Pony.ai Infrastructure Overview: Vehicle Systems, Simulation Platform, and Data Architecture

1. Pony.ai Infrastructure

Pony.ai, an autonomous driving company, faces typical internet‑scale infrastructure challenges such as storage, compute platforms, and web service governance, plus additional vehicle‑specific challenges like onboard systems, simulation platforms, and fleet operation requirements.

The rapid expansion of its autonomous fleet demands highly scalable infrastructure to handle increasing vehicle numbers, data volume, engineers, and code base.

2. Vehicle Onboard System

The onboard system processes sensor data (LiDAR, cameras, etc.) through a pipeline of Perception, Prediction, Planner, and Control modules, supported by high‑precision maps, localization, and routing.

Key requirements include reliable module communication, heterogeneous compute resource allocation (GPU for perception, CPU for other modules), extensive logging for debugging, and stringent safety monitoring and alerting.

To meet these needs, Pony.ai developed its own framework, PonyBrain, abandoning ROS due to concerns about code quality control and suitability for autonomous driving.

Engineering practices for reliability include strict code review, unit testing, static analysis, ASAN, memory leak detection, multi‑stage system checks, and continuous integration with stable releases for fleet testing.

Performance optimizations involve careful message design to avoid costly data copies, resource‑aware scheduling, profiling to identify bottlenecks, and a flexible module interface for diverse compute resources.

3. Simulation Platform

The simulation platform provides low‑cost, low‑risk testing and rapid data‑driven algorithm iteration, supporting both replay of real‑world scenarios and generation of synthetic cases.

Challenges include ensuring simulation fidelity to vehicle dynamics, managing large volumes of road‑test data, and delivering high‑throughput distributed simulations for fast feedback.

4. Data Infrastructure

Data is the core driver for autonomous driving, with petabyte‑scale storage needs for sensor streams, logs, and map data.

Key challenges involve designing storage formats for both random and sequential access, selecting appropriate hot/cold storage solutions, ensuring high availability, horizontal scalability, and cost control.

Data processing pipelines must handle both CPU‑intensive and I/O‑intensive workloads, reduce latency from collection to analysis, and provide flexible task definitions for new processing jobs.

Data synchronization across multiple test sites and offices must operate under bandwidth constraints while maintaining a unified system.

5. Supporting Infrastructure Services

Fleet operation services require rapid development and iteration of web services, complex business logic, and large‑scale service orchestration, often built on Kubernetes.

Additional needs include cross‑platform visualization tools for algorithm and scenario inspection, demanding high‑performance 3D rendering and user‑friendly interfaces.

The article concludes with author information and community promotion.