Search Results (5)

Detecting drivable areas is a fundamental task in autonomous driving systems. Although semantic segmentation networks have demonstrated strong performance in segmenting drivable regions, two key challenges persist. First, acquiring sufficient contextual information in complex road scenarios remains difficult, often leading to segmentation errors. Second, the coarseness of extracted features may degrade accuracy even when texture information is available in RGB images. To address these issues, we propose an enhanced DeepLabv3+ algorithm called Split Convolution Selective Attention Network (SCSANet), which incorporates the Adaptive Kernel (AK) and Split Convolution Attention (SCA) modules. AK adaptively adjusts the receptive field to accommodate varying road scenarios, while SCA improves boundary clarity by enhancing channel interaction. In addition, we employ surface normals to provide complementary geometric information, thereby strengthening the ability of the network to recognize drivable areas. To compensate for the lack of publicly available datasets for closed or semi-closed scenarios, we introduce XMUROAD, a new dataset of binocular disparity images. Experiments on the XMUROAD dataset demonstrate that the proposed architectural improvements yield an mIoU gain of 1.63% under the same RGB input, and the full pipeline with surface normal input achieves improvements of 1.55% to 2.59% in mF1 and 2.94% to 4.83% in mIoU over state-of-the-art methods. Experiments on the KITTI dataset further verify the generalization capability of SCSANet, with improvements of 1.58% in mF1 and 2.88% in mIoU over state-of-the-art methods. The proposed method provides a practical approach for accurate drivable area detection in closed and semi-closed mobile-robot scenarios. Full article

Drivable area (DA) detection in unstructured off-road environments remains challenging for unmanned ground vehicles (UGVs) due to limited field-of-view, persistent occlusions, and the inherent limitations of individual sensors. While existing fusion approaches combine aerial and ground perspectives, they often struggle with misaligned spatiotemporal viewpoints, dynamic environmental changes, and ineffective feature integration, particularly at intersections or under long-range occlusion. To address these issues, this paper proposes a cooperative air–ground perception framework based on multi-source data fusion. Our three-stage system first introduces DynCoANet, a semantic segmentation network incorporating directional strip convolution and connectivity attention to extract topologically consistent road structures from UAV imagery. Second, an enhanced particle filter with semantic road constraints and diversity-preserving resampling achieves robust cross-view localization between UAV maps and UGV LiDAR. Finally, a distance-adaptive fusion transformer (DAFT) dynamically fuses UAV semantic features with LiDAR BEV representations via confidence-guided cross-attention, balancing geometric precision and semantic richness according to spatial distance. Extensive evaluations demonstrate the effectiveness of our approach: on the DeepGlobe road extraction dataset, DynCoANet attains an IoU of 61.14%; cross-view localization on KITTI sequences reduces average position error by approximately 10%; and DA detection on OpenSatMap outperforms Grid-DATrNet by 8.42% in accuracy for large-scale regions (400 m × 400 m). Real-world experiments with a coordinated UAV-UGV platform confirm the framework’s robustness in occlusion-heavy and geometrically complex scenarios. This work provides a unified solution for reliable DA perception through tightly coupled cross-modal alignment and adaptive fusion. Full article

Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivable areas and consequently paralyzes the robots. This paper proposes a deep learning network model for realizing high-resolution segmentation of agricultural roads by leveraging contextual representations to augment road objectness. The backbone adopts HRNet to extract high-resolution road features in parallel at multiple scales. To strengthen the relationship between pixels and corresponding object regions, we use object-contextual representations (OCR) to augment the feature representations of pixels. Finally, a differentiable binarization (DB) decision head is used to perform threshold-adaptive segmentation for road boundaries. To quantify the performance of our method, we used an agricultural semi-structured road dataset and conducted experiments. The experimental results show that the mIoU reaches 97.85%, and the Boundary IoU achieves 90.88%. Both the segmentation accuracy and the boundary quality outperform the existing methods, which shows the tailored segmentation networks with contextual representations are beneficial to improving the detection accuracy of the semi-structured drivable areas in agricultural scene. Full article

Environment perception is one of the major issues in autonomous driving systems. In particular, effective and robust drivable road region detection still remains a challenge to be addressed for autonomous vehicles in multi-lane roads, intersections and unstructured road environments. In this paper, a computer vision and neural networks-based drivable road region detection approach is proposed for fixed-route autonomous vehicles (e.g., shuttles, buses and other vehicles operating on fixed routes), using a vehicle-mounted camera, route map and real-time vehicle location. The key idea of the proposed approach is to fuse an image with its corresponding local route map to obtain the map-fusion image (MFI) where the information of the image and route map act as complementary to each other. The information of the image can be utilized in road regions with rich features, while local route map acts as critical heuristics that enable robust drivable road region detection in areas without clear lane marking or borders. A neural network model constructed upon the Convolutional Neural Networks (CNNs), namely FCN-VGG16, is utilized to extract the drivable road region from the fused MFI. The proposed approach is validated using real-world driving scenario videos captured by an industrial camera mounted on a testing vehicle. Experiments demonstrate that the proposed approach outperforms the conventional approach which uses non-fused images in terms of detection accuracy and robustness, and it achieves desirable robustness against undesirable illumination conditions and pavement appearance, as well as projection and map-fusion errors. Full article

The generation of digital maps with lane-level resolution is rapidly becoming a necessity, as semi- or fully-autonomous driving vehicles are now commercially available. In this paper, we present a practical real-time working prototype for road lane detection using LiDAR data, which can be further extended to automatic lane-level map generation. Conventional lane detection methods are limited to simple road conditions and are not suitable for complex urban roads with various road signs on the ground. Given a 3D point cloud scanned by a 3D LiDAR sensor, we categorized the points of the drivable region and distinguished the points of the road signs on the ground. Then, we developed an expectation-maximization method to detect parallel lines and update the 3D line parameters in real time, as the probe vehicle equipped with the LiDAR sensor moved forward. The detected and recorded line parameters were integrated to build a lane-level digital map with the help of a GPS/INS sensor. The proposed system was tested to generate accurate lane-level maps of two complex urban routes. The experimental results showed that the proposed system was fast and practical in terms of effectively detecting road lines and generating lane-level maps. Full article