Driving Datasets

Driving datasets are crucial for developing and evaluating autonomous driving systems, aiming to provide realistic and diverse representations of real-world driving scenarios. Current research focuses on creating datasets with high-fidelity sensor data (including cameras, LiDAR, and even event-based cameras), detailed annotations of events and driver behavior, and diverse weather and traffic conditions, often employing simulation to augment real-world data. These datasets are used to train and benchmark various models, including vision-language models, convolutional and spiking neural networks, and deep reinforcement learning frameworks, improving the safety and robustness of autonomous vehicles. The availability of high-quality, comprehensive driving datasets is essential for advancing the field and ensuring the safe deployment of self-driving technology.