Inferring Road Boundaries Through and Despite Traffic

Suleymanov, Tarlan; Amayo, Paul; Newman, Paul

doi:10.1109/itsc.2018.8569570

Cited by 18 publications

(19 citation statements)

References 20 publications

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“…There are numerous approaches in semantic segmentation; we mainly compare our method to those pursuing a good tradeoff between high quality and computation, such as SegNet, ENet, and ERFNet. Moreover, to compare [22], we verified the model of inferring occluded road boundaries by replacing the decoder part of the model with a new one that is suitable for our task. The verified model is referred to as ORBNet in our work, which retained the encoder and employed a decoder similar to that in the DeepLabv3+ algorithm [6].…”

Section: Methodsmentioning

confidence: 99%

“…However, the presence of foreground objects makes it hard to obtain full road despite the occlusion. To infer the road boundaries despite the occlusion, Suleymanov et al [22] presented a convolutional neural network that contained intra-layer convolutions and produced outputs in a hybrid discrete-continuous form. Becattini et al [23] proposed a GAN-based (Generative Adversarial Network) semantic segmentation inpainting model to remove all dynamic objects from the scene and focus on understanding its static components (such as streets, sidewalks, and buildings) to get a comprehension of the static road scene.…”

Section: Related Workmentioning

confidence: 99%

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Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Wang

Yan

Zou

et al. 2019

Sensors

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The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene. To this end, we create a small but effective dataset based on the KITTI dataset named KITTI-OFRS (KITTI-occlusion-free road segmentation) dataset and propose a lightweight and efficient, fully convolutional neural network called OFRSNet (occlusion-free road segmentation network) that learns to predict occluded portions of the road in the semantic domain by looking around foreground objects and visible road layout. In particular, the global context module is used to build up the down-sampling and joint context up-sampling block in our network, which promotes the performance of the network. Moreover, a spatially-weighted cross-entropy loss is designed to significantly increases the accuracy of this task. Extensive experiments on different datasets verify the effectiveness of the proposed approach, and comparisons with current excellent methods show that the proposed method outperforms the baseline models by obtaining a better trade-off between accuracy and runtime, which makes our approach is able to be applied to autonomous vehicles in real-time.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Wang

Yan

Zou

et al. 2019

Sensors

View full text Add to dashboard Cite

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“…Works based on the proposed dataset include [15]- [17], where CNN-based deep-leaning models -Visible Road Boundary Detection (VRBD) and Occluded Road Boundary Inference (ORBI) -were proposed for detection and inference of visible and occluded road boundaries and were applied for scene understanding and lateral localisation problems.…”

Section: Related Workmentioning

confidence: 99%

“…Having data samples separated in two classes, visible and occluded, can be beneficial for operational safety [15]. To partition our raw road boundary masks into these two classes we trained the U-Net architecture [20] with raw masks.…”

Section: E Partitioningmentioning

confidence: 99%

The Oxford Road Boundaries Dataset

Suleymanov

Gadd

Martini

et al. 2021

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)

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“…Most camera-based methods address the problem using stereo cameras and 3D geometry to identify road boundaries in the scene [2], [3], [4], [5], [6], [7], [8]. In contrast, our previous work [9] used only a single monocular camera and deep convolutional neural networks for image processing to detect visible and occluded road boundaries. In this paper, we follow a similar machine-learning approach to infer both visible and occluded road boundaries, but we use LIDAR data as input.…”

Section: Introductionmentioning

confidence: 99%

Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR

Suleymanov

Kunze

Newman

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Road boundaries, or curbs, provide autonomous vehicles with essential information when interpreting road scenes and generating behaviour plans. Although curbs convey important information, they are difficult to detect in complex urban environments (in particular in comparison to other elements of the road such as traffic signs and road markings). These difficulties arise from occlusions by other traffic participants as well as changing lighting and/or weather conditions. Moreover, road boundaries have various shapes, colours and structures while motion planning algorithms require accurate and precise metric information in real-time to generate their plans.In this paper, we present a real-time LIDAR-based approach for accurate curb detection around the vehicle (360 degree). Our approach deals with both occlusions from traffic and changing environmental conditions. To this end, we project 3D LIDAR pointcloud data into 2D bird's-eye view images (akin to Inverse Perspective Mapping). These images are then processed by trained deep networks to infer both visible and occluded road boundaries. Finally, a post-processing step filters detected curb segments and tracks them over time. Experimental results demonstrate the effectiveness of the proposed approach on realworld driving data. Hence, we believe that our LIDAR-based approach provides an efficient and effective way to detect visible and occluded curbs around the vehicles in challenging driving scenarios.

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Inferring Road Boundaries Through and Despite Traffic

Cited by 18 publications

References 20 publications

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

The Oxford Road Boundaries Dataset

Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR

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