How Baidu Cloud Powers End-to-End Autonomous Driving Data Ops and AI

1. Autonomous Driving Data Closed‑Loop Business Scenario

Automatic driving development typically follows a workflow that starts with data collection, then proceeds through data transmission, storage, and processing, branching into two loops: the perception loop (data processing, model training, algorithm R&D) and the simulation loop (scenario mining, conversion, and testing).

The presented solution aims to build two data‑closed‑loop chains for autonomous driving.

2. Baidu Intelligent Cloud – Data Operation Service Overview

2.1 Road Data Collection Service

Data originates from production vehicles or dedicated collection vehicles. Challenges include vehicle modification, mapping qualification compliance, and post‑collection data quality control. Baidu provides services covering vehicle retrofitting, road collection, and compliant data cleaning.

2.2 Data Compliance Service

Since the 2022 regulation, road data is considered mapping data, requiring compliance for usage. Baidu offers a compliance solution that designs end‑to‑end compliant schemes, provides compliance environments (offline rooms or online clouds), and handles data desensitization.

2.3 Data Annotation Service

Large volumes of collected data (hundreds of kilometers per day) need annotation before model training. Enterprises can either build internal teams or outsource; Baidu combines annotation services, project management, and manpower to deliver efficient, high‑quality labeling, with AI‑assisted pre‑labeling, smart tools, and multi‑stage quality checks.

2.4 Data Management Operation Service

For massive datasets, Baidu offers three categories of operation services: full‑process project management, data mining (19 major categories, 70+ sub‑categories), and data quality assurance using internal AI algorithms.

2.5 Model Training Service

Baidu assists in building effective training datasets, planning annotation scope, and evaluating models against a large benchmark set to identify bad cases and supplement training data for performance improvement.

2.6 Simulation Scenario Library Construction Service

Collected data is transformed into high‑precision OpenDRIVE files via Baidu's Log‑to‑World tool, enabling large‑scale simulation testing without costly real‑world runs.

3. Autonomous Driving Toolchain Platform

3.1 Four Major Tool Platforms

The ecosystem includes a Data Management Platform, Annotation Platform, Perception Model Training Platform, and Simulation Cloud Platform, each supporting the end‑to‑end workflow.

3.2 Data Management Platform

Provides unified storage, data cleaning, processing, visualization, and mining for raw and annotated data, supporting multi‑modal formats (images, point clouds, vehicle telemetry, etc.).

3.3 Intelligent Annotation Platform

Offers project management, 2D/3D/voice/text annotation tools, AI pre‑labeling, AI‑assisted labeling, and AI‑driven quality inspection.

3.4 Perception Training Platform

Supports major frameworks such as TensorFlow, PyTorch, and PaddlePaddle, with model acceleration and edge deployment capabilities.

3.5 Perception Evaluation Platform

Integrates Baidu's accumulated evaluation methods and metrics to generate visual reports, helping identify model weaknesses and guide data supplementation.

3.6 Simulation Platform

Combines a cloud‑based simulation engine, distributed testing modules, and tools for converting raw data into simulation scenarios.