Autonomous Decision Making Process TRIZ Cases

Control rights shift to the recipient at delivery so an autonomous vehicle can be repositioned around fences, guardrails, or slopes for package access.

Server-built behavior models use object class, actions, and surroundings data to improve autonomous vehicle prediction, safety, and navigation.

Mutual CAN monitoring lets two brake controllers hand over brake module control, maintaining reliable autonomous braking during faults.

A removable docking layout links a road vehicle and flight vehicle to enable VTOL takeoff, road use, and practical mode switching.

When sensors detect obstacles or machine health issues, supervisor-guided exception handling helps autonomous work machines pause or continue safely.

Occupant detection lets parking control switch between smoother in-cabin maneuvers and faster remote parking with tighter route curvature.

Remote vehicles request and receive extra ego-vehicle identifiers in V2X messages to improve transmitter matching for ADAS.

Point classification and clustering from LIDAR data improve object detection speed and property estimation accuracy for autonomous vehicles.

Remote assistance, roadgraphs, and live imagery help autonomous cargo trucks switch modes and park safely at destination facilities.

A heat-map grid checks predicted road-user paths in real time, helping autonomous vehicles handle unpredictable motion more accurately.

Multi-sensor road scanning and real-time feedback help a mobile robot detect hazardous vehicles and trigger braking or acceleration while crossing.

When primary planning fails, a reduced-capability controller generates road-aware fallback trajectories to avoid unsafe hard braking.

Cross-vehicle telemetry compares driving metrics to predict renter performance in unfamiliar vehicles and support safer access decisions.

Seat motion and driver-readiness checks delay manual takeover until the seat returns from autonomous mode to a safe driving position.

When confidence falls below a threshold, the vehicle sends sensor data to a remote assistor and switches to a guided autonomous mode.

High-resolution cameras extend object detection beyond LiDAR range, then fuse with radar and LiDAR for earlier tracking and lane changes.

Periodic multi-sensor checks verify driver attention focus, fatigue, and distraction to support safer driving and autonomous handovers.

When battery charge drops too low, nearby home or vehicle charging hosts are ranked and scheduled to restore EV range without fixed stations.

Mode-based event acceptance lets a mobile object evaluate sidewalk trajectories for safer, more comfortable, or more efficient travel.

A roof-integrated viewing area with fluid cleaning keeps autonomous vehicle sensors clear of dirt, snow, water, and ice.

Lossy onboard compression cuts autonomous vehicle sensor storage and offloading downtime while keeping decompressed data usable for autonomy models.

A unified heatmap uncertainty model replaces per-object updates, cutting collision-check computation for safer drivable area decisions.

A modular control stack unifies vehicle interfaces, telematics, perception, and cloud sync to port autonomous farm functions across mixed equipment.

Importance scoring ranks nearby agents so autonomous vehicles can focus prediction resources on high-impact traffic and keep planning timely.

Ranks road entities by collision risk using onboard and external sensor data, helping autonomous vehicles allocate avoidance resources more effectively.

Axle sensor data reveals real-time load imbalance, allowing autonomous vehicles to adjust maneuvers for safer handling and better fuel use.

Trajectory control adapts to lead or following position in multi-lane turns, reducing cut-off risk and abrupt evasive maneuvers.

Multiple situation detection elements model sensor data in parallel to cut computing effort while keeping automated vehicle control robust in real time.

By comparing current and past sensor data, the vehicle adjusts field-of-view volume to sustain object detection in changing conditions.

Low-confidence sensor detections are sent to a secondary processor or human for confirmation, improving autonomous vehicle safety and decision accuracy.

Road structure and motion models guide destination-state generation to improve long-horizon moving target track prediction accuracy.

Natural language commands are translated into absolute vehicle paths using sensor-based object disambiguation for accurate navigation in continuous environments.

Preplanned package handoff at centralized robotic locations lets autonomous vehicles deliver securely with minimal route deviation and added task load.

Two-stage user authentication verifies identity outside and inside the cabin before departure, preventing unintended autonomous vehicle starts.

Context-aware pullovers let an autonomous vehicle enter a small curb space first, then reverse before or after boarding to improve pickup safety.

A trajectory manager validates AI and fallback paths, then gates return from safety stop until monitor or operator release.

A server-generated vehicle VM predicts resource shortages and offloads computation to keep autonomous driving and passenger applications stable.

Validated road event reports from fleet vehicles are merged into a filtered driving map, improving obstacle awareness and route planning.

Duplex monitoring combines onboard obstacle detection with remote camera review to stop and safely restart autonomous vehicles despite sensor limits.

A redundant fallback controller generates road-type-aware trajectories from available sensors, helping autonomous vehicles avoid unsafe lane stops during failures.

Computing devices assess local failures, choose a safe pullover spot for transfer, and request a second vehicle to minimize trip interruption.

Fault-isolated safety power rails keep steering, braking, and sensors alive long enough for an autonomous vehicle to complete a safe roadside stop.