Autonomous Decision Making Process TRIZ Cases

Crosswalk segments and pedestrian speed hypotheses let autonomous vehicles anticipate occluded walkers and avoid abrupt braking.

Wireless messages from roadside infrastructure or nearby vehicles guide braking and steering when traffic signals or hazards are obscured.

Adjustable discomfort thresholds help autonomous vehicles find speed profiles that balance passenger comfort, road-user interaction, and safety margins.

A top-mounted camera ring combines 360° coverage with vibration damping, thermal regulation, and EMI protection for reliable autonomous sensing.

Dynamic switching of sensor range, direction, and processing parameters helps autonomous vehicles focus on regions of interest and use resources efficiently.

Mode-specific collision response thresholds let autonomous vehicles adapt warnings and interventions to cut false positives and missed risks.

Pedestrian terminal approval lets the vehicle confirm action schedule understanding and suppress unsafe operation when no acknowledgment is received.

Confidence-weighted predictive control balances safety and comfort by tuning vehicle trajectory tracking from probabilistic motion plans.

Real-time camera, RADAR, and LIDAR inputs guide lane changes and turns without HD maps, reducing compute load and expanding operating range.

Pulse-width comparison and time-spaced emissions let LIDAR detect surface normals while reducing cross-talk and point-cloud ambiguity.

Serial control authorization lets nearby vehicles reposition and open a safe path for autonomous movement in dense parking lots.

Human response data from road-scene images and video trains models to predict pedestrian and cyclist actions beyond motion vectors.

Lateral acceleration and yaw-rate signals are used to estimate mass, center of gravity, and inertia changes in real time for steering and stability control.

Retrofitted sensors, processors, and actuators enable logistics GSE to optimize paths, avoid collisions, and improve maintenance monitoring.

Dynamic task allocation shifts noncritical autonomous driving computations to the cloud to cut onboard energy use without delaying real-time control.

Partitioned decision tables let autonomous vehicle speed planning adapt locally in real time without retraining the full model for edge cases.

Polynomial steering profiles are simulated and adjusted to keep self-driving car steering within physical limits while reaching target states.

Task-by-task switching between autonomous and tele-operated control cuts latency and errors while preserving reliability in unstructured environments.

Hidden steering and pedal controls rotate between manual and autonomous modes to reduce cabin distraction while preserving driver access.

Probability-based classification of stationary vehicles helps autonomous cars avoid unnecessary stops and choose safe passing behavior.

Explanation data such as similar cases and certainty scores makes automated reasoning decisions traceable and supports human review when confidence is low.

Threshold-based switching between autonomous, cooperative, and manual driving reduces repeated mode changes during temporary driver intervention.

Dynamic magnetic sensor selection helps robotic tools ignore interfered boundary signals and avoid erratic movement far from the wire.

UAV imagery and environmental sensor data refine broad restricted zones into precise operating boundaries for autonomous fleets.

Merging elevation and chromaticity maps improves occupancy grids for mobile robots navigating around transparent, reflective, dark, and small objects.

Configurable LiDAR, thermal, and IR modules correlate sensor data in a common frame to cut parallax errors and decision delays.

A synchronized pipeline with bounded execution and preemptive scheduling cuts sensor-to-actuator latency and jitter in flight controllers.

A blockchain ledger records key autonomous vehicle events to enforce smart liability contracts while limiting data volume and network congestion.

Automatic reentry to viable autonomous flight modes after evasive maneuvers cuts pilot workload and helps keep the aircraft near its planned path.

A universal control router converts trajectory-based inputs into vehicle-specific actuator commands, reducing retraining across aircraft, cars, and watercraft.

By combining georeferenced vegetative index data with in situ sensor input, the machine qualifies real-time models for adaptive subsystem control.

Reference-station networks validate GNSS correction data and carrier phase ambiguities to deliver faster, reliable centimeter-level positioning.

Onboard sensors build a local weather profile so watercraft can adjust speed, direction, and power even when remote connectivity is limited.

Uses staged turning circles and Dubins segments to keep autonomous mobile body paths inside small or edge-constrained areas.

Sensor-triggered smart contracts on a distributed ledger automate liability assignment during autonomous and manual control transitions.

Temporary stop and path switching let an operation vehicle move automatically between work regions without manual intervention.

Vision-guided pose correction and 3D point-cloud mapping let autonomous mowers navigate defined work regions without boundary wires.

Machine-learning docking control adapts robotic vehicle maneuvers to platform and load variation, reducing docking damage risk.

A UAV lands away from the target, then uses ground movement, sensors, and a robotic arm to pick up payloads precisely in unimproved terrain.

Partial 2D scans are matched to known pallet or container configurations so autonomous vehicles can identify and handle obscured transport structures.

A route-setting approach shifts an autonomous work vehicle to a modified stop position with the right orientation while limiting route-change delay.

Iterative planner-controller feedback uses simulated trajectory offsets to improve tracking accuracy while keeping autonomous driving paths collision-free and efficient.

Recorded work-machine position data defines snow-clearing routes automatically, cutting manual setup time while improving target accuracy.

Iterative vertex-based polygon avoidance cuts computation time for safe, flyable aircraft lateral trajectories under fixed vertical constraints.

A UAV switches between mapped wireless stations and short- or long-range links to keep factory communication stable near machinery.

An autonomous sensor platform follows repeatable paths to capture wireless signal anomalies and handoff events across changing access point setups.

Operator task histories are turned into attributes to match remote AV assistance requests faster, cutting downtime and wasted bandwidth.

By combining georeferenced crop index maps with in situ sensor data, the machine updates and validates field models for accurate real-time harvesting control.

A diffuser and optical element reshape LiDAR emission to avoid near-range saturation while improving distant obstacle detection.

Real-time fine-tuning of world and behavior models helps autonomous earth-moving vehicles adapt to changing sites with fewer accidents and delays.