Neural networks play a critical role in enabling autonomous vehicles to perceive their environment, make decisions, and improve over time. They process raw sensor data (like camera images, lidar, and radar) to identify objects, predict behaviors, and plan safe paths. By learning patterns from vast datasets, neural networks allow vehicles to handle complex, real-world scenarios that are difficult to program with traditional rules-based systems.
One primary application is perception and object recognition. Convolutional neural networks (CNNs) analyze camera feeds to detect pedestrians, vehicles, traffic signs, and lane markings. For example, a CNN might process a 360-degree camera view to segment the road, identify a stop sign obscured by tree branches, or track a cyclist merging into traffic. Lidar and radar data are often processed using specialized architectures like PointNet or recurrent neural networks (RNNs) to handle sparse 3D point clouds and predict object trajectories. These models must operate in real time, balancing accuracy with computational efficiency to meet the strict latency requirements of driving systems.
Another key use is decision-making and control. Neural networks, particularly reinforcement learning (RL) models or transformer-based architectures, predict the behavior of other road users and plan safe maneuvers. For instance, a vehicle might use a trained RL policy to decide when to change lanes on a highway by evaluating the speed and intent of nearby cars. In urban environments, transformer models can process sequences of sensor data and historical driving patterns to anticipate sudden events, like a car running a red light. These networks often work alongside traditional control systems, which handle low-level tasks like maintaining speed or steering angle, ensuring a blend of adaptability and reliability.
Finally, neural networks enable continuous improvement through data. Autonomous vehicles collect terabytes of real-world driving data, which are used to retrain models and address edge cases. For example, if a vehicle encounters a rare scenario like a deer crossing a foggy road, the data can be added to training sets to improve future detection. Simulation environments also generate synthetic data to test how neural networks handle scenarios that are dangerous or impractical to replicate physically. Over-the-air updates deploy these improved models to vehicle fleets, creating a feedback loop that enhances safety and performance without requiring hardware changes. This iterative process ensures that neural networks evolve alongside real-world conditions, maintaining robust autonomy over time.