Autonomous vehicles navigate and make decisions using a combination of sensors, software, and algorithms designed to perceive the environment, plan routes, and execute driving actions. The core system relies on real-time data from sensors like LiDAR, cameras, radar, and ultrasonic sensors. These sensors feed data into a perception system that identifies objects (e.g., vehicles, pedestrians, traffic signs) and tracks their positions and movements. Localization techniques, such as GPS fused with inertial measurement units (IMUs) and high-definition maps, help the vehicle pinpoint its exact location. For example, LiDAR creates a 3D point cloud of the surroundings, while cameras detect lane markings and traffic signals, enabling the vehicle to “see” its environment.
The decision-making process involves path planning and behavior prediction. Path planning algorithms generate safe trajectories by considering factors like speed limits, traffic rules, and obstacles. Machine learning models, such as neural networks, are often trained on large datasets to predict how other road users might behave. For instance, if a pedestrian steps onto a crosswalk, the system evaluates their trajectory and adjusts the vehicle’s speed or steering angle to avoid a collision. Control systems, like PID controllers or model-predictive control, translate these decisions into precise throttle, brake, and steering inputs. Developers often use simulation tools like CARLA or Apollo to test these algorithms under diverse scenarios before real-world deployment.
Safety and redundancy are critical. Autonomous systems are designed with fail-safes, such as multiple sensor redundancies (e.g., radar cross-verifying camera data) and fallback algorithms for edge cases. For example, if a camera is blinded by sunlight, the vehicle might rely more on radar or LiDAR until visibility improves. Decision-making layers prioritize actions based on risk—like swerving versus braking—using cost functions that weigh collision severity and passenger safety. Open-source frameworks, such as ROS (Robot Operating System), provide modular architectures for integrating these components. By combining real-time data processing, predictive modeling, and robust control logic, autonomous vehicles aim to replicate human driving intelligence while minimizing errors.