External Condition Input Parameters TRIZ Cases

A continuous towing point replaces unstable threshold-based virtual walls, smoothing speed changes while preserving collision avoidance.

Ground-truth occupancy grids and BIP optimization improve autonomous vehicle sensor placement by balancing safety-critical perception and compute load.

Pedal release and low-speed detection let the vehicle hold an initial creep speed, reducing driver strain across changing road and load conditions.

Road-boundary sensing identifies lane merges, then classifies moving and stationary objects to avoid sudden braking in autonomous driving.

Rear-axle trajectory control lets articulated and single buses share one BRT path while adapting steering and acceleration to weight and road friction.

Detects vulnerable road users behind a vehicle and triggers route, speed, or lane changes to reduce rear-end collision risk in city traffic.

Driving force is limited by brake temperature thresholds to reduce brake fade while avoiding speed-based control that increases driver discomfort.

Separate radar bounding boxes and velocity tracking for each articulated vehicle section improve localization on curves and in close traffic.

Mounting the grip heater controller inside the meter cover improves water and dust protection while avoiding side cowl removal during installation.

Real-time tire-road friction estimation guides braking or steering choices to avoid objects and improve control on low-friction roads.

Multi-threshold driver alerts and object-type-aware braking or steering help vehicles respond when lateral avoidance space is limited.

Local 3D wind sensing from a side-mirror pitot tube replaces unreliable remote weather data and helps automated vehicles adjust driving tasks.

Multiple speed-based judgment schemes improve lane change intent detection from vehicle position and motion for smoother, safer control.

Occupant stop requests are handled by ranking candidate stopping locations from map and sensor data, then replanning the AV trajectory as conditions change.

Stored parking methods linked to specific parking sections let shared-vehicle users skip repeated selection and start automatic parking or exit more smoothly.

Brake temperature feedback limits acceleration instead of vehicle speed, reducing brake fade risk while easing driver discomfort.

HUD notifications highlight moving objects and predict the next camera display, helping commercial drivers manage attention across multiple screens.

Brief accelerator taps let assisted longitudinal control raise acceleration on demand without disengaging vehicle guidance.

Objective driver retirement scoring combines vehicle data and observed inputs to classify older drivers and deliver personalized guidance.

By shortening or extending following distance when a rear adjacent-lane vehicle may cut in, the control reduces rapid deceleration and driver discomfort.

When motorcycle cruise control slows too much behind a preceding vehicle, switching to speed maintenance at a reference speed helps prevent instability.

Monitored curve lateral acceleration is compared with expected values to tune ADAS speed proposals to driver behavior and sustain trust.

Pedal input changes hill descent target speed, letting drivers adjust downhill pace without leaving the steering wheel.

Tracks lateral and combined motion of the lead vehicle to adapt following distance and improve safety in poor visibility.

Stored parking methods linked to specific sections let shared vehicles recall and propose prior parking settings, reducing repeated user input.

Two-stage machine learning adjusts tire pressure and suspension to match driving conditions and driver preferences for wear, comfort, and fuel economy.

Camera-based lane width validation detects diverging lanes and keeps steering aligned to the opposite lane line for stable vehicle control.

Camera and sensor fusion adjusts following distance when the lead vehicle shows lateral motion, abnormal states, or lighting cues.

Using motor torque, rack force, and slip angle, this case estimates road friction faster for vehicle control on ice, snow, and hydroplaning.

Intermittent braking on moderate downhill slopes prevents self-excited brake vibration while keeping vehicle speed near the set value.

Low-pass filtering of lane point sequences removes small-radius curves, reducing lateral acceleration, vibration, and EPS motor load.

Non-visible road paint lets autonomous vehicles mark irregular conditions ahead, improving event sharing and route decisions without distracting human drivers.

Before autonomous takeover, the controller checks visibility and wiper mode, switching only when driver sight can be maintained.

Route and speed-limit data are used to time roundabout exit acceleration, avoiding false exit detection and speed limit violations.

Captures accelerator pedal actuation and vehicle response to adapt automated longitudinal control for safer, smoother driver override.

Sensors and vehicle-state monitoring identify shoulder MRC zones so an autonomous vehicle can execute a safe minimal risk stop during abnormal conditions.

A planning lattice with probabilistic collision costs helps autonomous vehicles re-plan around dynamic obstacles with lower computation.

Warnings are timed from driver gaze and reaction time, prioritizing the primary collision object to cut distraction and missed hazards.

Uses towing vehicle sensor data to infer trailer presence, state, and blind spots for safer autonomous driving without extra hardware.

Real-time lateral acceleration lets a vehicle adjust curve-entry speed without relying on uncertain navigation data, improving comfort and control.

Lateral stability assessment helps adaptive cruise control avoid untimely acceleration or braking when a preceding vehicle turns or changes lanes.

Fleet telemetry clustering matches drivers to behavior profiles, enabling faster personalized driver assistance tuning with better comfort and safety.

Combined vehicle-state probabilities are tuned to context and driving style to predict maneuvers and trigger proactive driver-assistance actions.

Combining pre-braking and in-braking slope estimates improves hill brake pressure accuracy when sensors fluctuate and wheel-speed data is weak.

A hierarchical, road-rule-aware interaction model cuts traffic-state prediction complexity for real-time autonomous vehicle motion planning.

By switching lane-change mode based on adjacent-lane length, the controller helps complete branching-road merges before lane separation.

A centralized capability broadcaster updates motion limits from environmental conditions so AV planning modules stay consistent and efficient.

When opposite-area objects are detected, lateral return speed is reduced to suppress lane deviation with less driver anxiety.

Vehicle and environmental sensing identifies repeated unsafe or inefficient driving patterns and delivers corrective feedback through in-car or mobile interfaces.