Autonomous Vehicles and Drones

What you'll learn

• Classify driving automation with SAE J3016 levels, define operational design domains and describe the architecture of an automated driving system.
• Select and combine cameras, radar, lidar and other sensors for automated vehicle perception, including their limitations in winter weather.
• Explain GNSS, RTK, inertial dead reckoning and map-relative localization, and describe how HD maps are built, used, maintained and monitored for integrity.
• Compare physics-based, map-aware and learned prediction, and design behaviour planners that account for interaction and safety guardrails.
• Apply the bicycle model, Frenet-frame planning, pure pursuit, Stanley and MPC, and compute stopping distance, time to collision and RSS safe distance.
• Model quadrotor thrust, power, endurance and dynamics, and explain cascaded control and failsafes in open-source autopilots.
• Plan autonomous drone missions and explain BVLOS operation, detect-and-avoid, ground risk, delivery and inspection, with awareness of changing rules.
• Use the rule of three, disengagement statistics, simulation and scenario testing, and apply ISO 26262, ISO 21448 and UL 4600 to automated vehicle validation.
• Map the regulation of automated vehicles in Canada and the United States across federal, provincial, state and municipal levels.
• Analyze handover, mode confusion, trust calibration, equity and privacy, and critically evaluate the trolley problem as a guide to vehicle ethics.
• Compare automation in trucking, transit, mining, agriculture and sidewalk delivery and explain why autonomy spreads first to controlled domains.
• Develop a safety case and validation plan for a campus shuttle or delivery drone with hazards, requirements, statistical test quantities and a regulatory pathway.