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.
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.
Example vendors
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