyond sensor range.

A stepwise control target shift between preceding vehicles smooths steering changes and keeps vehicle tracking stable during target switching.

Maintaining adjacent-lane direction estimates cuts processor load while steering around hazards without entering an oncoming lane.

Ground contact load and tire deformation correct vehicle model estimates, improving suspension stroke speed and stability control accuracy.

Sensor-based user signatures let a shopping mobility aid match walking pace and health state, reducing effort without fixed slow movement.

Pre-downhill speed and gear targeting limits downhill overspeed while cutting foundation brake wear, overheating, and fuel loss.

Monitoring nearby vehicles' lateral position and velocity helps predict lane changes early and choose safer autonomous trajectories.