AI USE CASE
Autonomous Haul Truck Fleet Management
AI-driven autonomous navigation and coordination for open-pit mine haul truck fleets.
What it is
Deploys reinforcement learning and computer vision to enable haul trucks to navigate, dispatch, and coordinate autonomously in open-pit mining operations. Autonomous fleets typically reduce haulage operating costs by 15–25%, eliminate fatigue-related incidents, and improve truck utilisation rates by 10–20%. Fuel consumption and tyre wear can drop by 8–15% through optimised routing, while 24/7 continuous operation removes shift-change downtime.
Data you need
Real-time GPS telemetry, LiDAR/camera sensor feeds, historical haul cycle data, mine site topographic maps, and equipment maintenance logs.
Required systems
- erp
- data warehouse
Why it works
- Phased rollout starting with a pilot corridor before full-site autonomous operation reduces risk.
- Dedicated on-site AI operations team to monitor, retrain, and maintain models as site conditions evolve.
- Close partnership with OEM vendors who provide certified sensor suites and safety-validated software stacks.
- Strong change management programme to retrain operators as supervisors and address workforce concerns early.
How this goes wrong
- Sensor degradation in dusty or extreme-weather conditions causes navigation failures and safety incidents.
- Insufficient site-specific training data leads to poor generalisation of the RL model to edge-case terrain.
- Integration complexity with legacy fleet management and dispatch systems delays go-live by 12+ months.
- Regulatory and safety certification requirements for autonomous vehicles on-site stall deployment indefinitely.
When NOT to do this
Do not attempt autonomous fleet deployment at sites with fewer than 20 haul trucks or highly complex, frequently reconfigured pit geometries — the capital investment and integration overhead will not be recovered within any realistic business case.
Vendors to consider
Sources
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