End to End Learning for Self-Driving Cars (1604.07316v1)

Abstract: We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads. The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. Compared to explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end-to-end system optimizes all processing steps simultaneously. We argue that this will eventually lead to better performance and smaller systems. Better performance will result because the internal components self-optimize to maximize overall system performance, instead of optimizing human-selected intermediate criteria, e.g., lane detection. Such criteria understandably are selected for ease of human interpretation which doesn't automatically guarantee maximum system performance. Smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps. We used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVE(TM) PX self-driving car computer also running Torch 7 for determining where to drive. The system operates at 30 frames per second (FPS).

• The paper introduces an end-to-end CNN model that directly maps raw images to steering commands, streamlining the traditional modular self-driving architecture.
• The paper demonstrates the model's effectiveness using 72 hours of driving data with robust training techniques and data augmentation for diverse road conditions.
• The paper suggests future research directions, including multi-sensor fusion and transfer learning, to enhance safety, generalization, and performance.

End to End Learning for Self-Driving Cars

The paper "End to End Learning for Self-Driving Cars" by Bojarski et al. presents a seminal exploration of autonomous vehicle navigation using an end-to-end learning approach. Authored by researchers at NVIDIA Corporation, the paper elucidates techniques that leverage convolutional neural networks (CNNs) to process raw image data from a single front-facing camera and output steering commands directly.

Methodology

The approach taken by Bojarski et al. diverges from traditional modular systems, which typically decompose the self-driving task into several stages including perception, path planning, and control. Instead, the authors propose a complete end-to-end system whereby CNNs learn to map input images directly to steering angles. This methodology encompasses several key components:

1. Data Collection: The authors employed a data-driven approach, collecting driving data from human drivers under various road and weather conditions. The dataset includes 72 hours of driving data, with overland routes traversing highways, suburban areas, and country roads.
2. Network Architecture: The proposed CNN consists of 9 layers, including normalization, convolutional, and fully connected layers. This architecture is designed to handle the complexity of visual driving cues while maintaining computational efficiency.
3. Training: The network was trained using mean squared error (MSE) between the human driver commands and the network's predictions. Notably, data augmentation techniques, such as adding random perturbations to images, were employed to promote robustness.

Experimental Results

The paper provides a detailed account of their experimental results, demonstrating the efficacy of the end-to-end approach:

• Performance: The trained model was capable of driving on a diverse set of roads without requiring explicit road markings or detailed maps. The system showed proficiency in navigating various road types and conditions, including unpaved roads and roads with poor visibility.
• Generalization: The model exhibited strong generalization abilities, effectively performing on roads not seen during the training phase.

Implications and Future Work

The implications of this research are significant for both theoretical and practical advancements in autonomous vehicle technology:

• Simplicity and Efficiency: The end-to-end learning model simplifies the pipeline for self-driving systems by eliminating the need for hand-crafted features and complex processing modules. This can lead to more efficient implementations and reduce the computational overhead associated with traditional systems.
• Safety and Robustness: Future research could explore the integration of additional sensors and modalities to enhance the system's safety and robustness. For instance, combining camera data with LiDAR or RADAR inputs could help in handling edge cases and improving overall reliability.
• Transfer Learning and Domain Adaptation: Another promising direction is leveraging transfer learning and domain adaptation techniques to extend the capability of the model to novel environments and driving conditions. This could minimize the need for extensive data collection campaigns for each new operational domain.

Conclusion

In summary, "End to End Learning for Self-Driving Cars" by Bojarski et al. represents a substantial contribution to the field of autonomous driving by advocating for an integrated learning approach to vehicle control. The demonstration of a CNN-based system achieving competent navigation using raw image data underscores the potential of deep learning in transforming autonomous driving technologies. Further work in this domain is poised to build on these foundational insights, refining the models and expanding their applicability to realistic and varied driving scenarios.