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Journal articles on the topic 'Road scene understanding'

Author: Grafiati
Published: 10 March 2023
Last updated: 31 July 2025

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1

Zhou, Wujie, Sijia Lv, Qiuping Jiang, and Lu Yu. "Deep Road Scene Understanding." IEEE Signal Processing Letters 26, no. 4 (2019): 587–91. http://dx.doi.org/10.1109/lsp.2019.2896793.

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Huang, Wenqi, Fuzheng Zhang, Aidong Xu, Huajun Chen, and Peng Li. "Fusion-based holistic road scene understanding." Journal of Engineering 2018, no. 16 (2018): 1623–28. http://dx.doi.org/10.1049/joe.2018.8319.

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Naudé, August J., and Herman C. Myburgh. "Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention." Sensors 23, no. 17 (2023): 7355. http://dx.doi.org/10.3390/s23177355.

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Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demon
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Wang, Chao, Huan Wang, Rui Li Wang, and Chun Xia Zhao. "Robust Zebra-Crossing Detection for Autonomous Land Vehicles and Driving Assistance Systems." Applied Mechanics and Materials 556-562 (May 2014): 2732–39. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2732.

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Road scene understanding is critical for driving assistance systems and autonomous land vehicles. The main function of road scene understanding is robustly detecting useful visual objects existing in a road scene. A zebra crossing is a typical pedestrian crossing used in many countries around the world. When detecting a zebra crossing, an autonomous lane vehicle is normally required to automatically slow down its speed and to trigger a path-planning strategy for passing the zebra crossing. Also, most of driving assistance systems can send an early-warning signal to remind drivers to be more ca
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Elhenawy, Mohammed, Huthaifa I. Ashqar, Andry Rakotonirainy, Taqwa I. Alhadidi, Ahmed Jaber, and Mohammad Abu Tami. "Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding." Electronics 14, no. 7 (2025): 1282. https://doi.org/10.3390/electronics14071282.

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Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language–Image Pretraining (CLIP) models, which can be optimized for real-time deployment on edge devices. The proposed system outperforms state-of-the-art in-context learning methods, including the zero-shot capabilities of GPT-4o, particularly in complex scenarios. By conducting frame-level analyses o
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Xu, Zhaosheng, Zhongming Liao, Xiaoyong Xiao, Suzana Ahmad, Norizan Mat Diah, and Azlan Ismail. "Target image detection algorithm of complex road scene based on improved multi-scale adaptive feature fusion technology." International Journal for Simulation and Multidisciplinary Design Optimization 16 (2025): 6. https://doi.org/10.1051/smdo/2025004.

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Understanding road scenes is crucial to the safe driving of autonomous vehicles, and object detection in road scenes is necessary to develop driving assistance systems. Current object detection algorithms are not very good at handling complex road scenes, and public datasets do not always adequately represent city traffic. Using Improved Multi-Scale Adaptive Feature Fusion Technology (IMSAFFT), this work suggests a real-time traffic information identification method to fix the issues of low detection accuracy of road scenes and high false detection rates in panoramic video images. In addition,
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Liu, Huajun, Cailing Wang, and Jingyu Yang. "Vanishing points estimation and road scene understanding based on Bayesian posterior probability." Industrial Robot: An International Journal 43, no. 1 (2016): 12–21. http://dx.doi.org/10.1108/ir-05-2015-0095.

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Purpose – This paper aims to present a novel scheme of multiple vanishing points (VPs) estimation and corresponding lanes identification. Design/methodology/approach – The scheme proposed here includes two main stages: VPs estimation and lane identification. VPs estimation based on vanishing direction hypothesis and Bayesian posterior probability estimation in the image Hough space is a foremost contribution, and then VPs are estimated through an optimal objective function. In lane identification stage, the selected linear samples supervised by estimated VPs are clustered based on the gradient
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Deepa Mane, Et al. "A Review on Cross Weather Traffic Scene Understanding Using Transfer Learning for Intelligent Transport System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 2027–38. http://dx.doi.org/10.17762/ijritcc.v11i10.8886.

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Intelligent transport systems (ITS) have revolutionized the transportation industry by integrating cutting-edge technologies to enhance road safety, reduce traffic congestion and optimize the transportation network. Scene understanding is a critical component of ITS that enables real-time decision-making by interpreting the environment's contextual information. However, achieving accurate scene understanding requires vast amounts of labeled data, which can be costly and time-consuming. It is quite challenging to Understand traffic scene captured from vehicle mounted cameras. In recent times, t
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Yasrab, Robail. "ECRU: An Encoder-Decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding." Journal of Imaging 4, no. 10 (2018): 116. http://dx.doi.org/10.3390/jimaging4100116.

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This research presents the idea of a novel fully-Convolutional Neural Network (CNN)-based model for probabilistic pixel-wise segmentation, titled Encoder-decoder-based CNN for Road-Scene Understanding (ECRU). Lately, scene understanding has become an evolving research area, and semantic segmentation is the most recent method for visual recognition. Among vision-based smart systems, the driving assistance system turns out to be a much preferred research topic. The proposed model is an encoder-decoder that performs pixel-wise class predictions. The encoder network is composed of a VGG-19 layer m
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Qin, Yuting, Yuren Chen, and Kunhui Lin. "Quantifying the Effects of Visual Road Information on Drivers’ Speed Choices to Promote Self-Explaining Roads." International Journal of Environmental Research and Public Health 17, no. 7 (2020): 2437. http://dx.doi.org/10.3390/ijerph17072437.

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Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as “self-explaining roads” (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers’ speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitt
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Jeong, Jinhan, Yook Hyun Yoon, and Jahng Hyon Park. "Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation." Sensors 20, no. 9 (2020): 2457. http://dx.doi.org/10.3390/s20092457.

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Lane detection and tracking in a complex road environment is one of the most important research areas in highly automated driving systems. Studies on lane detection cover a variety of difficulties, such as shadowy situations, dimmed lane painting, and obstacles that prohibit lane feature detection. There are several hard cases in which lane candidate features are not easily extracted from image frames captured by a driving vehicle. We have carefully selected typical scenarios in which the extraction of lane candidate features can be easily corrupted by road vehicles and road markers that lead
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Topfer, Daniel, Jens Spehr, Jan Effertz, and Christoph Stiller. "Efficient Road Scene Understanding for Intelligent Vehicles Using Compositional Hierarchical Models." IEEE Transactions on Intelligent Transportation Systems 16, no. 1 (2015): 441–51. http://dx.doi.org/10.1109/tits.2014.2354243.

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Song, Chuanwang, Yinghao Ma, Yuanteng Zhou, et al. "Road Scene Semantic Segmentation Based on MPNet." Electronics 14, no. 13 (2025): 2565. https://doi.org/10.3390/electronics14132565.

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The increasing demands for high-precision semantic segmentation in applications such as autonomous driving, unmanned aerial vehicles, and robotics has made improving segmentation accuracy a major research focus. In this paper, we propose MPNet, (multi-scale progressive network) a novel semantic segmentation model based on the DeepLabV3+ architecture. First, a lightweight MobileNetV2 was employed as the backbone network, and a new multi-scale feature fusion structure was constructed by integrating the backbone with the centralized feature pyramid network (CFPNet). Then, based on the ASPP module
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Guo, Jinghua, Jingyao Wang, Huinian Wang, Baoping Xiao, Zhifei He, and Lubin Li. "Research on Road Scene Understanding of Autonomous Vehicles Based on Multi-Task Learning." Sensors 23, no. 13 (2023): 6238. http://dx.doi.org/10.3390/s23136238.

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Road scene understanding is crucial to the safe driving of autonomous vehicles. Comprehensive road scene understanding requires a visual perception system to deal with a large number of tasks at the same time, which needs a perception model with a small size, fast speed, and high accuracy. As multi-task learning has evident advantages in performance and computational resources, in this paper, a multi-task model YOLO-Object, Drivable Area, and Lane Line Detection (YOLO-ODL) based on hard parameter sharing is proposed to realize joint and efficient detection of traffic objects, drivable areas, a
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Sun, Jee-Young, Seung-Won Jung, and Sung-Jea Ko. "Lightweight Prediction and Boundary Attention-Based Semantic Segmentation for Road Scene Understanding." IEEE Access 8 (2020): 108449–60. http://dx.doi.org/10.1109/access.2020.3001679.

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Wang, Huan, YangYang Hou, and Mingwu Ren. "A Shape-Aware Road Detection Method for Aerial Images." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (2017): 1750009. http://dx.doi.org/10.1142/s0218001417500094.

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Road detection in aerial images is a crucial technique for visual navigation and scene understanding in relation to unmanned aerial vehicles (UAVs). A shape-aware road detection method for aerial images is proposed in this paper. It first employs the stroke width transform (SWT) and a geodesic distance based superpixel clustering to generate proposal regions. Then, a shape classification is responsible for selecting all potential road segments from the proposal regions which appear to be long and with consistent width. All road segments selected are clustered into several groups based on width
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Wang, Haixia, Yehao Sun, Zhiguo Zhang, Xiao Lu, and Chunyang Sheng. "Depth estimation for a road scene using a monocular image sequence based on fully convolutional neural network." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092530. http://dx.doi.org/10.1177/1729881420925305.

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An advanced driving assistant system is one of the most popular topics nowadays, and depth estimation is an important cue for advanced driving assistant system. Depth prediction is a key problem in understanding the geometry of a road scene for advanced driving assistant system. In comparison to other depth estimation methods using stereo depth perception, determining depth relation using a monocular camera is considerably challenging. In this article, a fully convolutional neural network with skip connection based on a monocular video sequence is proposed. With the integration framework that
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Deng, Yanzi, Zhaoyang Lu, and Jing Li. "Coarse-to-fine road scene segmentation via hierarchical graphical models." International Journal of Advanced Robotic Systems 16, no. 2 (2019): 172988141983116. http://dx.doi.org/10.1177/1729881419831163.

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The road scene segmentation is an important problem which is helpful for a higher level of the scene understanding. This article presents a novel approach for image semantic segmentation of road scenes via a hierarchical graph-based inference. A deep encoder–decoder network is first applied for a fast pixel-wise classification. Then, hierarchical graph-based inference is performed to get an accurate segmentation result. In the inference process, all the object classes are grouped into fewer categories which contains at least one class. The category labels are assigned to image superpixels usin
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19

Billones, Robert Kerwin C., Argel A. Bandala, Laurence A. Gan Lim, Edwin Sybingco, Alexis M. Fillone, and Elmer P. Dadios. "Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (2018): 704–10. http://dx.doi.org/10.20965/jaciii.2018.p0704.

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This paper presents the development of a vision-based system for microscopic road traffic scene analysis and understanding using computer vision and computational intelligence techniques. The traffic flow model is calibrated using the information obtained from the road-side cameras. It aims to demonstrate an understanding of different levels of traffic scene analysis from simple detection, tracking, and classification of traffic agents to a higher level of vehicular and pedestrian dynamics, traffic congestion build-up, and multi-agent interactions. The study used a video dataset suitable for a
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Qusay Sellat and Kanagachidambaresan Ramasubramanian. "Application of Deep Neural Network Structures in Semantic Segmentation for Road Scene Understanding." Optical Memory and Neural Networks 32, no. 2 (2023): 137–46. http://dx.doi.org/10.3103/s1060992x23020108.

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21

Trabelsi, Rim, Redouane Khemmar, Benoit Decoux, Jean-Yves Ertaud, and Rémi Butteau. "Recent Advances in Vision-Based On-Road Behaviors Understanding: A Critical Survey." Sensors 22, no. 7 (2022): 2654. http://dx.doi.org/10.3390/s22072654.

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On-road behavior analysis is a crucial and challenging problem in the autonomous driving vision-based area. Several endeavors have been proposed to deal with different related tasks and it has gained wide attention recently. Much of the excitement about on-road behavior understanding has been the labor of advancement witnessed in the fields of computer vision, machine, and deep learning. Remarkable achievements have been made in the Road Behavior Understanding area over the last years. This paper reviews 100+ papers of on-road behavior analysis related work in the light of the milestones achie
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Yu, Chunlei, Veronique Cherfaoui, Philippe Bonnifait, and Dian-ge Yang. "Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids." Sensors 20, no. 2 (2020): 352. http://dx.doi.org/10.3390/s20020352.

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Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space. This paper presents a grid-based evidential approach for modeling semantic road space by taking advantage of a prior map that contains lane-level information. Road rules are encoded in the grid for s
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Lisauskas, Bartas, and Rytis Maskeliunas. "Efficient Transformer-Based Road Scene Segmentation Approach with Attention-Guided Decoding for Memory-Constrained Systems." Machines 13, no. 6 (2025): 466. https://doi.org/10.3390/machines13060466.

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Accurate object detection and an understanding of the surroundings are key requirements when applying computer vision systems in the automotive or robotics industries, namely with autonomous vehicles or self-driving robots. A precise understanding of road users or obstacles is essential to avoid potential accidents. Due to the presence of many objects and the diversity of the environment, the segmentation of the road scene remains a challenging task. In our approach, a Transformer-based backbone is employed for robust feature extraction in the encoder module. In addition, we have developed a c
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Kiy, K. I., and D. A. Anokhin. "A NEW TECHNIQUE FOR OBJECT DETECTION AND TRACKING AND ITS APPLICATION TO ANALYSIS OF ROAD SCENE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-2/W1-2021 (April 15, 2021): 119–24. http://dx.doi.org/10.5194/isprs-archives-xliv-2-w1-2021-119-2021.

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Abstract. In this paper, a new technique for real-time object detection and tracking is presented. This technique is based on the geometrized histograms method (GHM) for segmenting and describing color images (frames of video sequences) and on the facilities for global image analysis provided by this method. Basic elements of the technique that make it possible to solve image understanding problems almost without using the pixel arrays of images are introduced and discussed.A real-time parallel software implementation of the developed technique is briefly discussed. This technique is applied t
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Maaz, Abdul Basith, Abdul Owaise, Adnan Shariff, Mohammed Hamza Rahamathulla, and Naheem MR. "Advancements in Deep Learning for Autonomous Driving in Indian Road Conditions." Journal of Intelligent Data Analysis and Computational Statistics 1, no. 1 (2024): 31–37. http://dx.doi.org/10.46610/joidacs.2024.v01i01.004.

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Recent advancements in deep learning techniques tailored for autonomous driving in Indian road conditions are crucial for revolutionizing transportation systems. Indian roads present unique challenges, including unpredictable traffic patterns, diverse road infrastructures, and challenging weather conditions. Deep learning is pivotal in addressing these challenges, focusing on perception, decision-making, and control. Various architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL), are analyzed for their efficacy in
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Wang, Kewei, Fuwu Yan, Bin Zou, Luqi Tang, Quan Yuan, and Chen Lv. "Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles." Sensors 19, no. 21 (2019): 4711. http://dx.doi.org/10.3390/s19214711.

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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 OFRSNe
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Tapiro, Hagai, Avinoam Borowsky, Tal Oron-Gilad, and Yisrael Parmet. "Where do older pedestrians glance before deciding to cross a simulated two-lane road? A pedestrian simulator paradigm." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 11–15. http://dx.doi.org/10.1177/1541931213601003.

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Knowing where to older pedestrians allocate their glances before deciding to cross the road can contribute to understanding the causes that lead them to make bad road crossing decisions. Research on older drivers suggest that they are over involved in crashes that involve navigation through intersections mainly because they focused on their travel path and rarely on other areas in the scene from where a hazard might appear. Yet, it is less known how older pedestrians spread their attention on their expected travel path. Eleven older participants (over 65) and ten younger adults were asked to m
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Hylander, Johan, Britt-Inger Saveman, and Lina Gyllencreutz. "A Sense of Trust, the Norwegian Way of Improving Medical On-Scene Managing Major Tunnel Incidents: An Interview Study." Prehospital and Disaster Medicine 34, s1 (2019): s166. http://dx.doi.org/10.1017/s1049023x19003790.

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Introduction:Norway is a country with many road tunnels and therefore also has experience with rescue operations in tunnel environments. Major incidents always challenge involved emergency services’ management skills. Oslo, Norway has a specially trained medical on-scene commander, a function already existing in police and rescue service. Intra-agency communication and management of personnel are essential factors for a successful rescue effort.Aim:To investigate the medical management provided by the specially trained Norwegian medical on-scene commander in relation to tunnel incidents.Method
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Ninan, Stephen, and Sivakumar Rathinam. "Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap." Sensors 23, no. 18 (2023): 7915. http://dx.doi.org/10.3390/s23187915.

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Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot’s understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach, however, is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMap have proven to be a useful alternative in these situati
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Cai, Ying Feng, Hai Wang, and Wei Gong Zhang. "Learning Patterns of Motion Trajectories Using Real-Time Tracking." Advanced Materials Research 403-408 (November 2011): 2768–71. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2768.

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The understanding and description of behaviors for road vehicles is a hot topic of intelligent visual surveillance system. Trajectory analysis is one of the basic problems in behavior understanding, from which anomalies can be detected and also accidents can be predicted. In this paper, we proposed a hierarchical self-organizing neural network model to learn trajectory distribution pattern and a probability model for accident recognition. Sample data including motion trajectories are first get by real-time vehicle tracking. The self-organizing neural network algorithm is then applied to learn
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Yang, Yuanfeng, Husheng Dong, Gang Liu, Liang Zhang, and Lin Li. "Cross-Domain Traffic Scene Understanding by Integrating Deep Learning and Topic Model." Computational Intelligence and Neuroscience 2022 (March 18, 2022): 1–15. http://dx.doi.org/10.1155/2022/8884669.

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Understanding cross-domain traffic scenarios from multicamera surveillance network is important for environmental perception. Most of existing methods select the source domain which is most similar to the target domain by comparing entire domains for cross-domain similarity and then transferring the motion model learned in the source domain to the target domain. The cross-domain similarity between overall different scenarios with similar local layouts is usually not utilized to improve any automatic surveillance tasks. However, these local commonalities, which may be shared across multiple tra
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N.Dhamanam, M.Kathirvelu Dr., and GovindaRao T. "Review of Environment Perception for Intelligent Vehicles." International Journal of Engineering and Management Research 9, no. 2 (2019): 13–17. https://doi.org/10.5281/zenodo.3355887.

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Overview of environment perception for intelligent vehicles supposes to the state-of-the-art algorithms and modeling methods are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. Integrated lane and vehicle tracking for driver assistance system that improves on the performance of both lane tracking and vehicle tracking modules. Without specific hardware and software optimizations, the fully implemented system runs at near-real-time speeds of 11
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Cho, Minjun, Sungwoo Kim, Dooho Choi, and Yunsick Sung. "Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions." Applied Sciences 15, no. 7 (2025): 3712. https://doi.org/10.3390/app15073712.

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Autonomous driving technology has advanced significantly. However, it is challenging to accurately generate captions for driving environment scenes, which involve dynamic elements such as vehicles, traffic signals, road conditions, weather, and the time of day. Capturing these elements accurately is important for improving situational awareness in autonomous systems. Driving environment scene captioning is an important part of generating driving scenarios and enhancing the interpretability of autonomous systems. However, traditional vision–language models struggle with domain adaptation since
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Mauri, Antoine, Redouane Khemmar, Benoit Decoux, Madjid Haddad, and Rémi Boutteau. "Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility." Journal of Imaging 7, no. 8 (2021): 145. http://dx.doi.org/10.3390/jimaging7080145.

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For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localiz
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Chen, Haoyang, Na Li, Hangguan Shan, Eryun Liu, and Zhiyu Xiang. "Multi-Task Trajectory Prediction Using a Vehicle-Lane Disentangled Conditional Variational Autoencoder." Sensors 25, no. 14 (2025): 4505. https://doi.org/10.3390/s25144505.

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Trajectory prediction under multimodal information is critical for autonomous driving, necessitating the integration of dynamic vehicle states and static high-definition (HD) maps to model complex agent–scene interactions effectively. However, existing methods often employ static scene encodings and unstructured latent spaces, limiting their ability to capture evolving spatial contexts and produce diverse yet contextually coherent predictions. To tackle these challenges, we propose MS-SLV, a novel generative framework that introduces (1) a time-aware scene encoder that aligns HD map features w
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Ramirez-Robles, Ethery, Oleg Starostenko, and Vicente Alarcon-Aquino. "Real-time path planning for autonomous vehicle off-road driving." PeerJ Computer Science 10 (July 24, 2024): e2209. http://dx.doi.org/10.7717/peerj-cs.2209.

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Background Autonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and a
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Sun, Zhongyu, Wangping Zhou, Chen Ding, and Min Xia. "Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image." ISPRS International Journal of Geo-Information 11, no. 3 (2022): 165. http://dx.doi.org/10.3390/ijgi11030165.

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Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to add
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Zhang, Jie, Chunfang Liu, and Kuk Chol Ri. "Big Sur: Kerouac’s Spiritual Drop Scene." English Language and Literature Studies 12, no. 3 (2022): 17. http://dx.doi.org/10.5539/ells.v12n3p17.

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Big Sur is an important novel in Kerouac’s late period. The origin, content and purpose of this novel are significantly different from his previous works. Through analyzing the symbolic images in the novel, we can understand Kerouac’s painful reflection on his resistance against the American mainstream culture, his deep understanding of the failure to convey his spiritual appeals and his inability to compromise with the society. The study of Big Sur not only shows Kerouac’s spiritual collapse, but also reveals the spiritual trajectory of the Beats from fanaticism
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Ueda, Yuriko, Miho Adachi, Junya Morioka, Marin Wada, and Ryusuke Miyamoto. "Data Augmentation for Semantic Segmentation Using a Real Image Dataset Captured Around the Tsukuba City Hall." Journal of Robotics and Mechatronics 35, no. 6 (2023): 1450–59. http://dx.doi.org/10.20965/jrm.2023.p1450.

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We are exploring the use of semantic scene understanding in autonomous navigation for the Tsukuba Challenge. However, manually creating a comprehensive dataset that covers various outdoor scenes with time and weather variations to ensure high accuracy in semantic segmentation is onerous. Therefore, we propose modifications to the model and backbone of semantic segmentation, along with data augmentation techniques. The data augmentation techniques, including the addition of virtual shadows, histogram matching, and style transformations, aim to improve the representation of variations in shadow
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Tabatabaie, Mahan, Suining He, Hao Wang, and Kang G. Shin. "Beyond "Taming Electric Scooters": Disentangling Understandings of Micromobility Naturalistic Riding." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 3 (2024): 1–24. http://dx.doi.org/10.1145/3678513.

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Electric(e)-scooters have emerged as a popular, ubiquitous, and first/last-mile micromobility transportation option within and across many cities worldwide. With the increasing situation-awareness and on-board computational capability, such intelligent micromobility has become a critical means of understanding the rider's interactions with other traffic constituents (called Rider-to-X Interactions, RXIs), such as pedestrians, cars, and other micromobility vehicles, as well as road environments, including curbs, road infrastructures, and traffic signs. How to interpret these complex, dynamic, a
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Li, Yongfu, Yingkai Long, Mingming Du, Xiping Jiang, and Xianfu Liu. "Navigation and Positioning Analysis of Electric Inspection Robot Based on Improved SVM Algorithm." Journal of Sensors 2022 (July 25, 2022): 1–6. http://dx.doi.org/10.1155/2022/4613931.

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In order to improve the accuracy of electric inspection robot navigation and positioning, an improved SVM algorithm was proposed to improve the accuracy of inspection. The research focuses on sensor calibration technology, lane line detection and robot positioning technology, obstacle detection and tracking technology, and substation road scene understanding technology. The results show that the radar measurement results have great fluctuation and deviation due to the existence of noise, but the results are smoother after EKF estimation. Secondly, the accuracy of the improved SVM classifier in
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GIRLESCU, Nona, Madalina Maria DIAC, Iuliana HUNEA, Simona Irina DAMIAN, Anton KNIELING, and Diana BULGARU ILIESCU. "COMPLEX MECHANISMS IN ROAD TRAFFIC ACCIDENTS CONCERNING PEDESTRIANS. A CASE STUDY." Medicine and Materials 1, no. 1 (2021): 11–22. http://dx.doi.org/10.36868/medmater.2021.01.01.011.

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Pedestrian injuries vary according to vehicle type, position during the accident, and pedestrian’s age, thus determining complex aspects by associating multiple types of traumas. In forensic practice, it should be noted that the lesion-producing mechanisms recorded among pedestrians are most frequently mixed, reason for which a correct and careful examination of the victim must be supported by the characteristics of the vehicle involved in the accident, as well as by other elements at the crime scene. It is necessary to thoroughly examine the injuries, an analysis that should always be charact
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Paliska, Dejan, Milan Batista, Roman Starin, and Daša Fabjan. "An Attempt to Attain New Information in Reconstruction of Road Traffic Accidents Applying Digital Image Processing." PROMET - Traffic&Transportation 23, no. 2 (2012): 113–19. http://dx.doi.org/10.7307/ptt.v23i2.138.

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Court expertise dealing with the reconstruction of road traffic accidents often have to take into account the possibility that an accident could have been a set-up. Such suspicions can be eliminated only by considering all the evidence material from the accident scene. In case of photographic material experts come across the missing material, bad lighting, lack of contrast, different angle perspectives, blurring, omitting important details, etc. Therefore, different methods in forensics image processing have been developed. Most of these methods are primarily used in the processing of differen
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MAYA, MARGARITA LÓPEZ. "The Venezuelan Caracazo of 1989: Popular Protest and Institutional Weakness." Journal of Latin American Studies 35, no. 1 (2003): 117–37. http://dx.doi.org/10.1017/s0022216x02006673.

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27 February 1989 saw a popular revolt, which was to escalate dramatically, break out in Venezuela. Both Caracas and most of the main and secondary cities of the country were the scene of barricades, road closures, the stoning of shops, shooting and widespread looting. This article describes the events occurring during the Caracazo or Sacudón, as the episode is known, in order to show the key role played by the weakness of a set of social and political institutions in the violent forms of collective action that prevailed. This data, on a comparative basis, may enrich our understanding of other
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Kovács, Réka, and Andrada Savin. "Music Autobiographies – Performing Selves." Acta Universitatis Sapientiae, Philologica 14, no. 3 (2022): 127–42. http://dx.doi.org/10.2478/ausp-2022-0029.

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Abstract The article pays tribute to four artists of the music scene, i.e. Bob Dylan, Bruce Springsteen, Patti Smith, and John Luther Adams. It walks in their footsteps through their autobiographies and features the major landmarks in their artistic and creative evolution. Despite the various incongruent traits in their music style, background, or gender, music autobiographies prove to be valuable assets, based on which correlations and contrasts can be elucidated, the road to growing into an artist can be followed, and the creative spirit can be grasped. We hereby conclude that autobiographie
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Tian, Renran, Stanley Chien, Yaobin Chen, and Rini Sherony. "Pedestrian Moving Patterns during Potential Conflicts with 110 On-Road Driving Vehicles." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (2019): 2036–40. http://dx.doi.org/10.1177/1071181319631434.

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As the most commonly seen vulnerable road users, protection and interaction with pedestrians are key functionalities in vehicle active safety and self-driving research areas. Development and evaluation of such systems require deeper understanding of pedestrian behaviors, especially motion patterns, in different driving environments. Traditionally, most of the pedestrian movement studies rely on fixed roadside cameras in specific road locations with higher pedestrian density, like intersections and junctions. Although these studies can provide information to describe pedestrian walking behavior
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Researcher. "A REVIEW PAPER ON STUDY OF ROAD USER CHARACTERISTICS USING VISSIM FOR INDIAN SCENARIO." Journal of Public Transportation System (JPTS) 3, no. 2 (2024): 23–39. https://doi.org/10.5281/zenodo.14160140.

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With rising levels of urbanization and vehicle traffic, India's transportation scene is changing quickly. In this dynamic context, an understanding of road user characteristics is essential for efficient transportation planning and traffic management. This study analyses road user behaviour and features in the Indian environment using VISSIM, a microscopic traffic simulation program. The study's primary objective is to gather actual traffic data from India's urban and suburban areas, including a range of factors such as user behaviour, traffic density, and road conditions. The study examines v
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Chen, Rung-Ching, Vani Suthamathi Saravanarajan, Long-Sheng Chen, and Hui Yu. "Road Segmentation and Environment Labeling for Autonomous Vehicles." Applied Sciences 12, no. 14 (2022): 7191. http://dx.doi.org/10.3390/app12147191.

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In autonomous vehicles (AVs), LiDAR point cloud data are an important source to identify various obstacles present in the environment. The labeling techniques that are currently available are based on pixel-wise segmentation and bounding boxes to detect each object on the road. However, the Avs’ decision on motion control and trajectory path planning depends on the interaction among the objects on the road. The ability of the Avs to understand the moving and non-moving objects is the key to scene understanding. This paper presents a novel labeling method to combine moving and non-moving object
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He, Yelu, Cheng Fu, Wernher Brucks, and Robert Weibel. "Modelling the influence of traffic infrastructure characteristics on e-scooter accidents in the city of Zurich." AGILE: GIScience Series 6 (June 9, 2025): 1–7. https://doi.org/10.5194/agile-giss-6-26-2025.

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Abstract. The rapid emergence of shared electric scooter (e-scooter) services has posed new challenges to road safety over the last few years as a serious worldwide public concern. Previous studies have investigated e-scooter accidents from multiple perspectives. However, research gaps still exist in understanding the role of infrastructure-related factors in e-scooter accidents. This study aims to investigate and model the relationship between the characteristics of traffic infrastructure and the presence of e-scooter accidents, especially the presence of curbs and the complexity of street vi
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Han, Guodong, Shuanfeng Zhao, Pengfei Wang, and Shijun Li. "Driver Attention Area Extraction Method Based on Deep Network Feature Visualization." Applied Sciences 10, no. 16 (2020): 5474. http://dx.doi.org/10.3390/app10165474.

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The current intelligent driving technology based on image data is being widely used. However, the analysis of traffic accidents occurred in intelligent driving vehicles shows that there is an explanatory difference between the intelligent driving system based on image data and the driver’s understanding of the target information in the image. In addition, driving behavior is the driver’s response based on the analysis of road information, which is not available in the current intelligent driving system. In order to solve this problem, our paper proposes a driver attention area extraction metho
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