Autonomous driving is a rapidly advancing field that seeks to make vehicles safer and more efficient on the road. Recent studies have explored the use of end-to-end approaches in autonomous driving, which involve training AI models directly on real-world data rather than relying on predefined rules and simulations. However, these methods face challenges such as dealing with long-tail problems, limited expert demonstrations, and curse of dimensionality.
To overcome these challenges, researchers have proposed using diffusion models to generate realistic simulation scenarios for training end-to-end autonomous driving tasks. These models can automatically generate scenes similar to those that the AI model has failed in, allowing for more extensive and diverse training data. By training on these simulated scenes, the AI model can learn to better predict its behavior in real-world situations.
Another challenge in end-to-end autonomous driving research is the limited quality and quantity of expert demonstrations available. The accuracy of the learned reward function depends heavily on the quality and diversity of the data used to train it. In complex high-dimensional state spaces, even a small amount of data can result in an inaccurate representation of the true underlying reward structure.
Lastly, many IRL algorithms involve computationally expensive optimization problems that can be challenging when dealing with large state spaces or complex reward functions. To overcome these limitations, researchers are exploring new methods and techniques to simplify the process while still capturing the essence of the article without oversimplifying.
Minimizing L2 Loss for Autonomous Driving: Exploring Data Augmentation and Regularization Techniques
