Pony.ai

Technology

Lidars

Radars

Cameras

Combined

We develop our technology to master a variety of demanding road scenarios across the US and China and address some of the toughest challenges in the safe deployment of autonomous vehicles.

Localization & Map

Perception

Prediction

Planning & Control

Infrastructure

Hardware

Localization is critical for our vehicle to determine its precise physical location - down to the centimeter-level accuracy. Our multi-sensor fusion approach provides rich datasets that enables us to understand the static world around us, providing key information required for safe driving operations.

Our Perception module combines the strengths of a heuristic approach and deep learning models to boost performance, while ensuring the safety and operational redundancy of our vehicles. Performance capability is further enhanced by our multi-sensor fusion technology, which intelligently leverages the reliable sensor data depending on different environmental or driving scenarios.

The Prediction module works to project how other vehicles, pedestrians, and objects (together, “road agents”) may move or behave based on several inputs, including Perception output, raw sensor data, and data regarding previous decisions made by the road agent (i.e. sequence of events). The output of the Prediction module for a certain road agent is a series of predicted trajectories, each with an assigned probability of occurrence.

We fuse machine learning and deep learning to create a robust Planning and Control module able to smoothly navigate complex road scenarios — from the streets and highways of California to the bustling eight-lane intersections of Chinese metropolises, while being prepared for outlier behaviors or events caused by other road agents.

Successful autonomous driving technology deployment and scale rely on an entire set of supporting infrastructure. From the real-time onboard system to off-vehicle data processing and management, we have built a solid foundation that drives rapid iteration, scalable development of a high-quality system, and efficient testing in all aspects of software and hardware development.

Excellent performance, high reliability, and deployment-ready are the key guiding principles for our hardware solutions. Our engineers custom design the sensor fusion module and compute system to bring high performance components together with cutting-edge software, resulting in a tightly integrated full-stack system. All driving and safety-critical elements are equipped with hardware redundancies.

Extreme weather

Rush hour

Long-haul freight

Animals on the road

Comprehensive Safety Guarantee

In order to ensure safety, Pony.ai's autonomous driving vehicles are based on ISO 26262 functional safety methodology, with comprehensive functional safety and redundancy. Our principle is that in the event of a single-point failure in the autonomous driving system or the vehicle, the vehicle can continue to operate safely; in the event of a dual-point failure, the vehicle can park safely.

20+ Safety Redundancies

7

Types of Autonomous Driving

Software System Redundancy

Multi-Layer Degradation System Redundancy
Fault Detection and System Arbitration Module Redundancy
Heterogeneous Algorithm on Main and Fallback System
Communication Redundancy on Main and Fallback System
Trajectory Cross Validation Redundancy on Main and Fallback System
Multi-Sensor Fusion Perception & Localization Redundancy
Multi-Algorithm Fusion Redundancy of Key ADS Modules

7

Types of Autonomous Driving Hardware

Component Redundancy

N×360° FOV coverage
Redundant Systems of Computing Units
Redundant Localization Sensors
Redundant Cellular Network Communications
Redundant Accident Detection
Redundant Storage for Key Data
Redundant Sensor Cleaning

5

Types of Vehicle

Platform Redundancy

Redundant Parking Brake System
Redundant Steering System
Redundant Braking System
Redundant Power Supply
Redundant DBW System

3

Types of User Interaction

Service Redundancy

Redundant Safety Warning outside the Car
Redundant Unlock Method by Cellphone NFC
Redundant System for Emergency Calls

1000+ Monitoring Mechanisms

Based on ISO 26262 functional safety methodology, more than a thousand monitoring mechanisms run in parallel with normal functions;
Based on the failure analysis and hazard evaluation, the failure mode and safety state are fully taken into consideration.

Multi-Layer Degradation Strategy

Capability to select the optimal MRM trajectory in complex scenarios, attaching importance to both safety and usability.

Multiple Vehicle Platforms

From all-electric passenger Robotaxis to long-distance freight trucks, our learnings and algorithms can be generalized across multiple vehicle platforms and applications to bring autonomous driving technology to a wide audience.

BYD Qin

Lincoln MKZ

Hyundai Kona

Lexus RX450

Aion LX

E-HS3

Toyota Sienna

Aion LX

Faw Jiefang Automotive J7

Sany JIANGSHAN

Safety First

We develop our technology to master a variety of demanding road scenarios across the US and China and address some of the toughest challenges in the safe deployment of autonomous vehicles.

Download Safety Report