Autonomous Decision Making Process TRIZ Cases

When obstacles block a planned path, the vehicle partially crosses lane boundaries to avoid getting stuck and continue the route safely.

Ranked heuristics and pre-mapped stopping points improve pickup clarity for autonomous vehicles without relying on vague user-vehicle coordination.

Vehicle operation data is classified to detect unauthorized autonomous driving without module output, enabling alerts or input inhibition.

By contracting occluded roadway regions from GPS and sensor data, this case cuts trajectory planning load while preserving robust autonomous navigation.

Preplanned switching times and trajectory constraints let autonomous vehicles hedge against uncertain agent paths without abrupt slowing or stopping.

A difficulty score guides whether an autonomous vehicle should use a same-side driveway to avoid inconvenient opposite-side pickup or drop-off.

Laser-guided AGV doffing aligns elongate arms with loader receptacles to move heavy bobbins faster with less manual handling.

Relative language commands are mapped to absolute vehicle paths, enabling self-guided navigation in continuous real-world environments.

Cryptographic hash chains and distributed ledger storage verify vehicle software configurations quickly and expose unauthorized modifications.

Severity-based stop planning adapts motion constraints to vehicle and environment states, enabling safer autonomous stops with less planning overhead.

Immersive response testing measures driver accuracy and reaction time to set autonomous vehicle warning times for safer handover.

Visibility grids model what external road users can and cannot see, letting autonomous vehicles adjust driving parameters in occluded zones.

Powered wheels and docking couplers let a bedside waste collector self-empty, clean, and charge without interrupting surgery.

Multi-sensor vehicle monitoring detects pedestrian movement and sends timely audible or visual alerts to improve driver response.

Virtual simulation and real-world feature monitoring improve autonomous vehicle risk scoring for fairer insurance premium adjustment.

Visual cues flag region offset when reference stations differ, helping operators choose autonomous travel areas more accurately.

Axle deflection and suspension stiffness data reveal load distribution, letting autonomous vehicles adjust routes and maneuvers for safer, lower-wear travel.

Blockchain hash verification checks vehicle software configurations at scale and triggers failsafe code when tampering is detected.