Outrider Deploys Reinforcement Learning Technology for Distribution Yard Throughput

Outrider announces its deployment of advanced reinforcement learning (RL) techniques to maximize freight throughput at customer sites.

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Outrider announces its deployment of advanced reinforcement learning (RL) techniques to maximize freight throughput at customer sites. Outrider’s RL models increase path planning speed by 10x and enable the Outrider System to move freight more efficiently and safely through busy, complex distribution yards.  

“Using the latest advances in AI, Outrider is continually decreasing the turn time of trailers moved autonomously in logistics yards,” says Vittorio Ziparo, CTO and executive vice president of engineering. “By training and evaluating our system performance with RL in simulation and real-world scenarios, our customers see incremental improvements in speed and efficiency with our technology.”

Key Takeaways:

  • Outrider’s AI-driven capabilities are complemented by industry-first redundant safety mechanisms, merging the benefits of AI with traditional functional safety approaches used for industrial operations. Outrider has addressed over 200,000 safety scenarios, and multiple third-party safety experts and Fortune 500 customers have validated its safety case.
  • Using years of data samples of behaviors, Outrider developed an RL curriculum of increasing difficulty for the model to learn. This technique reinforces preferred behaviors, such as following traffic rules and maintaining safe distances from other vehicles, and discourages undesirable behaviors. Once the RL models are tested extensively in simulation and on-vehicle at Outrider’s Advanced Testing Facility, the model and code are deployed into autonomous operations at customer sites.


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