Deep learning and artificial intelligence (AI) have influenced autonomous vehicle technology significantly over the past ten years. Self-driving vehicles rely on advanced decision-making systems that read data from sensors to navigate autonomously. As AI grows, these systems have increased in complexity, with modules dedicated to perception, path planning, behavior arbitration, and motion control. Deep learning plays a critical role within these subsystems, providing effective data processing and decision-making capacity.
CNNs (Convolutional Neural Networks), essential deep learning tools, are used to process spatial data like images, overcoming the limitations of traditional handcrafted features. Mimicking the mammalian visual cortex, CNNs can detect image features to aid object recognition. RNNs (Recurrent Neural Networks), on the other hand, process temporal sequences like video streams or text. They possess a feedback loop that makes them suitable for capturing temporal dependencies. Long Short-Term Memory (LSTM) networks overcome the vanishing gradient problem of RNNs, allowing modelling of long-term dependencies in sequences.
A key deep learning method for self-driving cars is DRL (Deep Reinforcement Learning), where an agent navigates its environment based on sensory data, taking actions to gain future rewards. DRL models like Deep Q-Networks (DQN) estimate optimal action policies through the training of neural networks to approximate maximum expected future rewards. Despite this technology’s potential, challenges remain in applying DRL to real-world driving situations.
Self-driving cars heavily depend on deep learning to perceive their surroundings. Using cameras and LiDAR sensors, deep learning aids in object detection, recognition, and scene understanding. Despite the advantages of each sensor, there’s ongoing debate about their respective reliability and the potential for integration.
The safety of deep learning in autonomous driving is a complex issue. To ensure safety, potential failures, system context, and definitions of safe behavior need to be understood. Although conventional standards like ISO 26262 exist, the application of these standards to deep learning systems is challenging. Deep learning presents unique risks, therefore, upcoming tailored safety standards need to be developed.
Deep learning and AI have yet to fully address important challenges in autonomous driving. These include:
Perception: Future systems should aim for improved detail recognition and better data integration from cameras and LiDAR.
Short- to middle-term reasoning: AI’s role is crucial in path planning activities, especially in local trajectory estimation.
Availability of training data: Deep learning’s effectiveness heavily relies on high-quality data.
Learning corner cases: Improved generalization power is needed to handle rare driving scenarios.
Learning-based control methods: Deep learning can learn control parameters adaptively, improving autonomous vehicle performance.
Functional safety: There are challenges integrating deep learning into safety-critical systems, such as meeting safety standards and ensuring explainability, stability, and robustness of neural networks.
Real-time computing and communication: Advances in hardware and communication networks are required to meet the demands of processing large volumes of sensor data in real time.
Despite the progress made in autonomous vehicle technology through deep learning and AI, there are still unresolved issues. However, the powerful combination of these technologies holds the promise of a safer, more efficient future in transportation.
