Three-layer redundancy architecture delivers robust physical AI for autonomous driving
PlusAI's AV2.0 Architecture for the Physical AI World
SuperDrive™'s perception system detecting and tracking vehicles in real-time with 3D spatial understanding
The Cutting-Edge REASONING-REFLEX Dual-Model
REASONING
Leverages a Vision Language Model to interpret complex real-world interactions and generate high-level driving decisions for out-of-ODD edge cases. Acts as strategic driving intelligence, understanding context and making informed decisions for unusual scenarios.
REFLEX
Our state-of-the-art transformer-based end-to-end model fuses perception with motion planning to execute rapid, real-time maneuvers. Our network design allows us to provide industry-leading high-performance models that can rapidly scale up to the latest automotive grade SoCs.
Redundant Fallback System
Operates on a secondary edge computer that continuously monitors the primary system's health. If a fault is detected, it triggers minimal-risk fallback maneuvers to bring the vehicle safely to a stop.
Remote Operations
Provides mission control and human-in-the-loop support for scenarios beyond the virtual driver's Operational Design Domain (ODD), ensuring oversight and intervention when needed.
Key Advantages
Interpretability
Through semantic reasoning via a Vision Language Model and a defined perception layer, SuperDrive™'s architecture ensures that each driving decision is transparent, explainable, and trustworthy for engineers and stakeholders.
Traceability
The architecture enables full traceability, making it possible to trace and rectify errors, identify corner cases, and support continual improvement across the entire autonomy stack.
Enhanced Safety
Rule-based safety envelopes enable region-specific driving behavior while cloud-based remote operations provide a human-in-the-loop for scenarios beyond the virtual driver's ODD.
Scalability
Zero-shot learning capabilities allow SuperDrive™ to adapt to out-of-sample edge cases, diverse regions, and new vehicle platforms without extensive retraining.