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Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation

This paper optimizes the reasoning-based end-to-end autonomous driving system Alpamayo 1 by redesigning it from a multi-reasoning to a single-reasoning architecture and eliminating inefficient kernel operations, achieving a 69.23% reduction in inference latency while preserving trajectory diversity and prediction quality.

Original authors: Yunseong Jeon, Namcheol Lee, Yoonsu Lee, Jangwoon Park, Sol Ahn, Jong-Chan Kim, Seongsoo Hong

Published 2026-05-12

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CC BY 4.0

Original authors: Yunseong Jeon, Namcheol Lee, Yoonsu Lee, Jangwoon Park, Sol Ahn, Jong-Chan Kim, Seongsoo Hong

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are teaching a self-driving car how to navigate a busy city. In the past, these cars were like black boxes: they saw the road and just turned the wheel, but no one knew why they made that decision.

Recently, a new type of car (like the one in this paper, called Alpamayo 1) was built to be more like a human driver. It doesn't just drive; it "thinks out loud." Before it moves, it generates a human-readable explanation (like, "I'm slowing down because there's a pedestrian crossing") and then predicts a few possible paths it could take.

The researchers in this paper looked at this smart car and asked: "It's very smart, but it's also very slow. Can we make it faster without making it dumber?"

Here is how they solved the problem, explained with simple analogies:

The Problem: The "Copy-Paste" Bottleneck

The original Alpamayo system had a habit of being overly thorough. If the car needed to predict six possible future paths (trajectories) to choose the safest one, the system would:

Think of a reason for Path #1.
Think of a completely new reason for Path #2.
Think of a completely new reason for Path #3... and so on.

The Analogy: Imagine you are a chef preparing six different meals for a party. The original system was like a chef who, for every single meal, wrote a brand-new, detailed recipe from scratch before cooking. Even if the meals were similar, the chef wasted time writing six different recipes. This made the kitchen (the computer) very slow, especially when the party got bigger.

The Solution 1: The "One Master Plan" (Single-Reasoning)

The researchers realized that the car didn't actually need a unique recipe for every single path. It just needed one good plan to understand the situation.

They redesigned the system so that the car thinks of one reason (e.g., "There is a pedestrian, so we must be careful") and then uses that same reason to generate all six possible paths. The paths still look different because the car adds a little bit of randomness to the driving moves, just like a human might swerve slightly left or right while staying in the same lane.

The Result: Instead of writing six recipes, the chef now writes one master recipe and uses it to cook all six meals.

Did the food taste different? No. The researchers tested the paths and found they were just as diverse and safe as before.
Did it get faster? Yes, significantly. The time spent "thinking" stopped growing as they asked for more paths.

The Solution 2: The "Assembly Line" (Optimizing the Cooking)

Even with one recipe, the actual cooking process (generating the driving moves) was still clunky. The computer was doing a lot of unnecessary "moving around" of ingredients in its memory. Every time it took a step to refine a path, it would copy its entire workspace to a new table, clear the old one, and start again.

The Analogy: Imagine a painter working on a canvas. The original system was like a painter who, after every single brushstroke, would stop, pack up all their paints, move to a new easel, unpack them, and then paint again. This "packing and unpacking" took up most of the time.

The researchers fixed this by:

Pre-setting the workspace: They prepared a large, fixed workspace that could hold everything needed for the whole painting session. No more packing and unpacking.
Recording the moves: They used a special tool (called CUDA graphs) to record the painter's exact hand movements once. Instead of the painter's brain (the CPU) telling the hand (the GPU) what to do for every single stroke, the brain just hit "Play," and the hand repeated the recorded moves instantly.

The Result: The "packing and unpacking" time vanished, and the painting happened much faster.

The Big Picture

By combining these two fixes, the researchers made the self-driving car 69% faster.

Before: It took about 13 seconds to think and plan for 6 paths.
After: It takes about 4 seconds.

The Takeaway:
The paper proves that you don't need to sacrifice intelligence for speed. By realizing that the car doesn't need to "reinvent the wheel" (or write a new reason) for every single possibility, and by organizing the computer's memory more efficiently, we can make these smart, reasoning-based self-driving systems fast enough to be useful in the real world.

Technical Summary: Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation

Problem Statement

Reasoning-based end-to-end (E2E) autonomous driving systems, such as Alpamayo 1, have emerged to improve the interpretability of driving decisions by generating human-readable reasoning alongside predicted trajectories. However, these systems face significant efficiency challenges that hinder their practical deployment in real-time scenarios.

The paper identifies two primary sources of latency overhead in Alpamayo 1:

Multi-Reasoning Architecture: The system currently employs a "multi-reasoning" approach where a distinct reasoning sequence (Chain-of-Thought) is generated for each predicted trajectory. This design causes inference latency to scale linearly with the number of trajectories, introducing substantial computational redundancy.
Inefficient Diffusion-Based Action Generation: The action generation module, which refines trajectories via iterative diffusion, suffers from runtime inefficiencies. Specifically, it utilizes a dynamic Key-Value (KV) cache management scheme that necessitates unnecessary memory copy operations and reallocations at every iteration. Furthermore, the repeated fine-granular GPU kernel launches from the CPU create significant overhead and idle periods, preventing continuous GPU execution.

Methodology

The authors propose a two-pronged optimization strategy targeting both the system architecture and the runtime execution of the inference pipeline.

1. Architectural Redesign: Single-Reasoning Approach

The authors redesign Alpamayo 1 from a multi-reasoning system to a single-reasoning system.

Mechanism: Instead of generating $N$ separate reasoning sequences for $N$ trajectories, the system generates a single reasoning sequence and its associated KV cache. This single context is then shared across a batch of $N$ randomly initialized action sequences.
Rationale: The authors hypothesize that trajectory diversity is primarily driven by the stochastic initialization of action sequences in the diffusion process rather than the variation in reasoning traces. By sharing the reasoning, redundant computation is eliminated while maintaining diversity through the stochasticity of the action generation module.

2. Runtime Optimization: Efficient Action Generation

To address the latency within the diffusion-based action generation module, two specific optimizations are implemented:

Static KV Cache Management: Recognizing that autonomous driving is a static periodic application where the maximum KV cache size can be precisely estimated offline, the authors replace the dynamic allocation scheme with a static pre-allocation strategy. A fixed buffer is allocated to hold the maximum required KV cache size (approximately 463.74 MB for a single trajectory). During diffusion iterations, the cache is updated via in-place writes using pointers, eliminating memory copy and reallocation overhead.
CUDA Graph Capture: Leveraging the deterministic nature of the action generation process (fixed memory addresses and tensor dimensions across iterations), the authors utilize CUDA graphs. The sequence of GPU kernel launches is captured during a warmup iteration and subsequently replayed as a single graph. This eliminates the per-kernel launch overhead and the associated CPU-GPU synchronization delays.

Key Contributions

The paper makes the following specific contributions:

Detailed Latency Profiling: A granular breakdown of Alpamayo's inference architecture, identifying the multi-reasoning design and dynamic KV cache management as the dominant bottlenecks.
Architectural Validation: A comparative analysis demonstrating that replacing multi-reasoning with single-reasoning does not meaningfully degrade trajectory diversity or prediction quality, challenging the assumption that multiple reasoning traces are necessary for diverse outputs.
Runtime Optimization Techniques: The implementation of static KV cache management and CUDA graph capture to eliminate inter-iteration overhead in diffusion-based action generation.

Experimental Results

The proposed optimizations were evaluated using the NVIDIA Physical AI Dataset (open-loop) and the AlpaSim autonomous driving simulator (closed-loop).

Latency Reduction: The combined optimizations achieve a 69.23% reduction in end-to-end inference latency. For a scenario generating six trajectories, the latency drops from 13.33 seconds (baseline) to 4.10 seconds (optimized).
Trajectory Diversity: Visual comparisons and quantitative metrics indicate that the single-reasoning approach maintains trajectory diversity comparable to the multi-reasoning baseline. The random initialization of action sequences proved sufficient to generate diverse behaviors.
Prediction Quality:
- Open-Loop: The single-reasoning approach achieved a minADE6@6.4s score of 0.743, comparable to the baseline's 0.725.
- Closed-Loop: Using the Distance-to-Failure (DTF) metric, the optimized system achieved 137.12 m, slightly outperforming the baseline's 131.58 m, indicating no degradation in driving stability.
Component Analysis: The action generation optimization alone reduced latency by 24.65% for a single trajectory. However, as the number of trajectories increased, the relative benefit of the single-reasoning architecture grew significantly, as it prevented the linear scaling of reasoning latency.

Significance and Claims

The paper claims that the joint analysis of system architecture and runtime execution is essential for improving the efficiency of reasoning-based E2E autonomous driving systems. By demonstrating that a single reasoning trace can effectively condition multiple diverse trajectories, the authors show that the computational cost of multi-reasoning is unnecessary. Furthermore, by optimizing the underlying runtime mechanisms (memory management and kernel launches), the system becomes viable for real-time applications where baseline latencies were previously prohibitive.

The work suggests that future VLA-based autonomous driving systems can achieve practical deployment by adopting single-reasoning architectures and employing static, deterministic runtime optimizations, thereby balancing interpretability, diversity, and efficiency without sacrificing prediction quality.