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Autonomous Driving Technology
By Kim Si-yeon Reporter, Sophomore of Electronic Engineering
A new era is approaching in which vehicles can operate without human drivers. Technologies such as Tesla’s Full Self-Driving (FSD) and Google’s Waymo represent crucial steps toward achieving “full autonomy.” Autonomous driving is advancing in stages from Level 1 to Level 5, with the current state remaining at Levels 2–3. Level 1 refers to single-function assistance, such as steering or acceleration support. At Level 2, partial steering and acceleration assistance are possible, while Level 3 allows conditional driving without human intervention under specific circumstances. Looking ahead, Levels 4 and 5 target complete driverless driving, carrying the potential to reshape transportation systems and daily life. The advancement of autonomous vehicles largely depends on the integration of three core technologies: control, vision, and localization. This article will explore how these elements enable vehicles to drive themselves.
Technologies for Autonomous Driving
Control technology serves as the brain of autonomous driving. A vehicle must do more than simply drive straight and turn. It must determine the optimal path while considering road conditions and destinations. Path-tracking algorithms make this possible. They compare the car’s current position with the designated route and then calculate the appropriate steering angle and speed. Optimal control methods ensure smoother and more stable driving while balancing efficiency and performance. Control technology also plays a vital role in emergencies. Automatic braking and stability systems work to protect passengers when sudden risks appear.
Vision technology functions as the eyes of an autonomous vehicle. Cameras, LiDAR, and radar detect pedestrians, vehicles, and traffic signals on the road. Segmentation techniques identify lanes and road boundaries. Tracking methods follow moving objects over time. With behavior prediction, the system can estimate the future actions of pedestrians and vehicles. A single sensor has limitations under difficult weather or environmental conditions. For this reason, sensor fusion, which combines data from multiple sensors, plays a critical role in achieving reliable perception.
Localization technology allows vehicles to determine their exact position on the road. GNSS, the Global Navigation Satellite System, and INS, the Inertial Navigation System, provide stability for long-distance driving, but their signals weaken in cities and tunnels. To address this limitation, SLAM, or Simultaneous Localization and Mapping, is used. The vehicle builds maps in real time and estimates its own position. HD map-matching technology refines this process to the lane level and increases driving stability. In tunnels and underground roads, correction methods maintain continuous operation. Control, vision and localization have advanced on their own, but complete autonomy can only be achieved when all three work together on real roads.
Barriers to Commercialization
Experts note that many challenges remain before autonomous vehicles can be fully commercialized. Safety and reliability must come first, and legal systems require urgent reforms. Questions about accident responsibility, insurance coverage and data use are still unresolved. In addition, infrastructure such as high-definition maps and V2X, or Vehicle-to-Everything communication, must be established. Public acceptance is another crucial factor.
Autonomous vehicles are no longer confined to science-fiction. If technological progress, legal support and social acceptance move forward in balance, the day will soon arrive when people in cities experience a world where holding the steering wheel becomes unnecessary.