Computer Vision in Autonomous Vehicles: How Self-Driving Cars See

A self-driving car must understand its environment as well as -- or better than -- a human driver. It needs to detect pedestrians, read traffic signs, identify lane markings, track other vehicles, estimate distances, predict trajectories, and react to unexpected events, all in real time. Computer vision is the primary technology that makes this possible, and the perception stack of an autonomous vehicle represents some of the most sophisticated computer vision engineering in the world.

The Sensor Suite: How Autonomous Vehicles Gather Data

Modern autonomous vehicles use multiple types of sensors, each with strengths and weaknesses that complement each other.

Cameras

Cameras provide rich color and texture information, excel at reading signs and traffic lights, and are inexpensive. Most AVs use 6-12 cameras providing a 360-degree view. However, cameras struggle in poor lighting, rain, and direct sunlight, and they don't directly measure distance.

LiDAR

LiDAR (Light Detection and Ranging) fires laser pulses and measures their return time to create precise 3D point clouds of the environment. It provides accurate distance measurements up to 200+ meters and works in any lighting condition. However, LiDAR is expensive, produces sparse data, and struggles in heavy rain and fog.

Radar

Radar uses radio waves to detect objects and measure their velocity directly. It works in all weather conditions and is inexpensive, but provides low spatial resolution. Radar is particularly valuable for detecting fast-moving objects and measuring their speed using the Doppler effect.

The camera-vs-LiDAR debate (epitomized by Tesla's camera-only approach vs Waymo's multi-sensor strategy) is one of the defining controversies in autonomous driving. Both approaches have proven viable, but with different trade-offs in cost, capability, and reliability.

Core Perception Tasks

3D Object Detection

The fundamental task: identifying vehicles, pedestrians, cyclists, and other road users in 3D space -- not just their 2D position in an image but their 3D location, dimensions, and orientation. From LiDAR, models like PointPillars and CenterPoint process point clouds directly. From cameras, BEVFormer and PETR create Bird's Eye View (BEV) representations that estimate 3D positions from 2D images.

Lane Detection and Road Understanding

Autonomous vehicles need to understand road geometry: where lanes are, how they curve, where intersections begin and end, and which lanes they can legally drive in. Modern systems combine camera-based lane detection with HD map data to achieve robust lane understanding even when markings are faded or occluded.

Traffic Sign and Signal Recognition

Reading speed limits, stop signs, yield signs, and traffic light states is critical. This is primarily a camera-based task, combining object detection (finding the sign/light) with classification (reading its content/state). The challenge is handling variations in design across regions, partially occluded signs, and unusual lighting conditions.

Free Space Detection

Determining which areas are drivable and which are occupied by obstacles, curbs, or off-road terrain. This is typically formulated as a semantic segmentation task, classifying every pixel or point as "drivable" or "not drivable."

Sensor Fusion: Combining Multiple Views

No single sensor is sufficient for safe autonomous driving. Sensor fusion combines information from cameras, LiDAR, and radar to create a more complete and reliable perception than any individual sensor could provide.

Early Fusion: Raw sensor data is combined before processing, allowing the model to learn cross-modal features directly. This is more computationally expensive but can capture subtle inter-sensor correlations.

Late Fusion: Each sensor is processed independently, and the detections are merged afterward. This is simpler to implement and debug but may miss synergies between sensor modalities.

BEV Fusion: The emerging dominant approach transforms all sensor inputs into a common Bird's Eye View representation and fuses them in this shared space. Models like BEVFusion have shown that this approach provides the best of both worlds -- the rich semantics of cameras and the precise geometry of LiDAR.

Prediction and Planning

Perception is only the first step. Once the vehicle understands its current environment, it must predict how the scene will evolve and plan its own actions accordingly.

Motion Prediction: Predicting where other road users will be in the next 3-8 seconds is essential for safe planning. Modern prediction models like QCNet and MTR generate multiple possible trajectories for each detected agent, accounting for the uncertainty in human behavior.

End-to-End Learning: An emerging trend is training a single neural network that takes sensor inputs and directly outputs driving actions (steering, acceleration, braking), bypassing the traditional modular pipeline. Tesla's FSD and projects like UniAD explore this direction, trading interpretability for potentially better overall performance.

Challenges That Remain

• Edge cases -- Unusual situations (construction zones, emergency vehicles, debris) that the system hasn't encountered in training
• Adverse weather -- Heavy rain, snow, fog, and direct sunlight degrade all sensors
• Long tail of safety -- Achieving 99% accuracy is insufficient when operating millions of miles; the rare 1% errors can be fatal
• Global diversity -- Driving cultures, road designs, and traffic patterns vary enormously worldwide
• Validation -- Proving that an AV is safe enough for unsupervised deployment requires billions of test miles