Towards End-to-End Lane Detection: an Instance Segmentation Approach

Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed "bird's-eye view" transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results

Enhanced free space detection in multiple lanes based on single CNN with scene identification

Many systems for autonomous vehicles' navigation rely on lane detection. Traditional algorithms usually estimate only the position of the lanes on the road, but an autonomous control system may also need to know if a lane marking can be crossed or not, and what portion of space inside the lane is free from obstacles, to make safer control decisions. On the other hand, free space detection algorithms only detect navigable areas, without information about lanes. State-of-the-art algorithms use CNNs for both tasks, with significant consumption of computing resources. We propose a novel approach that estimates the free space inside each lane, with a single CNN. Additionally, adding only a small requirement concerning GPU RAM, we infer the road type, that will be useful for path planning. To achieve this result, we train a multi-task CNN. Then, we further elaborate the output of the network, to extract polygons that can be effectively used in navigation control. Finally, we provide a computationally efficient implementation, based on ROS, that can be executed in real time. Our code and trained models are available online.Comment: Will appear in the 2019 IEEE Intelligent Vehicles Symposium (IV 2019

The Multiwavelength Survey By Yale-Chile (MUSYC) Wide K-Band Imaging, Photometric Catalogs, Clustering, And Physical Properties Of Galaxies At Z Similar To 2

We present K-band imaging of two similar to 30' x 30' fields covered by the Multiwavelength Survey by Yale-Chile (MUSYC) Wide NIR Survey. The SDSS 1030+05 and Cast 1255 fields were imaged with the Infrared Side Port Imager (ISPI) on the 4 m Blanco telescope at the Cerro Tololo Inter-American Observatory (CTIO) to a 5 sigma point-source limiting depth of K similar to 20 (Vega). Combining these data with the MUSYC optical UBVRIz imaging, we created multiband K-selected source catalogs for both fields. These catalogs, together with the MUSYC K-band catalog of the Extended Chandra Deep Field South (ECDF-S) field, were used to select K 20 BzK galaxies over an area of 0.71 deg(2). This is the largest area ever surveyed for BzK galaxies. We present number counts, redshift distributions, and stellar masses for our sample of 3261 BzK galaxies (2502 star-forming [sBzK] and 759 passively evolving [pBzK]), as well as reddening and star formation rate estimates for the star-forming BzK systems. We also present two-point angular correlation functions and spatial correlation lengths for both sBzK and pBzK galaxies and show that previous estimates of the correlation function of these galaxies were affected by cosmic variance due to the small areas surveyed. We have measured correlation lengths r(0) of 8.89 +/- 2.03 and 10.82 +/- 1.72 Mpc for sBzK and pBzK galaxies, respectively. This is the first reported measurement of the spatial correlation function of passive BzK galaxies. In the Lambda CDM scenario of galaxy formation, these correlation lengths at z similar to 2 translate into minimum masses of similar to 4 x 10(12) and similar to 9 x 10(12) M(circle dot) for the dark matter halos hosting sBzK and pBzK galaxies, respectively. The clustering properties of the galaxies in our sample are consistent with their being the descendants of bright Lyman break galaxies at z similar to 3, and the progenitors of present-day > 1L* galaxies.Astronom

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction