An adaptive approach for road boundary detection using 2D LIDAR sensor

Demir, Seyfullah; Ertop, Tayfun Efe; Koku, Ahmet Buğra; Konukseven, Erhan İlhan

doi:10.1109/mfi.2017.8170430

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…Among these, 3D LiDAR has been employed for road detection, due to its incredible capability of wide range point-cloud measurement. In this regard, Demir et al [9] proposed point-cloud based adaptive road detection method comprising of three key steps i.e. (i) Break point detection (ii) road region identification and (iii) driving zone determination.…”

Section: ) Multimodal Sensor Based Drivable Road Detectionmentioning

confidence: 99%

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Rasib¹,

Butt²,

Riaz³

et al. 2021

IEEE Access

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With the recent emergence of deep learning, computer vision-based applications have demonstrated better applicability in accomplishing driving tasks including drivable road region detection, lane keeping and steering control in self-driving vehicles. Till recently, numerous lane-marking detection based steering control and lane keeping methods have been proposed to perform autonomous driving on urban well-structured roads. But the matter of fact is that these methods are not feasible on roads where lane markings are not available or faded over time which makes drivable road region detection a crucial task. Also, it is highly difficult task to estimate the steering angle on such highly deteriorated roads using existing road detection and steering angle estimation methods. To the best of our knowledge, there is no standard benchmark available for drivable road region detection and steering angle estimation on unstructured roads. To this end, we present a large-scale dataset for drivable road detection, comprising of 15,000-pixel level high-quality fine annotations. Also, we present a novel end-to-end drivable road region detection and steering angle estimation method to ensure the autonomous driving on unstructured roads. The proposed method performs pixel-level segmentation to extract drivable road region and calculates the lane interception to estimate the steering angle of self-driving vehicles. Lastly, a comprehensive qualitative and quantitative analysis has been carried out to demonstrate the effectiveness of our proposed dataset, road detection and steering angle estimation method. Our benchmark is available at https://carl-dataset.github.io/index/.

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Section: ) Multimodal Sensor Based Drivable Road Detectionmentioning

confidence: 99%

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Rasib¹,

Butt²,

Riaz³

et al. 2021

IEEE Access

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“…There have been numerous studies in the past aimed at detecting curbs and road boundaries. These include single 2D-LiDAR sensor-based approaches used by [3]- [7]. In this approach, curb features are extracted and road boundaries use a fixed pitch angle, a series of depth and angle measurements for each LiDAR, and ego-vehicle movement information.…”

Section: A Related Workmentioning

confidence: 99%

CurbScan: Curb Detection and Tracking Using Multi-Sensor Fusion

Baek¹,

Tai²,

Bhat³

et al. 2020

Preprint

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Reliable curb detection is critical for safe autonomous driving in urban contexts. Curb detection and tracking are also useful in vehicle localization and path planning. Past work utilized a 3D LiDAR sensor to determine accurate distance information and the geometric attributes of curbs. However, such an approach requires dense point cloud data and is also vulnerable to false positives from obstacles present on both road and off-road areas. In this paper, we propose an approach to detect and track curbs by fusing together data from multiple sensors: sparse LiDAR data, a mono camera and low-cost ultrasonic sensors. The detection algorithm is based on a single 3D LiDAR and a mono camera sensor used to detect candidate curb features and it effectively removes false positives arising from surrounding static and moving obstacles. The detection accuracy of the tracking algorithm is boosted by using Kalman filter-based prediction and fusion with lateral distance information from low-cost ultrasonic sensors. We next propose a line-fitting algorithm that yields robust results for curb locations. Finally, we demonstrate the practical feasibility of our solution by testing in different road environments and evaluating our implementation in a real vehicle 1 . Our algorithm maintains over 90% accuracy within 4.5-22 meters and 0-14 meters for the KITTI dataset and our dataset respectively, and its average processing time per frame is approximately 10 ms on Intel i7 x86 and 100ms on NVIDIA Xavier board.

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“…In contrast, LiDAR sensors demonstrate robustness under various weather and lighting conditions and offer precise distance measurements [10]. Recently, 3D LiDAR has become one of the most important sensors for perceiving 3D environments in autonomous vehicles [11], [12].…”

Section: Introductionmentioning

confidence: 99%

SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

et al. 2022

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3D LiDAR has become an indispensable sensor in autonomous driving vehicles. In LiDAR-based 3D point cloud semantic segmentation, most voxel-based 3D segmentors cannot efficiently capture large amounts of context information, resulting in limited receptive fields and limiting their performance. To address this problem, a sparse voxel-based attention network is introduced for 3D LiDAR point cloud semantic segmentation, termed SVASeg, which captures large amounts of context information between voxels through sparse voxel-based multi-head attention (SMHA). The traditional multi-head attention cannot directly be applied to the non-empty sparse voxels. To this end, a hash table is built according to the incrementation of voxel coordinates to lookup the non-empty neighboring voxels of each sparse voxel. Then, the sparse voxels are grouped into different groups, and each group corresponds to a local region. Afterwards, position embedding, multi-head attention and feature fusion are performed for each group to capture and aggregate the context information. Based on the SMHA module, the SVASeg can directly operate on the non-empty voxels, maintaining a comparable computational overhead to the convolutional method. Extensive experimental results on the SemanticKITTI and nuScenes datasets show the superiority of SVASeg.

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An adaptive approach for road boundary detection using 2D LIDAR sensor

Cited by 9 publications

References 14 publications

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

CurbScan: Curb Detection and Tracking Using Multi-Sensor Fusion

SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

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