Autonomous Driving Fundamentals

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Autonomous Driving Fundamentals
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 854.37 MB | Duration: 1h 52m
Understand Automated Driving Systems, SAE Automation Levels, ODD, Perception, Localization, Prediction, Motion Planning

What you'll learn
Understand the architecture of automated driving systems
Distinguish ADAS from higher driving automation levels
Understand SAE Levels 0-5 and the Operational Design Domain
Explain sensing, localization, perception and sensor fusion
Understand prediction and uncertainty in dynamic traffic
Distinguish behavior, path and trajectory planning
Understand longitudinal, lateral and closed-loop vehicle control
Explain system limits, fallback, safety and cybersecurity concepts
Requirements
No previous automated-driving experience required
Description
This course contains the use of artificial intelligence.Understand the engineering principles behind modern automated driving systems.Automated driving combines sensing, localization, perception, prediction, decision making, motion planning and vehicle control into one continuously operating system. Understanding how these elements interact is essential for engineers working with modern automotive technologies.In this course, we build a structured understanding of the complete automated-driving chain - from observing the environment to executing controlled vehicle motion.We begin with the fundamentals of automated driving and the SAE Levels of Driving Automation, including the important differences between driver assistance, conditional automation and highly automated driving.You will then learn about the Operational Design Domain (ODD) and why automated-driving capability must always be understood within defined operating conditions.From there, we explore the technical architecture of an automated-driving system. You will learn how cameras, radar and LiDAR contribute environmental information, how localization and mapping establish the vehicle's position, and how perception and sensor fusion transform sensor data into a structured environment model.The course then moves beyond perception to one of the most important challenges in automated driving: understanding what may happen next.We examine how automated-driving systems interpret surrounding road users, predict possible future motion and manage uncertainty when several future behaviors remain plausible.Next, we follow the transition from environmental understanding to vehicle action. You will learn the differences between behavior planning, path planning and trajectory planning, how candidate trajectories are evaluated against constraints, and how an appropriate vehicle trajectory is selected.Finally, we connect planning with the physical vehicle through longitudinal and lateral control, steering, braking and propulsion.The course also introduces essential system-level topics including system limitations, fallback, Minimal Risk Conditions, Functional Safety, SOTIF and automotive cybersecurity.A complete automated highway-driving example brings these elements together and demonstrates how sensing, prediction, planning and control continuously interact during real vehicle operation.
Automotive engineers and developers,Systems and software engineers,ADAS and automated-driving professionals,Safety, SOTIF and cybersecurity engineers,Test, validation and integration engineers,Engineering students and technical professionals
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