Autonomous Decision Making Process TRIZ Cases

Fusing state data via a time-position diagram resolves sensor degradation by maintaining localization availability when onboard sensors fail.

A driving style evaluation system determines preferred driver maneuvers from manual operation data to personalize autonomous control.

A vehicle controller adjusts driving force transfer between automated and manual modes using pedal input signals.

Iterative leg matching identifies equivalent points for segment insertion, reducing unnecessary overflights and fuel consumption during tactical operations.

A vehicle control system determines travel priorities based on load and size conditions to manage AGV movement along a shared guide path.

Control unit sets offset modes by traveling scene to increase distance from adjacent vehicles, resolving fixed relative speed limitations.

A method determines autonomous driving quality from monitored parameters and associates it with manual driving quantities to calculate accident risk values.

Autonomous vehicle system detects external signals and sends response signals to verify interactions, resolving signal interpretation accuracy issues.

An autonomous travel carriage adjusts speed and elapsed time to reproduce planned routes with subgoal points.

A vehicle seat control apparatus limits reclining angles and rearward positions using dynamic actuators.

A remote system creates a simulated environment with virtual support elements to navigate vehicles through hazards outside their operational design domain.

Segmenting sensor data into event-related and non-event portions reduces storage requirements and network traffic while maintaining critical data availability.

Autonomous vehicle control system selects driving modes based on sensor data to resolve energy efficiency and travel time trade-offs.

Following vehicle sensors detect radial hazards to trigger preemptive braking, resolving collision risks caused by delayed lead vehicle response times.

Timing controller adjusts vehicle departure based on passenger activity to eliminate waiting delays at pick-up locations.

A navigation device computes obstacle probability from two-dimensional depth maps for real-time path planning.

A controller estimates vehicle position using a dynamics model and PID correction for steering control.

A perception calibration system corrects obstacle detection using vertical acceleration data from vehicle sensors.

A machine learning algorithm identifies shadow regions in captured images to generate textured 3D maps without visual artifacts.

A mobile app interface uses segmented pushbuttons and input pads to initiate autonomous vehicle parking maneuvers.

Autonomous delivery robots use machine learning to control exterior lighting and displays.

Proactive driving maneuvers generate interaction data for rare scenarios, enabling comprehensive vehicle profiles that improve behavior prediction accuracy.

An autonomous vehicle control system selects operational plans using situational awareness data and mission control inputs.

Segmented architecture isolates real-time computation from general-purpose processors to resolve stability and speed trade-offs.

Automated vehicle positioning adjusts queue spacing to resolve congestion bottlenecks while preventing safety hazards from blocked access.

Autonomous driving system adjusts vehicle behavior control parameters based on driver awareness levels.

A control system adjusts host vehicle lateral position relative to lane markers based on driver visual condition and remote vehicle location.

Classifies driving maneuvers as goal-oriented or stimulus-driven actions to build naturalistic datasets for autonomous vehicle control.

Dynamic control parameters adapt to key curvature radii, eliminating route deviations during directional changes in differential drive vehicles.

A vehicle control system compares driving scenario data against operational capabilities to determine autonomous mode suitability.

A tracking subsystem detects surrounding vehicle compliance status to plan appropriate collision avoidance maneuvers.

A navigation system aligns dynamic search spaces to vehicle uncertainty using range-only radar altimeter measurements.

Generating an alternate anchor line via lane quality indicators and map validation ensures reliable vehicle guidance when primary detection fails.

A remote driver coordinates with road users to resolve complex traffic situations.

An autonomous vehicle system detects hazardous oncoming objects and determines an altered travel route to avoid collisions.

A vehicle uses separate display circuits to present automated driving data and infotainment content independently.

Cloud server transmits activity profiles to vehicle electronic control units via a CAN bus network.

Dynamic path planning adjusts vehicle trajectories to bypass congested pocket lanes, resolving safety risks against route efficiency trade-offs.

Segmenting trajectory and behavior models reduces computational complexity while enhancing prediction accuracy for dynamic driving scenarios.

A safety-aware comparator analyzes projected travel paths from redundant subsystems to detect mismatches and resolve conflicts.

A speed following system calculates three distinct torque forces to control autonomous vehicle throttle and brake actuators.

Autonomous vehicles reduce energy waste by switching to lower power modes after parking.

A machine-learned model on autonomous vehicles determines vehicle actions from proximate object features.

Ultraviolet light irradiates a hygienic film on a vehicle door handle to reduce bacteria spread without manual cleaning labor.

A V2V communication apparatus detects surrounding vehicle autonomous modes and displays the status on side mirrors or head-up displays.

A vehicle control system uses monitor devices to generate virtual bird's-eye view images for user-designated parking area selection.

A vehicle control apparatus adjusts front and rear wheel driving force distribution during mode switching.