Autonomous Decision Making Process TRIZ Cases

See how mobile fulfillment containers use real-time demand detection and autonomous routing to

See how a mobile UV lamp apparatus uses wheels, motors, and optical filters to enable automated

See how authenticating both the unmanned vehicle and article before opening carry-in ports prev

See how a grid-traversal method enables lawn mowing robots to autonomously detect and store obs

See how automated mobility and optical filtering enable a UV lamp apparatus to disinfect larger

See how a robotic device monitors chamber status, automatically removes full containers, and re

See how modular food dispensing modules enable rapid menu changes and on-demand production acro

See how escape preprocessing operations—direction change, speed adjustment, and position record

See how AI neural networks analyze user emotion and health data from mobile and kitchen devices

See how interchangeable food modules on a reconfigurable skid enable autonomous order fulfillme

See how a removable control box with battery, pump, filter, and heater enables a portable hand-

See how visual map input and direct region selection replace complex settings in robot vacuum c

See how a movable reflector assembly redirects visible light away from users while optimizing U

See how a robotic debris collector autonomously detects full chambers, discards them at fixed p

Centralized farm data and preloaded route packets let feed vehicles run autonomously through signal dead zones without exposing sensitive information.

NFC tagging sends robot cleaner state data to a mobile terminal, speeding error guidance, maintenance access, and firmware support.

Centralized farm data and preloaded navigation packets let livestock vehicles run reliably without continuous building connectivity.

A mapped room layout on a portable device lets users choose cleaning regions easily, avoiding complex settings and manual control.

Mobile cooling units target overheating datacenter components or boost air handlers to prevent heat damage during cooling failures.

Distributed hangars let drones launch locally, recharge autonomously, and rotate in service to maintain continuous coverage with faster response.

Adaptive AOI classification changes object update frequency by road context, cutting compute load, heat, and power use in autonomous driving.

Fleet logic predicts wait time at intermediate stops and assigns nearby trips or maintenance to keep autonomous vehicles productive.

GAN-generated adversarial examples expose weak spots in UAV object models, prompting targeted remeasurement for more reliable recognition.

Real-time sensor and traffic data predict adaptive vehicle speed limits to improve safety, traffic flow, and fuel efficiency.

Steering input switches a combine harvester from autonomous to manual travel without stopping, enabling trajectory correction and smoother field work.

Autonomous capability metrics let vehicles identify useful partners nearby and adjust driving behavior for safer, more efficient coordination.

Compares expected and actual trailer pose to detect tire, mechanical, or cargo anomalies and trigger corrective autonomous driving actions.

Switchable display areas let autonomous work vehicle operators hide nonessential travel-state information and improve terminal usability.

An ANN combines proximity sensing and parking-state camera images to classify approaching objects and store or send identification data.

Sensor-guided trailer positioning helps autonomous yard tractors dock in tight logistics spaces while following workflow rules and avoiding obstacles.

A combined real and virtual sensor model generates verifiable road and distance parameters for autonomous vehicle motion planning.

Vehicles refine ANN models locally on unexpected sensor inputs while a central server retrains shared models to improve recognition accuracy.

A controller matches low-SOC or stranded EVs with nearby vehicles or chargers to transfer power and ease range anxiety.

By learning the leader vehicle's driving pattern in advance, a follower can quickly rejoin group driving after road or traffic deviations.

Machine learning maps autonomous vehicle sensor data to the right SOP and checklist, helping human agents resolve onboard service incidents faster.

Telematics and historical vehicle-type scores enable more objective DAS evaluation and support operation adjustment across driving contexts.

Blind spots from trailer occlusion and bad weather are mitigated by lateral lane positioning and shared sensing from vehicles and roadside hubs.

Predicted reactions of nearby vehicles are used to score and refine candidate paths, improving collision avoidance in autonomous driving.

A backup vehicle controller detects EMI or cyber-attacks, assesses threats, and triggers mitigating actions to keep autonomous systems safe.

Battery-aware AGV control estimates charge at the destination and adds charging or vehicle switching to avoid overdischarge and battery replacement.

When an autonomous vehicle is blocked by an occluded view, remote guidance can trigger slow forward movement to reveal the scene safely.

Partial sensor, computing, and power redundancy preserves minimum field of view and fallback driving capability after component failures.

Distinct fallback sensor domains plus backup computing and power resources maintain minimum autonomous driving capability after failures.

Declarative scenario definitions generate and refine virtual test variations to improve coverage and validate autonomous machine behavior.

A two-range vehicle perception scheme acts on low-confidence detections early, then validates them later to avoid delayed turn responses.

Low-probability, high-severity agent behaviors trigger a switch to short-term planning, improving autonomous driving safety with less compute.

Sensor-equipped mobile robots detect walking hazards along planned routes and reroute or alert staff to reduce pedestrian injuries.

Partial redundancy across sensor, computing, and power domains preserves minimum field of view and safe vehicle operation after failures.

Short-exposure imaging plus pixel oscillation and color analysis helps autonomous vehicles distinguish blinking turn signals from glare.

A learned trajectory initializer guides constrained robot planning away from local optima while preserving hard safety constraints.

Differential drive plus caster-assist torque steers autonomous transport vehicles with less wedging, scrubbing, wear, and drive-wheel torque.

Hazard sensing ahead of the lead truck enables preemptive braking in the following vehicle, preserving close platoon gaps with lower collision risk.

Adjustable optical filtering and sensor quality checks help cruise control stay safe when direct sunlight degrades object detection.

IMU and GPS-based behavior recognition lets an autonomous golf cart stop and restart without remote input, improving convenience and safety.

Projected fitting and simulation optimize noisy repeated vehicle paths into smoother reference trajectories that adapt to confined-area conditions.

Driver-set speed preferences let an autonomous vehicle adapt to nearby traffic flow while respecting speed limits and improving safety.

Sensor-based convoy control keeps trucks at constant headway, improving fuel savings while handling close-following safety and emergency maneuvers.

Spline-fitted ground curves classify sensor returns in changing grades, separating objects from terrain with lower computational load.

A machine-learned yield model replaces hand-coded driving rules to improve adaptability, lower latency, and scale autonomous navigation.

Real-time sensor analytics adjust autonomous vehicle operations to handle weather, road changes, and risky traffic with lower response delay.

Path generation adapts turning radius to two-wheel or four-wheel travel mode, improving automatic work vehicle efficiency and turning capability.

Nearby vehicle reactions are predicted in a decision tree so the planner can choose higher-value autonomous driving paths with faster decisions.

Real-time sensor feedback and machine learning guide furrow following and adaptive turnaround turns in irregular fields with less crop damage.

A virtual doppelganger model narrows interaction scenarios so autonomous vehicles can predict road user reactions with less computation.

Maps field zones, plans material application by crop and restriction data, and records machine-applied coverage for compliance.

Driver readiness and environmental risk are assessed together to trigger safer shifts between autonomous and manual vehicle control.

Validated fleet road reports are merged into a filtered driving map, helping autonomous vehicles navigate with current, cross-checked condition data.

Vehicles share braking distance, autonomous status, and maneuverability data to improve spacing, lane changes, and intersection priority.

Fusing camera and communication data, this case predicts surrounding object motion to help autonomous vehicles avoid secondary collisions.

A motorized rotating sensor carrier with hose-fed cleaning maintains sensor coverage, visibility, and connectivity as AV conditions change.

Curvilinear path decomposition separates lateral and longitudinal planning, enabling convex optimization and iterative trajectory refinement for autonomous vehicles.

Warped camera views simulate an elevated perspective to detect road features with less map data and lower processing load for vehicle navigation.

A polyline vehicle contour in half-plane coordinates cuts false collision alerts and computation, helping autonomous vehicles pass tighter spaces.

Mobile recharging vehicles broadcast location and charge status, then schedule EV rendezvous charging to cut wait time and ease range anxiety.

Sensor-equipped mobile robots detect walking hazards along navigation paths and trigger rerouting or alerts to reduce pedestrian injury risk.

Cross-modal and temporal self-labeling lets autonomous vehicles adapt perception models in real time without slow manual annotation.

Requested driving force control balances engine braking and service braking to hold downhill coasting speed with less brake load and shift shock.

A mobile waste collector navigates to a disposal station to offload, clean, and charge automatically, reducing staff interruption in hospitals.

A machine-learned yield model replaces hand-coded traffic rules to improve autonomous vehicle yield decisions in complex road scenarios.

By extracting relevant regions from sparse imagery, this case cuts unnecessary CNN convolutions and speeds prediction for autonomous vehicle perception.

Behavior planning uses influence targets and sound or light propagation maps to guide robot movement with less impact on nearby people.

Roadside and onboard resources are allocated together to maintain CAV sensing and control while cutting power use and preserving driving range.

Excluding selected travel trajectories from clustering and sparse map updates improves autonomous route guidance when road markings are unclear.

A 3D mesh model separates vehicle self-returns from external objects, reducing false positives in autonomous driving decisions.

Real-time challenge scoring switches among reinforcement learning agents to match road conditions and the driver's preferred aggressiveness.

A double-tree graph planner cuts sampling overhead and memory use while generating intuitive collision-free vehicle paths in complex spaces.

By turning onboard mobility vehicles into cabins, this cruise layout saves ship space, cuts service cost, and keeps power and data connected.

Parallel vehicle commands are cross-checked, and memory is tested only on mismatches to block faulty control outputs in real time.

Integrated V2V, V2I, and V2P messaging helps autonomous and manual vehicles coordinate safely, avoid collisions, and improve traffic flow.

When lane markings or GNSS signals are weak, the vehicle shifts lanes or adjusts position to improve localization precision and navigation reliability.

Map data and object status are fused to rank likely trajectories, helping autonomous vehicles avoid predicted paths and collisions.

A vehicle computing system tracks time spent in a new country to trigger region-specific software updates for compliant, reliable operation.

Current and past target-object data are compared to detect degradation, then sensor field of view is adjusted to sustain detection confidence.

Random lateral shifts in autonomous vehicle trajectories spread wheel loads across the road surface while preserving route guidance.

Top-down multi-channel training improves cut-in prediction for perpendicular vehicles in unstructured roads while reducing false positives.

Diffusion maps and receding-horizon updates let mobile robots avoid moving obstacles and navigate safely without a prior map.

Interaction parameters rank surrounding agents so autonomous vehicles reserve high-fidelity trajectory processing for critical ones and react faster.

When a vehicle door opens during automatic parking, the system keeps the target parking position in standby for faster restart without re-entry.

Spatial envelope filtering narrows autonomous vehicle trajectory candidates using vehicle constraints, cutting planning time and compute load.

Sensor fusion maps doors, parking spots, and obstacles so vehicles can filter unsafe curbside stop points and improve passenger access.

Cross-checking camera, lidar, and radar locations lets the controller reject spoofed sensor data and avoid erratic automated vehicle behavior.

Correlation weight matrices compare sensed and map lane lines to filter false detections and improve autonomous vehicle localization.

Priority-based mediation merges sensor and vehicle data to cut redundant commands and speed emergency control response.

A 3D vehicle model flags lidar self-return points from body reflections, reducing phantom objects and preserving autonomous driving accuracy.

Automatic background vehicle generation from road network route points speeds large-scale driving simulation while keeping traffic changes visually smooth.

Precomputed fallback trajectories let autonomous vehicles switch plans at the right time to avoid collisions while limiting passenger discomfort.

Maps field zones, applies feature-based offsets and restrictions, and records machine application data for accurate compliance-ready use.

User position and traffic data are used to propose stop candidates, helping autonomous luggage delivery reduce vehicle waiting and user delay.

External garage climate data calibrates vehicle ultrasonic sensors during temperature transitions, improving parking distance accuracy.

A speed-clearance function lets autonomous vehicles slow down to pass close objects safely without changing path geometry or stopping unnecessarily.

External server checks on surroundings data and driving strategy help automated vehicles detect errors early and switch to an emergency strategy.

Wheel angle, misalignment, and eccentricity cues help autonomous vehicles anticipate close cut-ins and adjust control earlier.

When onboard sensors face ambiguous road threats, remote viewers add targeted context and driving recommendations to improve AI response safety.

Projecting candidate motion plans onto a nominal path rewards real progress, enabling tighter autonomous turns with better comfort and efficiency.

Image control shifts display form across movable vehicle panels so content stays visible despite door motion and changing viewing distance.

Direct wireless, visual, audible, and gestural hailing lets nearby autonomous vehicles respond faster while reducing false detections.

Calculating a safe reverse distance from sensor, odometry, actuator, and storage errors helps vehicles retrace paths without exceeding deviation limits.

Integrated waveguides and beam splitters replace bulky fiber coupling in coherent LiDAR, enabling scalable IQ detection with longer range and lower interference.

Predicting nearby vehicle reactions helps autonomous trajectory planning eliminate path conflicts with dynamic obstacles before execution.

Curvature-based sensor fusion updates lane-level driving paths and visibility data in real time, balancing path accuracy with sensor complexity.

Sensor data flags groups breaking the same traffic rule, helping autonomous vehicles identify processions and yield without disruptive maneuvers.

Attention-weighted LSTM and graph lane modeling improve obstacle intention prediction, helping autonomous vehicles avoid collisions.

Off-board cloud comparison of AV computations and sensor data helps detect malfunctions, miscalibration, and fleet-wide maintenance risks.

Neural lidar segmentation labels point clouds with learned distance features to improve object recognition accuracy in changing driving scenes.

Location-specific traffic rule profiles help autonomous vehicles apply relevant rules faster while improving context-aware decision compliance.

Uses LiDAR intensity gradients and edge pairs to detect painted road markings without neural networks or calibrated intensity values.

Satellite position guidance helps operators turn a traveling work machine and align with the next start position with less skill and fewer errors.

Short-exposure image sequences detect blinking turn signals by tracking pixel oscillation within vehicle regions despite glare and variable lighting.

A virtual doppelganger models likely road-user reactions so the AV can prune trajectory options and plan around dynamic interactions more efficiently.

Simulated vehicle operation data trains autonomous earth-moving control to handle obstacles, cut hardware complexity, and improve coordination.

A state-machine controller checks airspeed, attitude, and actuator health, then blends VTOL and horizontal-flight laws for smoother transitions.

A detachable 360° LiDAR, fisheye camera, and radar module enables autonomous navigation without wireless infrastructure through onboard AI mapping.

Broadcast control signals let autonomous vehicles coordinate pull-over and lane-clearing maneuvers in emergency and roadwork traffic.

A computing device uses magnetic halting to align an aircraft rotor on its drag minimization axis, cutting air resistance during edgewise flight.

Boundary segments are preclassified by type so an autonomous off-road vehicle can change speed or position limits near each edge condition.

Waypoint optimization under runway constraints automates aircraft landing patterns to cut fuel use, shorten landing distance, and improve airport capacity.

Image and depth sensing let an inspection UAV locate the target object and generate collision-avoiding flight paths without manual overflight.

An onboard controller uses a single-waypoint path and autopilot to dock a watercraft without dock-mounted sensors or manual steering.

Double-row RFID tags define ingress and egress zones so industrial vehicles can detect travel direction and apply speed or height control accurately.

Dividing non-convex areas into editable cells helps users generate feasible coverage paths faster while reducing repeated traversals and soil compaction.

Detected target locations are stored as history so the vehicle can adapt its route and revisit critical monitoring spots more effectively.

Motorized wheels, retractable legs, and sensors let the cart fold, match vehicle height, and load groceries with less strain.

Route-based roadside units validate automated vehicle data latency before trips, helping detect transmission risks and connectivity gaps.

Autonomous UAV and sensor data collection enables remote damage assessment, obstacle-aware inspection, and faster insurance claim initiation.

Real-time sensor limits let the flight controller modify pilot inputs and flight plans to prevent unsafe maneuvers and aircraft damage.

Real-time vehicle status, task locations, and user proximity are combined to automate fleet service scheduling and route execution.

By storing the pre-avoidance mode and checking post-maneuver viability, the flight control system restores autonomous operation with less pilot workload.

Remote scheduling commands are stored through the cloud and dock, then passed to the robot when available, avoiding range and status limits.

Non-contact light pulse scanning lets a material transfer vehicle hold spacing and speed to a paving machine without paver-mounted sensors.

Hierarchical fusion of aerial, ground, and stationary sensor data enables real-time path adjustment for unmanned vehicles in unknown environments.

Pre-stored end-of-use positions and times let autonomous mobility services match riders to available vehicles faster and raise occupancy.

Machine vision and a neural network align striping hardware to worn roadway marks, cutting manual labor and improving marking accuracy.

Over-parameterized component tensors are merged into a compact neural network to cut embedded inference latency while preserving prediction accuracy.

Varying polygon-scanner facet angles and modulation improve long-range range and velocity detection while filtering interference.

A network node adapts UAV position reporting to collision risk, improving avoidance while limiting energy use and signaling overhead.

Matches autonomous working machine models to user time, work region, and cost needs using machine performance data for more accurate recommendations.

On-the-fly calibration uses assisting vehicles or landmarks to keep moving trucks and trailers accurately sensed without stopping.

An autonomous mobile response unit uses path planning and obstacle detection to deliver patients or equipment to hard-to-reach incident scenes.

Sensor and sub-system data automate damage classification, party alerts, and blockchain repair records to speed autonomous vehicle recertification.

Selectable earthmoving styles let a work vehicle adapt digging and filling to site conditions while reducing operator fatigue and cost.

Rolling-horizon scheduling inserts new port transport tasks into wAGV routes and uses tabu search to cut delay and waiting time.

Preflight battery selection uses charge, weight, and destination data to avoid oversized UAV batteries while ensuring enough power to reach the target.

Distributed acoustic sensing and machine learning classify construction vibrations along buried fiber optic cables to trigger early damage alerts.

On-the-fly calibration uses an assisting vehicle or landmarks to keep sensor alignment accurate during motion and support safe autonomous control.

When signal transmission turns abnormal, the UAV reroutes to an accessible landing location using real-time signal and environmental assessment.

Embedding neural networks compress autonomous vehicle sensor samples, enabling faster similarity search across large repositories with lower latency.

Tone-matching actual images to CG characteristics improves target recognition accuracy when training data relies on CG scenes.

Networked transport and lift units automate cargo transfer to aircraft, reducing operator error while improving loading precision and safety.

Event-triggered transfer from volatile to non-volatile vehicle memory preserves crash data for fault analysis and remote software updates.

Predicted tool delay lets a tractor start dispensing before the target point, improving placement accuracy while cutting product loss, time, and fuel use.

Wide-area context is converted into regional feature vectors, improving vehicle object classification without adding prediction time or resource load.

Combining georeferenced vegetative index data with in-field sensor data enables real-time yield prediction and machine control during harvest.

A powered open ramp and autonomous navigation let an unmanned lifeboat reach survivors precisely and recover them without onboard crew.

Hinged retention members adapt to varying headliner thicknesses, secure the cabin camera, and allow tool-free maintenance removal.

LiDAR terrain mapping combined with GPS and mower performance data enables more efficient mowing paths and remote fleet oversight.

Periodic evaluation data packets test vehicle subsystems and response times to detect communication faults before delays affect autonomous operation.

Camera-based person classification lets a mobile robot switch speed and movement range to balance staff efficiency with non-staff safety.

Infrastructure sensors detect path obstructions and trigger object moves or rerouting so mobile robots reach destinations with less travel time.

GNSS and geomagnetic guidance help a ball collecting robot reach variable discharge sites without falling into grooves or delaying collection.

Individually controlled crawler motors and adjustable camera mounts enable single-pass, multi-angle pipeline inspection with less time and radiation risk.

Additional vehicles with removable sensor pods synchronize with autonomous cars to generate labeled training data faster and more accurately.

Live video feedback and wireless control let a spherical self-propelled robot navigate toward selected targets while carrying varied payloads.

Interacting Gaussian processes model each agent to navigate crowded spaces while preserving intent, flexibility, and convex optimization.

Distance is measured by comparing reflected and transmitted polarization states, avoiding direct timing limits in high-precision ranging.

Machine learning and adaptive signal settings help LIDAR reject particulate false returns while preserving true object detection.

A hierarchical intelligence model combines sensor data, vision, and self-monitoring to enable reliable Level 4-5 autonomous operation.

Distributed leader-follower robots use local sensing and low-latency communication to maintain transport formations around obstacles.

When autonomous vehicles overtake and switch sequence, travel plans are updated to keep the new order and reduce unstable passing.

Active roadside devices and vehicle discovery signals deliver real-time road data when maps, cameras, and LiDAR struggle in snow, fog, or low light.

Raw autonomous vehicle data is clustered into personality identifiers, improving vehicle differentiation and search relevance.

When obstacles slow a planned route, the vehicle compares segment traversal times and shifts to a faster alternate path while preserving comfort.

Leader-follower control cuts manpower cost in self-driving fleets by centralizing decisions, simplifying follower vehicles, and detecting link failures.

Normalized relative-motion frames cut 3D trajectory processing load while improving multi-object pattern classification and response.

Image-based grass height and density sensing adjusts mower height automatically to cut load, improve mowing smoothness, and prevent overload.

Energy-based task assignment switches between interruptible and hard refueling to preserve autonomous vehicle service availability.

Offline AV testing switches from log replay to play-forward simulation, preserving real perception data while evaluating divergent control outputs.

Skipping occupied map cells during obstacle inflation cuts navigation-map latency while preserving collision-aware path planning for autonomous mobile devices.

Precomputed collision data and planning graphs speed autonomous vehicle path planning while handling uncertain dynamic obstacles in real time.

Nested virtual zones let a processor slow or block mining machine commands at geofence boundaries to prevent entry into restricted areas.

A boundary wire and charging loop guide the mower through crossing detection and fixed moves for reliable charging contact alignment.

A loop-crossing docking path guides the robotic mower into precise charging contact alignment with fewer wires and less docking complexity.

3D tiling and fixed-point Q-format compress autonomous vehicle point clouds while allowing math on compressed data without decompression.

Vehicle path, position, and speed define regions of interest so only relevant sensor data is captured and transmitted, cutting compute load.

Sensor-guided flight control identifies navigation status and applies automatic aircraft adjustments when pilots cannot maintain eVTOL control.

A vehicle-mounted UAV uses telepresence, obstacle avoidance, and a robotic arm to handle traffic stop tasks without exposing officers to roadside threats.

Idle and low-load autonomous vehicles pool onboard computing to train and test ML models on live sensor data for unusual driving scenarios.

A trained ML model assigns high or low resolution to sensor regions, cutting processing time and memory while preserving detection accuracy.

Deep reinforcement learning replaces hard-to-tune PID control, improving UAV precision and robustness in nonlinear flight conditions.

Slope-aware path planning aligns autonomous construction vehicle travel with steep terrain to avoid side slopes, sliding, and tip-over.

A road controller coordinates platoon speed and size at single-lane intersections to prevent bottlenecks and improve deterministic traffic flow.

Stored LIDAR scans and prior pose data let a suspended autonomous vehicle detect movement on restart and recover localization faster.

A wheeled robot repositions a signal booster to optimal indoor locations, balancing reliable coverage with autonomous navigation and battery use.

Autonomous bird stimulation, floor aeration, and egg collection reduce floor laying, contamination, and farmer workload in poultry houses.

A joining vehicle computes a rendezvous point, verifies the lead vehicle, and synchronizes speed and spacing for safer traffic flow.