Autonomous Vehicle Computing System

An Autonomous Vehicle Computing System is a real-time computing system that processes sensor data streams for autonomous driving decision tasks.

• AKA: AV Compute Platform, Autonomous Vehicle Processing Unit, Self-Driving Computer System.
• Context:
  • It can typically execute Neural Network Inference Tasks through specialized processor units.
  • It can typically process Sensor Data Fusion Tasks via parallel computing architecture.
  • It can typically run Motion Planning Algorithms using real-time operating systems.
  • It can typically manage Power Consumption Optimization through dynamic frequency scaling.
  • It can often coordinate GPU Processors with CPU processors for heterogeneous computing tasks.
  • It can often implement Redundancy Architectures via backup processing systems.
  • It can range from being a Centralized Computing System to being a Distributed Computing System, depending on its architectural topology.
  • It can range from being a Low-Power Computing System to being a High-Performance Computing System, depending on its processing capability.
  • It can range from being an Air-Cooled Computing System to being a Liquid-Cooled Computing System, depending on its thermal management.
  • It can range from being an Edge-Only Computing System to being a Hybrid Edge-Cloud Computing System, depending on its processing location.
  • It can interface with Autonomous Vehicle Sensor Systems for perception data processing.
  • ...
• Examples:
  • Custom Computing Platforms, such as:
    • Waymo Custom Compute Unit with proprietary ASIC design.
    • Tesla FSD Computer with neural network accelerators.
  • Commercial Computing Platforms, such as:
    • NVIDIA Drive Platform for AI-based processing.
    • Mobileye EyeQ Platform for vision processing.
    • Qualcomm Snapdragon Ride for scalable computing.
  • Processing Architectures, such as:
    • TPU-Based Computing System for tensor operations.
    • FPGA-Based Computing System for configurable acceleration.
  • ...
• Counter-Examples:
  • Cloud Computing System, which lacks real-time edge processing.
  • Desktop Computing System, which lacks automotive-grade requirements.
  • Smartphone Computing System, which lacks autonomous driving optimization.
• See: Computing System, Real-Time Computing System, Edge Computing System, GPU Computing, Neural Processing Unit, Embedded System, Autonomous Vehicle Sensor System.

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