Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation

Researchers have published a new study on arXiv detailing significant optimizations for Alpamayo 1, a reasoning-based end-to-end autonomous driving system. The paper addresses the efficiency challenges inherent in multi-reasoning approaches, which generate separate reasoning sequences for each predicted trajectory. The authors propose two key improvements: first, transitioning the system to a single-reasoning architecture where one reasoning sequence is shared across all trajectories; second, accelerating diffusion-based action generation by eliminating unnecessary copy operations and inefficient kernel executions. Extensive closed-loop and open-loop experiments demonstrate that these changes reduce inference latency by 69.23% without meaningfully degrading trajectory diversity or prediction quality. This work challenges the assumption that single-reasoning systems sacrifice diversity, highlighting the importance of jointly analyzing system architecture and runtime execution to enhance the performance of interpretable autonomous driving technologies.