Autonomous Decision Making Process TRIZ Cases

Segmenting the drive platform from specialized attachments reduces fleet size and maintenance costs while improving response times.

Segmenting detection into hierarchical levels improves measurement precision while managing device complexity in autonomous vehicle navigation.

A cooperative sensing system merges sensor data between principal and subordinate vehicles to enhance autonomy levels.

A multi-level alarm system segments warnings by risk severity to prompt timely human intervention when autonomous driving fails.

Remote computing system compares onboard and external signals to detect mismatches and generate restriction commands for autonomous vehicles.

A controller system monitors neural network outputs to trigger incremental re-training using new sensor data for autonomous machines.

A motor vehicle control device determines stopping needs using sensor data and traffic infrastructure signals to coordinate autonomous stops.

Wireless RF ecosystem replaces wiring harnesses to provide autonomous maneuver guidance without external personnel.

A universal control system generates simulated vehicle paths and orientations for user verification to produce approved commands.

Automated emergency light system evaluates vehicle speed and deceleration to trigger warning signals without driver input.

Onboard computing systems detect user identifiers from cellular networks to determine precise location data for autonomous vehicle routing.

Liquid crystal waveguide laser scanner steers beams to high priority areas, resolving low refresh rate limits in conventional LADAR systems.

Segmented composite poles embed directional antennas to maintain urban aesthetics and enable high-density 5G coverage without visual disruption.

A vehicle control system generates segment plans from prior manual operations to automate route traversal.

Drive assistance system reduces required driving ability by facilitating traffic environment comprehension when current skill is insufficient.

Configurable circuits detect sensor data defects and reprogram bitstreams to repair hardware logic without physical replacement.

Machine learning model adjusts vehicle parameters using passenger input data and vehicle logs to replace manual programming.

Central server coordinates autonomous vehicle fleet positioning to resolve safety efficiency trade-offs in unstructured environments.

A hierarchical vehicle control device architecture segments independent control units across logical levels to isolate failures and maintain system functionality.

A vehicle control system detects wheel slippage by comparing actual pose data against expected navigation paths.