Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention (1703.10631v1)

Abstract: Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here we explore the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). Our approach is two-stage. In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 hours of driving. We first show that training with attention does not degrade the performance of the end-to-end network. Then we show that the network causally cues on a variety of features that are used by humans while driving.

Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

The paper "Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention" by Jinkyu Kim and John Canny addresses key challenges in the deployment of deep neural networks for autonomous vehicle control: interpretability and causality of model outputs. As neural networks become central to self-driving technologies, understanding the decision-making process of these models is indispensable for stakeholders such as passengers, developers, insurers, and regulatory bodies.

Research Objective

The paper focuses on augmenting self-driving car models with interpretable visual explanations through the use of attention mechanisms. It asserts that these explanations must not sacrifice model performance while providing valuable insights into the neural networks' decision parameters. The proposed solution involves a two-stage model where a visual attention mechanism is first used to train a convolutional neural network (CNN) for steering angle predictions. A subsequent filtering process refines this attention to identify causal relationships rather than mere correlations in the visual data.

Methodology

1. Encoder Phase: The method begins by processing input images through a CNN without any max-pooling layers, preserving the spatial integrity of features. This output forms a convolutional feature cube which becomes the input for later processing in the attention mechanism.
2. Coarse-Grained Decoder with Visual Attention: Attention heat maps are produced which highlight potential areas of interest in the input image. These regions are weighted based on their projected importance for the prediction task. This attention mechanism ensures that the model's focal points can be visualized, indicating areas of the image that most influence the steering output.
3. Fine-Grained Decoder with Causal Filtering: The attention heat map is further refined to emphasize only those regions with a causal influence on the output. By removing attention assigned to spurious features that do not affect steering outcomes, the model can provide more accurate explanations of its decision-making process. This causal filtering step involves post-processing the attention blobs detected by clustering and evaluating their causal effect on the prediction.

Results and Implications

The authors demonstrate the efficacy of their approach on datasets containing over 1.2 million frames (16 hours of driving) and find that the attention-based model performs on par with or better than traditional end-to-end models in terms of mean absolute error (MAE), indicating that the integration of attention does not degrade control accuracy. Moreover, the attention maps produced are both accurate and interpretable, validating human-relevant features such as lane markings and traffic. The causal filtering process effectively reduces spurious attention by about 60%, suggesting a substantial improvement in explanation complexity.

The implications of this research span both practical and theoretical domains. Practically, it suggests a path to creating more robust and reliable self-driving systems that can offer understandable and traceable decision processes. Theoretically, it adds depth to our understanding of causal inference in machine learning models, particularly in convolutional architectures associated with spatial feature extraction.

Future Directions

Future work could extend this research by integrating more sophisticated temporal modeling components, such as multi-layered LSTMs, to enhance the capture of temporal dynamics in driving scenarios. Another potential research avenue could investigate the application of this method to varying driving conditions and environments, such as adverse weather or complex urban settings, to determine its generalizability and robustness.

In summary, this work provides significant insight into the integration of interpretable machine learning techniques within the autonomous driving domain, marking a significant step toward more transparent and trustworthy AI systems in safety-critical applications.