Warehouse Safety Training

BRIEF

Training people for danger, without the danger

Warehouse accidents cluster around the exact situations you cannot rehearse on a live floor: forklift crossings, blocked escape routes, loads placed where they should never be. Classroom slides do not build reflexes.

The brief: a training environment that feels real enough to change behavior, is safe enough to fail in, and is repeatable enough to measure. The constraint: it had to run on ordinary hardware a logistics company already owns.

• N-01Risk scenarios collected from real incident categories, not invented ones.
• N-02Repeatability chosen over spectacle: every scenario can be re-run and scored.

STRUCTURE

The scenario is the wireframe

Before any 3D, the training was designed as flows: walk a route, spot the hazard, respond, get corrected. Each scenario was mapped as a state machine of trainee decisions, wrong turns included, so the simulation always has an answer to what a person actually does.

The HUD was wireframed like a product, not a game: what the trainee must see at all times (zone status, current task), what appears only on error, and what stays out of the way.

• N-03A hazard taxonomy became the backbone: every part of the scene belongs to a risk class.
• N-04Failure paths designed first; success is just the path with no corrections.

SYSTEM

A visual language borrowed from the floor itself

The interface system reuses what warehouse workers already read fluently: floor markings, high-visibility signage, zone striping. Safety yellow is reserved for live danger and active guidance, so attention always means something.

Type in the HUD is monospaced and terse, sized for peripheral reading while moving. Every label follows the same grammar: zone, state, action.

• N-05One accent color, mapped to danger and guidance only, never decoration.
• N-06HUD text tested at walking speed: if it needs a pause to read, it got shorter.

CRAFT

A warehouse built to feel worked-in

The 3D environment was modeled and lit to read as a real workplace: racking with inconsistent loads, wear on the floor paint, forklift routes that make operational sense. Realism here is not cosmetic, hazard recognition only transfers if the scene looks like the trainee's actual world.

Movement, camera behavior, and sound were tuned so the body believes the space: head height, walking pace, the audible approach of a forklift you cannot see yet.

• N-07Audio does safety work: engine sound direction is a training signal, not ambience.
• N-08Lighting passes kept physically plausible so shadows never hide a hazard unfairly.

BUILD

An AI instructor wired into the scene graph

Built in Unity with C#. Every scenario runs on the decision state machine from the structure phase; trainee position, gaze, and actions stream into a telemetry layer.

The AI instructor consumes that telemetry live: it knows the trainee walked through an active forklift lane before a human observer would, and it phrases the correction in context. Sessions end with a generated report instead of a supervisor's memory.

• N-09AI feedback is grounded in scene telemetry, so corrections cite what actually happened.
• N-10Report format designed with supervisors: what they file, not what looks impressive.

SHIPPED

Assembled, live, and safe to fail in

The module ships as a complete loop: brief, scenario, correction, report. Trainees rehearse the situations that used to be untrainable, and supervisors get evidence instead of impressions.

• N-11The full six-layer assembly you just scrolled through is the product's real architecture.