Autonomous Decision Making Process TRIZ Cases

Biometric sensors calculate passenger satisfaction indices to adjust autonomous vehicle driving behavior in real time.

A roof-mounted fluid management system intercepts leaked coolant with a catch basin, routing it via drain tubes to prevent cabin contamination.

Segmented autonomous driving units exchange operational data to detect failures and trigger rapid deceleration, resolving single-point reliability risks.

Multiple receiver antennae measure RF signal arrival times to calculate precise indoor positions, overcoming GPS inaccuracy and reducing power consumption.

Optical fiber panels illuminate vehicle perimeters to display operational status through color and pattern variations.

A traveling control system moves the vehicle laterally away from adjacent traffic to increase separation distance.

A gateway module bridges vehicle bus and autonomous driving systems over distinct connections.

Server verifies vehicle location via geofence and requests electronic paperwork.

Segmenting routes into path segments allows the system to retrieve specific human driving behaviors, reducing passenger discomfort from unsmooth acceleration.

A vehicle control system adjusts acceleration and suspension stiffness based on occupant feedback to tailor ride quality.

A movement route generating apparatus plots travel trajectory curvature against distance to establish a path with minimal horizontal angular velocity.

A notification control apparatus generates sound image patterns based on future travel conditions to inform passengers of upcoming vehicle motion changes.

A computing device selects unmanned vehicles for tasks by comparing dynamic and static attributes.

Deploying a remote virtual electronic control unit eliminates physical hardware redundancy while maintaining fault tolerance during local system failures.

Autonomous vehicle system detects emergency vehicles via sensor emissions and adjusts navigation paths to prevent obstruction during transit.

Hardware-accelerated radial basis function calculation resolves the trade-off between modeling accuracy and real-time processing speed in engine control units.

Controller determines integrated sensor health via weighted correlation analysis to resolve reliability complexity trade-offs.

Remote server processing handles facial recognition for robotic deliveries, resolving security complexity trade-offs.

Deep neural networks process multi-sensor data to predict trajectories, resolving the contradiction between prediction accuracy and system complexity.

Assesses occupant injury probability via seat belt and posture sensors, then modifies speed and route parameters to prevent accidents.

Occupancy grid map adapts cell resolution to vehicle speed, reducing computational burden while maintaining environmental representation accuracy.

System compares identification aspects via remote operator to resolve automation versus manual authorization contradiction.

A distributed knowledge base system integrates fixed and learned data to control vehicle operations.

Continuous glucose monitoring devices measure driver glycemic levels to predict hypoglycemia risk, enabling timely interventions that mitigate accident hazards.

A real-time mission re-planning system detects incapacitated unmanned air vehicles and assigns tasks to remaining units.

Segmented training data structures enable dynamic model selection, resolving catastrophic memory loss while maintaining accuracy in varying settings.

A dual buffer zone system assigns distinct spatial constraints to obstacles based on prior planning states to maintain consistent vehicle trajectories.

An autonomous moving machine estimates self-location and calculates reliability to trigger recovery actions.

Integrating electro-dermal, physiological, and camera sensors reduces false drowsiness detection by cross-validating signals through a central controller.

Reconstructs obstacle trajectories from sensor history to infer lane configurations in rural areas with unclear markings.

A LIDAR processing system filters noisy remission signals to identify lane markers.

A lidar conflict resolver generates and scores multiple segmentation scenes to select the most accurate representation of nearby objects.

A shared vehicle system automatically adjusts seat and steering positions using biometric sensors to match user profiles.

A vehicle lamp controller adjusts visible light irradiation based on infrared sensing data.

A master-slave driver assistance interface decouples user input from vehicle actuation to enable intuitive maneuver selection.

A prediction system segments obstacle movement into longitudinal and lateral trajectories optimized by polynomial functions for accurate path planning.

A structural recurrent neural network analyzes pose information to generate prediction indicators for nearby vehicle movements.

Driving assistance device adjusts learned driver characteristics based on detected area driving patterns.

Sub-sampling video frames and classification data for remote analysis enables immediate autonomous driving system adjustments.

A trip optimization tool generates routes incorporating fuel and rest stops to improve planning accuracy.

Smart Gains model applies Gaussian process learning to resolve computational complexity and black-box issues in autonomous vehicle control.

Adaptive cruise control adjusts speed commands via selected acceleration profiles during lane changes.

A server system compares projected and actual power consumption to identify degrading components in autonomous vehicles.

Disambiguating system resolves ambiguous radar detections by selecting true targets via proximity to predicted positions, improving navigation accuracy.

A state machine controls autonomous vehicle navigation through junctions using sensor data to determine transition speeds and caution regions.

Following vehicles execute independent avoidance maneuvers based on leading vehicle predictions, resolving shared collision risks in platooning groups.

Extracting road features from map images predicts traffic participant behaviors, reducing reliance on costly high-definition maps.

Elevated sensor arrays capture environmental data and transmit it to vehicles, reducing onboard hardware complexity.