We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.
Traditional methods of camera-based drivable path estimation for road vehicles involve preprocessing steps to remove shadow and exposure artefacts @cite_26 @cite_11 , extraction of low-level road and lane features @cite_14 @cite_37 , fitting road and lane models to feature detections @cite_4 @cite_18 , and temporal fusion of road and lane hypotheses between successive frames @cite_7 @cite_20 . While effective in well-maintained road environments, these approaches suffer in the presence of occlusions, shadows and changing lighting conditions, unstructured roads, and areas with few or no markings @cite_19 . Robustness can be significantly increased by combining images with radar @cite_12 or LIDAR @cite_13 but at an increased sensor cost.
@cite_37 @cite_14 @cite_26 @cite_4 @cite_18 @cite_7 @cite_19 @cite_13 @cite_12 @cite_20 @cite_11
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Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented An essential functionality for advanced driver assistance systems (ADAS) is road segmentation, which directly supports ADAS applications like road departure warning and is an invaluable background segmentation stage for other functionalities as vehicle detection. Unfortunately, road segmentation is far from being trivial since the road is in an outdoor scenario imaged from a mobile platform. For instance, shadows are a relevant problem for segmentation. The usual approaches are ad hoc mechanisms, applied after an initial segmentation step, that try to recover road patches not included as segmented road for being in shadow. In this paper we argue that by using a different feature space to perform the segmentation we can minimize the problem of shadows from the very beginning. Rather than the usual segmentation in a color space we propose segmentation in a shadowless image which is computable in real---time using a color camera. The paper presents comparative results for both asphalted and non---asphalted roads, showing the benefits of the proposal in presence of shadows and vehicles. Particle filtering of boundary points is a robust way to estimate lanes. This paper introduces a new lane model in correspondence to this particle filter-based approach, which is flexible to detect all kinds of lanes. A modified version of an Euclidean distance transform is applied to an edge map of a road image from a birds-eye view to provide information for boundary point detection. An efficient lane tracking method is also discussed. The use of this distance transform exploits useful information in lane detection situations, and greatly facilitates the initialization of the particle filter, as well as lane tracking. Finally, the paper validates the algorithm with experimental evidence for lane detection and tracking. This paper describes a system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in real-time in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior. Our method is notable in several respects: it detects and estimates multiple travel lanes; it fuses asynchronous, heterogeneous sensor streams; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the road. We analyze the system's performance in the context of the 2007 DARPA Urban Challenge. With five cameras and thirteen lidars, our method was incorporated into a closed-loop controller to successfully guide an autonomous vehicle through a 90 km urban course at speeds up to 40 km h amidst moving traffic. Treats a problem arising in the design of intelligent vehicles: automated detection of lane and pavement boundaries using forward-looking optical and radar imaging sensors mounted on an automobile. In previous work, lane and pavement boundaries have always been located separately. This separate detection strategy is problematic in situations when either the optical or the radar image is too noisy. We propose a Bayesian multisensor image fusion method to solve our boundary detection problem. This method makes use of a deformable template model to globally describe the boundaries of interest. The optical and radar imaging processes are described with random field likelihoods. The multisensor fusion boundary detection problem is reformulated as a joint MAP estimation problem. However, the joint MAP estimate is intractable, as it involves the computation of a notoriously difficult normalization constant, also known as the partition function. Therefore, we settle for the so-called empirical MAP estimate, as an approximation to the true MAP estimate. Several experimental results are provided to demonstrate the efficacy of the empirical MAP estimation method in simultaneously detecting lane and pavement boundaries. Fusion of multi-modal images is not only of interest to the intelligent vehicles community, but to others as well, such as biomedicine, remote sensing, target recognition. The method presented in the paper is also applicable to image fusion problems in these other areas.