Perception Machines (Paperback)

How do self-driving cars actually see the world?

Perception Machines is a technical field guide to the algorithms, sensors, and compute architectures behind modern driver-assistance and autonomous-driving systems. It connects theory to the real systems decisions engineers make every
day — from sensor selection to latency budgets.

Inside you'll learn:

• How cameras, radar, lidar, and ultrasonics each perceive the driving scene — and where each one fails
• Sensor fusion: how modern stacks combine modalities into one coherent world model
• The compute architecture behind real-time perception, from SoCs to accelerators
• Latency budgets and systems trade-offs that shape every ADAS program
• How perception requirements flow down into validation, safety, and homologation

Who this book is for:

• ADAS and autonomous-vehicle engineers who want the full systems picture
• Robotics engineers moving into automotive perception
• Engineering graduate students studying sensor fusion and real-time systems
• Technical product managers and leaders who need to speak the language of perception

Written by an ADAS engineer with industry experience at automotive suppliers, a patent in the field, and published IEEE research, this book skips the hype and focuses on what actually ships in vehicles.

If you build, study, or lead teams working on machines that perceive the world — this is your field manual.