External Condition Input Parameters TRIZ Cases

Risk-scored location cells let autonomous vehicles adjust paths around erratic or damaged remote vehicles to reduce collision exposure.

By assigning IDs to nearby vehicles and analyzing their driving patterns, the control system predicts risk early to support defensive driving.

Road profile matching improves vehicle localization beyond GNSS, enabling more precise ADAS control and real-time road-based speed correction.

Iterative scoring of perception, decision, and control systems helps isolate autonomous vehicle faults and guide targeted updates.

A machine-learned predictor paired with an algorithmic trajectory shaper improves physical realism, accuracy, and sample efficiency for autonomous navigation.

Adaptive target speed adjustment or control suspension avoids unnecessary curved-road braking when driver deceleration is already detected.

Intersection sign and stop-line detection lets cruise control confirm turn intent and hold a safe speed through turning lanes.

Navigation-based road data adjusts target speed and acceleration before and after tollgates, including curves and lane changes.

An external operating device activates manual driving in automated vehicles, preserving control during failures and supporting maintenance use.

V2V lane change feedback helps deceleration control react to adjacent vehicles and avoid unnecessary braking when a merge does not start.

When level 3 conditions are unmet, the controller uses level 2 speed and lane adjustment plus clear feedback to enable safe activation.

Frequency-layered sounds guide acceleration or deceleration during lane changes, helping drivers align smoothly with a target vehicle position.

Sensor-based prediction anticipates trailer sway and applies wheel torque early to preserve maneuverability and avoid reactive emergency control.

Vectorized map elements and road entities in a graph neural network improve trajectory prediction while reducing autonomous vehicle computing load.

A front camera feeds the windshield HUD with the hood-blocked road view, helping drivers park tight spaces without looking away.

Dual-channel velocity planning combines direction, speed, and collision potential energy to avoid obstacles from multiple directions.

A dynamic reference point in the control barrier function improves low-speed obstacle detection and timely brake or steering actuation.

Variable pedal mapping adapts to automation level and takeover conditions to reduce incorrect pedal use and shorten driver takeover time.

Risk-based overlap mapping lets the vehicle react to oncoming lane intrusions early, improving readiness before emergency avoidance.

Reachable-boundary checks let an AV decide when to steer while stopped near obstacles, lane marks, and drivable-area limits.

Compressed latent representations let an autonomous driving model cut sensor-processing cost while preserving reliable control decisions.

Wheel position and coupler angle sensing let autonomous vehicles estimate trailer mass distribution and inertia for stable routing and speed control.

A courtesy-weighted lane cost function helps autonomous vehicles create safer merge buffers without overriding core safety constraints.

A configurable visual indicator embedded in the video stream helps detect freezing, delay, jitter, and other remote vehicle sensor faults.

Predictive coasting replaces abrupt braking in adaptive cruise control to maintain headway, cut energy loss, and reduce component wear.

An AI warning model combines lane, steering, and driver behavior data to detect unintentional lane crossing while reducing unwanted alerts.

A logic arbitration module balances planning and safety control using function and risk assessment to improve autonomous driving safety.

Location-based acceleration limiting slows autonomous vehicles near crosswalks, then restores speed to reduce pedestrian collision risk.

Configurable bandwidth lets upper and lower vehicle controllers coordinate motion support devices without loop interference or unstable response.

A deep-learning road severity estimator and meta-model improve real-time stuck prediction for off-road vehicles before they bog down.

When AV data is delayed or missing, remote assistance can disable or restrict stale actions to protect decision accuracy and safety.

ML outputs are translated into visual navigation constraints so remote operators can resolve AV error events faster and more accurately.

Grouped remote assistance lets one operator handle multiple stuck autonomous vehicles in congested areas, cutting delays and operator load.

By holding a constant target speed between closely spaced turns, this case cuts unnecessary acceleration, energy use, and driver discomfort.

Dynamic yaw-rate validation corrects vehicle model drift and detects degraded automated driving before control stability is lost.

Positioning-signal trajectory analysis helps vehicles detect likely user impairment despite poor lighting or obstacles and trigger control actions.

Fusing on-board sensors with external weather data, this model estimates road wetness to adjust driving, routing, and cleaning actions.

Driving features and operation frequencies are used to classify driver style and adjust acceleration and deceleration in real time.

Wheel angular velocity and drive-wheel slip are combined to estimate tyre pressure accurately without costly sensors or slow deflation detection.

A cascaded ML pipeline balances prediction accuracy and computation time for autonomous vehicle motion planning under limited processing resources.

Real-time 3D renderings turn autonomous vehicle perception outputs into human-verifiable scenes for safer evaluation during driving.

Fused camera and radar data let the ADAS ECU detect potholes, debris, and speed bumps early enough to brake, steer, or adjust ride response.

Driver behavior monitoring validates assumptions behind in-vehicle prompts and action timing, improving assisted driving reliability.

Adjusting longitudinal acceleration from lateral motion and cargo or occupant data helps platoons keep spacing while improving comfort on curves.

Route-based slip limits set acceleration, braking, and target speed so unattended dump trucks keep grip while shortening braking distance.

Lane departure intervention points and vehicle behavior are combined to detect, size, and classify road obstacles across wider areas.

Trajectory optimization links path points to speed under energy constraints to smooth turns, cut energy use, and improve driving safety.

Stored forward trajectory and road surface data let the controller correct the return path for stable automatic reversing on bad roads.

Stored onboard rules let vehicle controllers evaluate trigger conditions locally, cutting network delay and preserving automated actions during outages.