Computer vision in autonomous vehicles refers to the use of cameras and algorithms to interpret visual data from the vehicle’s surroundings, enabling it to navigate, detect objects, and make decisions. Unlike systems relying solely on LiDAR or radar, computer vision processes images and video to identify lane markings, traffic signs, pedestrians, vehicles, and other obstacles. For example, a self-driving car uses cameras to detect a red traffic light by analyzing color, shape, and position within the frame. This visual understanding is critical for tasks like lane-keeping, collision avoidance, and path planning, forming the foundation of perception in autonomous systems.
The core technical challenge lies in transforming raw pixel data into actionable insights. Algorithms like convolutional neural networks (CNNs) process images to classify objects (e.g., distinguishing a car from a bicycle) or segment scenes into regions (e.g., road vs. sidewalk). Object detection models such as YOLO (You Only Look Once) or Faster R-CNN identify and localize multiple objects in real time, while semantic segmentation divides the image into categories like “drivable area” or “pedestrian.” These tasks require handling variations in lighting, weather, and occlusion—for instance, recognizing a partially obscured stop sign or a pedestrian hidden behind a parked car. Developers often optimize models for latency and accuracy, balancing the need for real-time inference with reliable results.
Computer vision integrates with other systems to ensure robustness. For example, LiDAR provides depth data to complement camera-based object detection, while radar handles adverse weather conditions. Sensor fusion combines these inputs to reduce errors. Tools like the KITTI dataset provide labeled images for training models, and frameworks like TensorFlow or PyTorch streamline implementation. Testing involves validation against edge cases, such as rare road signs or unusual obstacles, often using simulation tools like CARLA. Developers must continuously refine models to address gaps in real-world performance, ensuring the system adapts to diverse environments. This iterative process, combined with hardware optimizations (e.g., deploying models on embedded GPUs), makes computer vision a critical, evolving component of autonomous driving stacks.