Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation

TL;DR

This paper improves the efficiency of reasoning-based end-to-end autonomous driving systems by redesigning Alpamayo 1 into a single-reasoning architecture and optimizing diffusion-based action generation, significantly reducing inference latency.

Contribution

It systematically analyzes Alpamayo 1's architecture, demonstrating that single-reasoning maintains diversity and accelerates inference, with practical optimizations reducing latency by over 69%.

Findings

Replacing multi-reasoning with single-reasoning preserves trajectory diversity.

Optimizations eliminate inter-block overhead, accelerating diffusion-based generation.

Achieved a 69.23% reduction in inference latency without sacrificing performance.

Abstract

Reasoning-based end-to-end (E2E) autonomous driving has recently emerged as a promising approach to improving the interpretability of driving decisions as it can generate human-readable reasoning together with predicted trajectories. Such approaches commonly generate multiple trajectories to capture diverse future behaviors, and they fall into two categories: (1) multi-reasoning, where one reasoning sequence is generated per trajectory, and (2) single-reasoning, where a single reasoning is shared across all trajectories. The former offers richer diversity at the cost of redundant computation, while the latter is more efficient but is often assumed to sacrifice diversity. Alpamayo 1, a representative system, adopts the multi-reasoning approach and achieves competitive trajectory prediction performance. However, the efficiency of this design remains largely unexplored, making it a well-motivated subject for investigation. In this paper, we systematically analyze and improve Alpamayo 1 in two ways. First, we reduce inference latency while preserving trajectory diversity by redesigning Alpamayo 1 into a single-reasoning system. Through extensive experiments, we find that replacing multi-reasoning with single-reasoning does not meaningfully degrade trajectory diversity. Second, we accelerate diffusion-based action generation by eliminating inter-block overhead arising from unnecessary copy operations and inefficient kernel execution. Through closed-loop and open-loop experiments, we validate both optimizations, demonstrating a 69.23% reduction in inference latency while maintaining trajectory diversity and prediction quality. These results highlight the importance of jointly analyzing system architecture and runtime execution to improve the efficiency of reasoning-based E2E AD systems.