Autonomous Decision Making Process TRIZ Cases

A machine learning model predicts occupancy probabilities in occluded regions to evaluate candidate trajectories.

A parking assist apparatus adjusts the target parking position based on detected occupant location differences between learning and operating modes.

Electronic control units manage sensor cleaning while maintaining autonomous driving operations.

Deep reinforcement learning trains neural networks via simulated trial-and-error to navigate diverse parking scenarios while preventing collisions.

A vehicle controller switches between open-space and on-lane modes to maneuver parked vehicles toward driving lanes.

Screen photoelectric elements convert image light into electric power, extending flight time beyond battery capacity limits.

Magnetic coupling eliminates gear backlash in a two-axis sensor platform, maintaining precise positioning for autonomous vehicle sensing.

Driver monitoring enables mode switching in complex environments, reducing cancellation risks.

A collision cost calculation system selects the least damaging trajectory for an autonomous vehicle.

Electronic control unit generates two traveling plans using different data scopes to select the most reliable option for vehicle motion.

A vehicle diagnostic system determines navigation instructions based on identified risk conditions and operating states.

A mobile robot uses a projector to emit encoded light signals for precise indoor navigation.

Motorized seats pivot and fold to resolve space constraints while maintaining safe vehicle operation.

A driver state recognition module detects cognitive states and shares data via DSRC to nearby vehicles.

A prediction processing unit generates corrected ego vehicle data and predicted object data using sensor inputs.

Dynamic priority arbitration resolves simultaneous entry conflicts by assigning waiting times based on vehicle position and speed to prevent collisions.

A sensor synchronization system generates time-synchronized pulses to format data with precise timestamps.

Controller transfers data to nonvolatile storage before module damage, preventing loss during accidents.

An onboard computing system detects vehicle portion movements using targets to adjust sensor calibration parameters in real time.

A sensor distribution hub feeds two independent micro-processing units to ensure continuous vehicle control.

A dynamic trigger system routes specific sensor data to passengers when an autonomous vehicle detects a potentially dangerous event.

An autonomous vehicle control unit combines driver intent and motion prediction outputs via a function approximator to resolve partial information trade-offs.

A perception system merges onboard and edge sensor data into a unified output for autonomous vehicle action determination.

A rotating annular member with liquid nozzles cleans sensor windows, replacing multiple stationary units to reduce complexity and fluid consumption.

An external guidance vehicle deploys from the main UAV to capture obstacle data, resolving blocked camera fields of view during delivery.

Autonomous vehicle sensor data annotation identifies inconsistent traffic information to detect spoofing attempts.

Controller device uses leading vehicle auxiliary data to maintain autonomous driving mode when sensor reliability deteriorates in fog.

Autonomous transport vehicle resolves speed stability trade-off by locking caster wheels during high-speed movement to maintain balance.

A curvature-corrected path sampling system generates sample points using a decay ratio to create stable trajectories.

Automated delivery system eliminates human operator costs by using self-service compartment locking and electronic authentication for proof of delivery.

A vehicle control system detects operators and applies mode-specific profiles to adjust settings automatically.

Fuses acoustic and magnetic sensor data to determine the operating environment for autonomous maritime vessels.

A moving object controller selects execution plans based on estimated external sensor detection accuracy.

Computing device generates vehicle and object trajectories to determine collision risk.

Dynamic sensor re-weighting resolves occlusion and lighting contradictions by prioritizing informative visual data for reliable gaze detection.

A remote operator inputs waypoints for stuck autonomous vehicles, which validate the path against safety protocols before execution.

A vehicle control system calculates trajectory errors to determine state of health metrics.

A vehicle control system adjusts automatic transmission oil feeding amounts based on manual or autonomous driving modes to manage lubrication.

A trajectory planning system uses clothoid curves to generate optimized vehicle paths for autonomous driving.

A computing device transmits real-time automation level availability to vehicle drivers via a user interface.

An onboard illuminance sensor monitors ambient light intensity to prevent a drone from entering dark zones where operators lose visual tracking capability.

Replacing mechanical linkages with a digital x-by-wire platform, the system scales computation power via node segmentation to resolve signal limits.

Reflect backward waypoints to create a virtual forward path, resolving phase contribution complexity in automated vehicle maneuvering.

Mobile devices display virtual surroundings to validate parking spaces, resolving sensor detection inaccuracies in remote park assist systems.

An automated vehicle system determines driving and prohibited areas to navigate bottlenecks by using restricted zones for safe passage.

Heterogeneous RF signal separation estimates object location and velocity using phase and amplitude data from multiple antennas.

A mobile robot predicts pedestrian and vehicle movement intent using sensor data analysis and computer vision tracking algorithms.