End-to-end Learning of Image based Lane-Change Decision
This addresses safety in lane changes for human drivers and autonomous vehicles, but it is incremental as it applies existing deep learning methods to a specific driving task.
The paper tackles lane-change decision-making by proposing an end-to-end deep learning framework, SLCAN, which directly classifies safety from rear view images without explicit object detection, achieving 96.98% accuracy on unseen roadways.
We propose an image based end-to-end learning framework that helps lane-change decisions for human drivers and autonomous vehicles. The proposed system, Safe Lane-Change Aid Network (SLCAN), trains a deep convolutional neural network to classify the status of adjacent lanes from rear view images acquired by cameras mounted on both sides of the vehicle. Rather than depending on any explicit object detection or tracking scheme, SLCAN reads the whole input image and directly decides whether initiation of the lane-change at the moment is safe or not. We collected and annotated 77,273 rear side view images to train and test SLCAN. Experimental results show that the proposed framework achieves 96.98% classification accuracy although the test images are from unseen roadways. We also visualize the saliency map to understand which part of image SLCAN looks at for correct decisions.