Technical White Paper

Scaling In-Vehicle AI

From Telematics to Autonomous Perception

A Technical White Paper on How Compute Requirements Evolve Across In-Vehicle AI Workloads

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What You'll Learn

Traditional in-vehicle computing has focused heavily on vehicle telemetry, connectivity, data logging, and fleet management. As AI moves into driver monitoring, perception, sensor fusion, and autonomous functions, system requirements become significantly more complex.

This white paper provides a practical framework for understanding how in-vehicle AI workloads scale and how to select the right compute architecture for deployment.

How in-vehicle AI scales from Monitor → Analyze → Understand → Act
How system requirements change from basic telematics to autonomous perception
How to compare x86, NVIDIA Jetson, and x86 + discrete GPU architectures
How sensor bandwidth, memory, storage, power, and thermal constraints affect system design
How to select an architecture based on the complete workload and data path

Driver Monitoring

Multi-Camera Video Analytics

Sensor Fusion

Autonomous Navigation

Why Download the White Paper?

In-Vehicle AI Workloads Are Becoming More Complex

As cameras, AI inference, and sensor complexity increase, bottlenecks can shift from processor performance to sensor bandwidth, memory throughput, storage, networking, power, and thermal capacity.

Selecting hardware based only on CPU, GPU, or TOPS performance can result in an architecture that cannot support the complete workload.

This white paper introduces a workload-driven framework that evaluates the entire system—from sensors and interfaces to processing, storage, networking, and vehicle output.

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In-Vehicle Computing VS In-Vehicle AI

Traditional in-vehicle computing connects and monitors. In-vehicle AI adds interpretation, intelligence, and real-time decision support.

In-Vehicle Computing

Collects vehicle data
Records camera feeds
Reads CAN Bus data
Connects to fleet systems

In-Vehicle AI

Interprets vehicle data
Analyzes camera feeds
Combines telemetry with AI
Generates intelligent events

How In-Vehicle AI Workloads Scale

Monitor Analyze Understand Act

As workloads progress through these levels, the entire system architecture must scale together—not just compute performance.

Level 1: Basic AI Telematics

Basic AI telematics systems use embedded computing to monitor vehicle status, record video, and perform lightweight AI analytics. These systems focus on improving visibility into vehicle operations and enhancing basic safety monitoring.

Level 2: Smart Fleet AI

Smart fleet AI platforms enable vehicles to analyze multiple data sources such as cameras, sensors, and vehicle networks in real time. These systems enhance fleet safety, operational efficiency, and situational awareness.

Level 3: Advanced Vehicle Perception Systems

Advanced vehicle perception systems use high-performance AI computing to process multi-camera and sensor data for real-time environmental understanding. These platforms enable intelligent perception for robotics, industrial vehicles, and advanced transportation systems.

Level 4: Autonomous Vehicle Systems

Autonomous vehicle systems use powerful AI platforms to enable vehicles to perceive their environment, make decisions, and operate with minimal or no human intervention. These systems combine perception, localization, and planning to support autonomous mobility.