Engineering Autonomous Driving Systems: Building AI, Robotics, and Software for Self-Driving Vehicles

Engineering Autonomous Driving Systems: Building AI, Robotics, and Software for Self-Driving Vehicles

Author: Neil Carden

Description: The hardest part of building a self-driving car is rarely the machine learning. A stack of individually accurate models can still fail in the real world because perception, planning, and control are not coordinated as a single, safety-critical system. This book was written for engineers who want to bridge that gap and design autonomous driving systems that work reliably under tight timing and uncertainty. It's the discipline that turns research prototypes into vehicles you can trust. Engineering Autonomous Driving Systems: Building AI, Robotics, and Software for Self-Driving Vehicles is a comprehensive engineering guide covering the entire autonomous driving stack. It moves from architecture and real-time middleware to perception, sensor fusion, localization, prediction, motion planning, vehicle control, simulation, testing, and production deployment. Rather than treating AI as a black box, the book emphasizes modular design, open source platforms like ROS 2 and Autoware, and the engineering trade-offs behind every algorithm. You'll also learn how to debug and test your stack with the same rigor you'd apply to any safety-critical software. What amplifies the value of this book is its systems-engineering lens. Each chapter follows a consistent path from an engineering problem to the mathematical foundation, then to implementation details and a candid discussion of real-world constraints. The book is structured to mirror the vehicle's data flow: architecture and middleware, perception and data engineering, localization and mapping, prediction and planning, vehicle control, simulation and integration, testing and validation, and finally production engineering. You'll see not only how each module works, but how it depends on the output of the previous module and what happens when those interfaces fail. What can you expect to learn? The book treats each layer of the stack with practical depth. You'll explore open-source tools like ROS 2, Autoware, and CARLA, and use them to build working systems. Here are a few of the core skills you'll gain: • Architect a robust autonomous driving stack with clean module interfaces and strict real-time budgets. • Fuse camera, lidar, and radar data through calibration, time sync, and Kalman filtering for multi-sensor tracking. • Plan safe, smooth trajectories with sampling-based, graph-based, and optimization-based methods, then control the vehicle with PID, pure pursuit, Stanley, and model predictive control. The perception chapters go far beyond simple object detection. You'll learn how to process camera images for object and lane detection, recognize traffic signs and lights, and perform semantic segmentation for dense scene understanding. LiDAR and radar perception cover point cloud processing, 3D object detection, clustering, filtering, and multi-object tracking. In the sensor fusion chapter, the book dives into coordinate transformations, calibration, time synchronization, extended Kalman filtering, and practical fusion architectures. Localization receives equal treatment: GNSS/RTK, IMU, wheel odometry, visual and LiDAR localization, and SLAM with loop closure, plus HD map construction and updating. You'll also see how sensor fusion feeds directly into localization and mapping, creating a coherent world model. Prediction and planning chapters tackle the vehicle's decision-making core. You'll model the future motion of surrounding agents with behavior modeling and multi-agent prediction, including uncertainty. The book then shows how to turn those predictions into actions: global route planning, behavior trees and state machines, lane changes, merging, intersections, and rule-based decisions. Motion planning covers configuration spaces, A*/hybrid A*, RRT, Frenet planning, and trajectory optimization with collision checking. Control finishes the loop with longitudinal and lateral control designs, from PID and geometric trackers to model predictive control. Moving beyond the algorithm, the book devotes significant attention to validation and commercialization. You'll learn how to build high-fidelity simulations with CARLA and Gazebo, create scenario libraries, and run software- and hardware-in-the-loop tests. Fault injection, edge cases, and safety metrics help you assess system readiness. The final chapters explore performance optimization, including latency budgets, CPU/GPU utilization, and real-time scheduling. You'll also learn about automotive-grade hardware, drive-by-wire integration, OTA updates, monitoring, and fleet telemetry to operate autonomous vehicles in production. This is the knowledge that separates demos from dependable products. This book is aimed at software engineers, robotics engineers, and machine learning practitioners who want to build self-driving systems or deepen their understanding of autonomous driving software. It is equally valuable for technical leads and researchers looking for a structured overview of the AV stack. With its focus on real-time constraints, safety, and production realities, Engineering Autonomous Driving Systems offers a clear blueprint for designing, implementing, and deploying autonomous vehicles. If you are ready to move from isolated models to an integrated system, this guide will show you how. The book is written to be a working reference, not just a reading experience.

AI summary: Engineering Autonomous Driving Systems by Neil Carden is a 635-page technical guide covering the complete software stack for self-driving vehicles. The book presents a systems-engineering approach across eight parts: architecture and middleware, perception and data engineering, localization and mapping, prediction and planning, vehicle control, simulation and integration, testing and validation, and production engineering. It is written for software, robotics, and machine learning engineers who want to design, prototype, and deploy autonomous driving systems using tools such as ROS 2, Autoware, and CARLA.

Best for

Software engineers, robotics engineers, and machine learning practitioners building autonomous driving systems; also technical leads and AV researchers.

Reader persona

A software or robotics engineer working on an AV stack who needs practical, systems-level guidance to integrate perception, planning, control, and deployment safely.

Search intent

Engineers searching for a comprehensive, implementation-focused book on how to build and operate autonomous driving software systems.

Unique angle

A full-stack systems-engineering perspective that treats autonomous driving as a safety-critical real-time system, using open-source platforms like ROS 2 and Autoware to illustrate implementations.

Content type

technical engineering guide

Quick summary

• Engineering Autonomous Driving Systems is a technical book that explains how to design and build the software and algorithms for self-driving cars.
• The book covers the full autonomous driving stack, including perception, sensor fusion, localization, prediction, motion planning, control, simulation, and production deployment.
• It is targeted at software, robotics, and machine learning engineers working on autonomous vehicles.
• The book emphasizes open-source platforms like ROS 2, Autoware, and CARLA for building and testing autonomous driving systems.
• It treats autonomous driving as a safety-critical systems engineering problem, not just a collection of machine learning models.

Key topics: Autonomous driving architecture, Real-time middleware and ROS 2, Camera, LiDAR, and radar perception, Sensor fusion, Localization and SLAM, HD mapping, Motion and behavior prediction, Route, behavior, and motion planning, Vehicle control and model predictive control, Simulation, testing, and production deployment

Entities: ROS 2, Autoware, Apollo, CARLA, Gazebo, Kalman filter, Extended Kalman filter, Model Predictive Control, SLAM, HD maps, LiDAR, GNSS/RTK

Needs addressed

• Design a modular, safety-critical architecture for autonomous driving software
• Fuse camera, LiDAR, and radar data into reliable environmental perception
• Plan smooth, collision-free trajectories and control the vehicle with classical and predictive controllers
• Validate AV software through simulation, fault injection, and scenario-based testing
• Deploy and operate autonomous driving systems in production with monitoring and over-the-air updates

Read if

• Autonomous driving software engineers
• Robotics engineers moving into automotive
• ML engineers working on perception, prediction, or planning
• Technical leads and architects designing AV stacks
• Graduate students and researchers in autonomous systems

May not fit if

• Readers without programming or robotics fundamentals
• Anyone seeking a non-technical overview of self-driving car policy or business
• Engineers focused only on hardware/mechanical components rather than software

Table of contents

1. Introduction (introduction)
2. Engineering the Autonomous Driving Stack (part)
3. From Architecture to Implementation (chapter)
4. The Autonomous Driving Stack & Engineering Requirements (section)
5. Real-Time Constraints & Module Interfaces (section)
6. Designing for Safety and Reliability (section)
7. Software Architecture for Autonomous Vehicles (chapter)
8. Modular System Design & Middleware (ROS 2, Autoware, Apollo) (section)
9. Data Flow, Synchronization, Logging & Diagnostics (section)
10. Perception and Data Engineering (part)
11. Camera-Based Perception (chapter)
12. Image Processing, Object & Lane Detection (section)
13. Traffic Sign/Light Recognition & Semantic Segmentation (section)
14. Model Training and Evaluation (section)
15. LiDAR and Radar Perception (chapter)
16. Point Cloud Processing & 3D Object Detection (section)
17. Radar Detection, Tracking, Clustering & Filtering (section)
18. Multi-Object Tracking (section)
19. Sensor Fusion (chapter)
20. Coordinate Transformations, Calibration & Time Sync (section)
21. Kalman & Extended Kalman Filtering (section)
22. Camera-Radar/LiDAR Fusion & Multi-Sensor Tracking (section)
23. Data and Machine Learning Engineering (chapter)
24. Dataset Collection, Annotation & Quality (section)
25. Training Pipelines & Validation Datasets (section)
26. Hard-Case Mining, Versioning, Deployment & Monitoring (section)
27. Localization and Mapping (part)
28. Localization Engineering (chapter)
29. GNSS/RTK, IMU & Wheel Odometry (section)
30. Visual & LiDAR Localization (section)
31. Multi-Sensor Localization & Error Handling (section)
32. Mapping and SLAM (chapter)
33. Map Representation & HD Map Construction (section)
34. Visual & LiDAR SLAM, Loop Closure (section)
35. Map Updating & Localization Against Maps (section)
36. Prediction and Planning (part)
37. Motion and Behavior Prediction (chapter)
38. Object Trajectories & Behavior Modeling (section)
39. Intention & Multi-Agent Prediction (section)
40. Uncertainty Modeling & Prediction Evaluation (section)
41. Route and Behavior Planning (chapter)
42. Global Route Planning & State Machines/Behavior Trees (section)
43. Lane Changes, Merging & Intersections (section)
44. Rule-Based Decision Making (section)
45. Motion Planning (chapter)
46. Configuration Spaces, A*/Hybrid A* & Dijkstra (section)
47. RRT, Sampling-Based & Frenet-Based Planning (section)
48. Optimization-Based Planning, Collision Checking & Trajectory Generation (section)
49. Vehicle Control (part)
50. Longitudinal and Lateral Control (chapter)
51. Vehicle Models & PID Control (section)
52. Pure Pursuit & Stanley Controller (section)
53. Speed/Acceleration Control & Controller Tuning (section)
54. Model Predictive Control (chapter)
55. MPC Fundamentals & Vehicle Constraints (section)
56. Trajectory Tracking & Optimization Formulation (section)
57. Real-Time MPC & Control Trade-Offs (section)
58. Simulation and System Integration (part)
59. Autonomous Driving Simulation (chapter)
60. Why Simulation Matters, CARLA & Gazebo (section)
61. Scenario, Sensor & Traffic Simulation (section)
62. Simulation Limitations (section)
63. Building an Autonomous Driving Prototype (chapter)
64. Setting Up the Stack & Connecting Sensors (section)
65. Perception, Localization, Planning & Control Pipelines (section)
66. Running the Complete Loop & Debugging (section)
67. Testing and Validation (part)
68. Testing Autonomous Driving Software (chapter)
69. Unit, Integration & Regression Testing (section)
70. Software-in-the-Loop & Hardware-in-the-Loop (section)
71. Performance Testing (section)
72. Scenario-Based Validation (chapter)
73. Scenario Libraries & Normal/Dangerous Scenarios (section)
74. Edge Cases & Fault Injection (section)
75. Safety Metrics, Coverage & Validation (section)
76. Production Engineering (part)
77. Performance and Real-Time Optimization (chapter)
78. Latency Budgets & CPU/GPU Utilization (section)
79. Model Optimization & Memory Management (section)
80. Parallel Processing, Scheduling & Real-Time Performance (section)

Frequently asked questions