Vision-based ego-lane inference using High-Definition (HD) maps is essential in autonomous driving and advanced driver assistance systems. The traditional approach necessitates well-calibrated cameras, which confines variation of camera configuration, as the algorithm relies on intrinsic and extrinsic calibration. In this paper, we propose a learning-based ego-lane inference by directly estimating the ego-lane index from a single image. To enhance robust performance, our model incorporates the two-head structure inferring ego-lane in two perspectives simultaneously. Furthermore, we utilize an attention mechanism guided by vanishing point-and-line to adapt to changes in viewpoint without requiring accurate calibration. The high adaptability of our model was validated in diverse environments, devices, and camera mounting points and orientations.

High-definition (HD) maps are emerging as an essential tool for autonomous driving since they provide high-precision semantic information about the physical environment. To function as a reliable source of map information, HD maps must be constantly updated with changes that occur to the state of the road. In this paper, we propose a novel framework for HD map change detection that can be used to maintain an up-to-date HD map. More specifically, we design our HD map change detection algorithm based on deep metric learning, providing a unified framework that directly maps an input image to estimated probabilities of HD map changes. To reduce the discrepancy between input domains, i.e., camera image and HD map, we propose an effective learning scheme for metric space based on adversarial learning. Finally, we augment our framework with a pixel-level local change detector that specifies the region of changes in the image. We verify the effectiveness of our framework by evaluating it on a city-scale urban HD map dataset. Experimental results show that our method can robustly detect changes against noises due to dynamic objects and error in vehicle poses.

Maps are an increasingly important tool in our daily lives, yet their rich semantic content still largely depends on manual input. Motivated by the broad availability of geo-tagged street-view images, we propose a new task aiming to make the map update process more proactive. We focus on automatically detecting changes of Points of Interest (POIs), specifically stores or shops of any kind, based on visual input. Faced with the lack of an appropriate benchmark, we build and release a large dataset, captured in two large shopping centers, that comprises 33K geo-localized images and 578 POIs. We then design a generic approach that compares two image sets captured in the same venue at different times and outputs POI changes as a ranked list of map locations. In contrast to logo or franchise recognition approaches, our system does not depend on an external franchise database. It is instead inspired by recent deep metric learning approaches that learn a similarity function fit to the task at hand. We compare various loss functions to learn a metric aligned with the POI change detection goal, and report promising results.

We propose a deep learning algorithm for single-image depth estimation based on the Fourier frequency domain analysis. First, we develop a convolutional neural network structure and propose a new loss function, called depth-balanced Euclidean loss, to train the network reliably for a wide range of depths. Then, we generate multiple depth map candidates by cropping input images with various cropping ratios. In general, a cropped image with a small ratio yields depth details more faithfully, while that with a large ratio provides the overall depth distribution more reliably. To take advantage of these complementary properties, we combine the multiple candidates in the frequency domain. Experimental results demonstrate that proposed algorithm provides the state-of-art performance. Furthermore, through the frequency domain analysis, we validate the efficacy of the proposed algorithm in most frequency bands.