Baidu’s AI IaaS for Autonomous Driving: Architecture, Performance & Cost Savings

1. Growing Maturity and Market Acceptance of Autonomous Driving

Public data shows that the penetration of autonomous driving is steadily increasing. L2+ penetration has reached 23.2%, and higher‑level L3/L4 capabilities are gaining broader recognition. Baidu’s L4‑based Apollo now processes over ten thousand daily orders, while monthly EV sales have surpassed 600,000 units, indicating strong consumer demand and market potential.

2. Challenges in Building an Autonomous Driving Cloud and End‑to‑End Solution

Accelerating autonomous‑driving technology requires ever‑growing data volumes and larger models that must cover a wide range of scenarios, leading to high cost and time consumption for each iteration. The key challenge is cost efficiency across data ingestion, storage, processing, model training, simulation, and vehicle‑side deployment.

Baidu Baige AI Heterogeneous Computing Platform addresses these challenges by offering a low‑cost, high‑performance AI IaaS solution that tightly integrates AI acceleration, AI storage, and AI container capabilities.

3. Data Storage and Processing Architecture

The platform provides a six‑tier hierarchical storage foundation, ranging from ultra‑low‑cost cold and archive storage to high‑performance parallel file systems. Unified data management enables seamless migration between tiers.

To boost processing efficiency, the solution offers three major categories and thirty sub‑categories of intelligent data handling, dramatically improving data access and business processing speed.

RapidFS, a distributed I/O cache, moves data close to compute memory, delivering five‑fold faster data access.

4. Model Inference and Training Acceleration

4.1 AIAK‑Inference Engine

AIAK‑Inference is a cloud‑native inference accelerator that abstracts multiple high‑efficiency Baidu‑internal engines into a unified framework. It incorporates operator‑level optimizations for typical autonomous‑driving models, achieving 40%‑90% efficiency gains and up to 90% latency reduction.

4.2 AIAK‑Training Engine

AIAK‑Training provides full‑life‑cycle optimization for AI training, covering data loading, forward computation, parameter updates, and distributed training components. It abstracts key components (AIAK.OP, AIAK.IO, AIAK.Loss) so users modify only a few lines of code. Automatic tuning adapts optimization strategies to each model, delivering 50%‑391% training speedup and improving both single‑GPU and multi‑GPU efficiency.

5. AI Container Scheduling and Resource Pooling

5.1 GPU Container Virtualization

The platform supports fractional GPU allocation (e.g., 1/4, 1/2) and offers both user‑mode and kernel‑mode isolation, improving utilization and providing fine‑grained resource control.

5.2 Remote GPU

Remote GPU decouples CPU and GPU resources, enabling arbitrary CPU/GPU ratios. It features dynamic mounting, dynamic isolation, and transparent fault‑tolerance, delivering near‑local‑disk throughput for intermittent tasks such as simulation and data processing.

6. Case Studies

A leading car manufacturer reduced training time by 170% and improved GPU utilization by 2.5× using the end‑to‑end acceleration and pooling capabilities. Internally, Baidu’s Apollo autonomous‑driving solution leverages the same platform for weekly OTA updates and daily simulation of millions of kilometers, dramatically enhancing simulation throughput.