Autonomous Decision Making Process TRIZ Cases

An adaptive control system generates expected inputs from a driver model to compute variance and blend automated steering with actual driver commands.

Autonomous vehicle system uses LTL evaluation to trigger multi-modal feedback, resolving the contradiction between automation efficiency and passenger trust.

A cloud-based system manages traffic policies for autonomous vehicles using GPS location data to determine applicable rule sets.

Driving assistance apparatus identifies following vehicle driver state and controls autonomous path to avoid collisions with dangerously running vehicles.

Waypoint database links destination types to preset vehicle actions, enabling automated hazard light management at intermediate stops.

Wireless broadcasting exchanges position and velocity data between vehicles to detect incidents beyond direct line-of-sight.

A saliency estimation system trains AI models to mimic expert driver attention patterns using ground truth databases and LSTM layers.

Training neural networks in a reduced parameter subspace reduces storage space and transmission bandwidth while maintaining model accuracy.

An asynchronous clock-less data co-processor segments Dijkstra's algorithm across a mesh array to reduce power consumption and system weight.

Automated driving system compares detectable sensor range against required detection distance to verify operational design domain status.

A vehicle adjusts remote operator event sensitivity based on wireless network performance to maintain connectivity.

A motor vehicle system detects approaching priority traffic and calculates safe path deviations to clear the roadway.

A sensor-fusion network coordinates data across multiple vehicles to validate individual sensor readings through cross-checking.

Autonomous mobility vehicles use onboard sensors and kiosks to eliminate waiting times while navigating pedestrian zones.

Machine learning algorithms analyze human driving data to create optimal style profiles for autonomous vehicles.

A profile generator creates condensed environmental data structures for autonomous vehicles to reduce processing demands.

A neural network classifies vehicle sensor data using a configurable cost matrix to prioritize safety-critical decisions.

Electronic control unit extends steering duration based on real-time lane position data to suppress post-obstacle deviation and ensure safe driver handover.

Detecting emergency events enables computing systems to adjust autonomous vehicle positions and speeds, resolving conflicts with emergency vehicle routes.

A system detects vehicle positions to transfer parcels between moving units.

An automated driving system actively changes parameters to search for a driver's acceptable range.

A vehicle traveling assistance device adjusts brake activation timing to align with driver perception during automatic driving.

A Virtual Chauffeur Agent uses task planning AI to handle unforeseen ride situations, reducing system complexity while improving service quality.

A dual neural network controller system detects incorrect outputs and triggers incremental re-training to maintain operational reliability.

Computing devices determine optimal shear force and reposition pins via actuators before impact to minimize collision damage.

Dynamic spatial separation adjusts lane buffers between autonomous and manual vehicles to resolve roadway capacity versus safety trade-offs.

Autonomous aerial vehicle control system issues predefined commands when data link loss occurs, resolving reliability complexity trade-offs.

A path-time analytical curve generation system selects polynomial functions to control autonomous vehicle trajectories during emergency stops.

A steering system for autonomous vehicles uses alternate control mechanisms to enable user interaction without a physical steering wheel.

Dynamic lamp movement overcomes static irradiation limits, expanding effective treatment area and improving propagation efficiency.

A capacitor buffers wireless power to stabilize AGV battery voltage, preventing depletion during short charging stops.

A processor-implemented path planning method constructs road models to predict vehicle sprung-mass dynamics and select alternative trajectories.

A distributed knowledge base system integrates sensor data to control vehicle operations and navigation.

A vehicle controller calculates a final determination value by integrating sensor data with external inputs from surrounding vehicles and servers.

A vehicle control system estimates upper friction torque to set a variable determination threshold for driver hands-on state detection.

A driving state switching portion compares manual and autonomous operation amounts to prevent unsuitable mode transitions.

Autonomous transport vehicles dynamically adjust routes to avoid obstacles, resolving navigation precision versus adaptability trade-offs.

Eye tracking sensor verifies driver awareness before transferring control, resolving safety risks from premature handover in complex driving scenarios.

Segmented flight routes with vehicle-mounted docking stations extend operational range while reducing collision risks near populated areas.

Autonomous vehicle control system uses episodic memory recall to generate situation aware events from sensor data.

Columnar antennas and emergency buttons flank the elevation mechanism, deterring foreign objects from entering the hazardous zone.

Controller uses sensors to compute optimal gap, reducing re-alignment time and operator workload during slot-dozing.

Centralized server analysis of onboard event logs detects security threats, reducing computational burden on vehicles while ensuring fleet-wide protection.

A steering control system calculates rotation angles from high precision map curvature data to manage autonomous vehicle turning paths.

A vehicle notification device adjusts warning output direction based on predicted pedestrian movement patterns.

Electromechanical switches configure auto-guidance paths, eliminating complex graphical interfaces and reducing system costs for agricultural vehicles.

Situational complexity quantification adapts autonomous vehicle control schemes using sensor data.

A control device calculates predicted user arrival times to manage vehicle movement start timing.

A navigational system calculates relative velocities to identify surrounding vehicles of interest and define current navigable spaces for autonomous driving.