D-STGCN: Dynamic Pedestrian Trajectory Prediction Using Spatio-Temporal Graph Convolutional Networks by Bogdan Ilie Sighencea, Ion Rareș Stanciu, Cătălin Daniel Căleanu

“…Predicting pedestrian trajectories in urban scenarios is a challenging task that has a wide range of applications, from video surveillance to autonomous driving. The task is difficult since pedestrian behavior is affected by both their individual path’s history, their interactions with others, and with the environment. …”
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Embedded System Vehicle Based on Multi-Sensor Fusion by Rui Tong, Quan Jiang, Zuqi Zou, Tao Hu, Tianhao Li

“…As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. …”
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Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis by Juan Luis Hortelano, Jorge Villagrá, Jorge Godoy, Víctor Jiménez

“…Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). …”
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A CRITERION OF RADIAL DISTORTION ON STRUCTURE FROM MOTION WITH CONSUMER-GRADE CAMERAS by T. Xiao, S. Guo, L. Wu, Q. Yan, F. Deng

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DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain by Xiaoxuan Chen, Yineng Chen, Xiayu Wang, Xinghui Zhu, Kui Fang

“…More importantly, VANET can provide route planning and autonomous driving for drivers by analyzing data. However, VANET’s data privacy and security are a huge challenge when serving drivers. …”
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High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles by Eduardo Sánchez Morales, Julian Dauth, Bertold Huber, Andrés García Higuera, Michael Botsch

“…A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. …”
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RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance by Jongwon Kim, Jeongho Cho

“…With the recent advancements in multimodal data-based object detection architectures, autonomous driving technology has significantly improved, and the latest algorithm has achieved an average precision of up to 96%. …”
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A Layer Selection Optimizer for Communication-Efficient Decentralized Federated Deep Learning by Luca Barbieri, Stefano Savazzi, Monica Nicoli

“…The proposed technique is validated on a vehicular cooperative sensing use case characterized by challenging real-world datasets and complex DNNs typically employed in autonomous driving with up to 40 trainable layers. The impact of layer selection is analyzed under different distributed coordination configurations. …”
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Accurate and Robust Monocular SLAM with Omnidirectional Cameras by Shuoyuan Liu, Peng Guo, Lihui Feng, Aiying Yang

“…Simultaneous localization and mapping (SLAM) are fundamental elements for many emerging technologies, such as autonomous driving and augmented reality. For this paper, to get more information, we developed an improved monocular visual SLAM system by using omnidirectional cameras. …”
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EHB Gear-Drive Symmetric Dead-Zone Finite-Time Adaptive Control by Shuai Wang, Qinghua Cao, Fukuo Ma, Jian Wu

“…Electrohydraulic braking (EHB) systems can better adapt to the development of autonomous driving technology. The gear transmission system plays a crucial role in EHB deceleration and torque increase mechanisms. …”
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SyntCities: A Large Synthetic Remote Sensing Dataset for Disparity Estimation by Mario Fuentes Reyes, Pablo D'Angelo, Friedrich Fraundorfer

“…However, available datasets mostly focus on close-range applications like autonomous driving or robot manipulation. To reduce the domain gap while training we present SyntCities, a synthetic dataset resembling the aerial imagery on urban areas. …”
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3D Object Detection Based on Attention and Multi-Scale Feature Fusion by Minghui Liu, Jinming Ma, Qiuping Zheng, Yuchen Liu, Gang Shi

“…Three-dimensional object detection in the point cloud can provide more accurate object data for autonomous driving. In this paper, we propose a method named MA-MFFC that uses an attention mechanism and a multi-scale feature fusion network with ConvNeXt module to improve the accuracy of object detection. …”
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A framework of safety analysis with temporal feature based on MBSA and case study for ACC system by Lisong Wang, Qin Zhang, Jun Hu

“…Abstract The safety of automotive Adaptive Cruise Control (ACC) system is of great significance to prevent fatigue driving, improve driving comfort, reduce accident rate and promote the development of intelligent transportation and autonomous driving technology. However, the current safety analysis of ACC lacks consideration of the temporal dynamic property, so it is necessary to establish a set of safety analysis methods to consider the temporal characteristics. …”
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Multi-Input Deep Learning Based FMCW Radar Signal Classification by Daewoong Cha, Sohee Jeong, Minwoo Yoo, Jiyong Oh, Dongseog Han

“…In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. …”
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Boosting Multi-Modal Unsupervised Domain Adaptation for LiDAR Semantic Segmentation by Self-Supervised Depth Completion by Adriano Cardace, Andrea Conti, Pierluigi Zama Ramirez, Riccardo Spezialetti, Samuele Salti, Luigi Di Stefano

“…LiDAR semantic segmentation is receiving increased attention due to its deployment in autonomous driving applications. As LiDARs come often with other sensors such as RGB cameras, multi-modal approaches for this task have been developed, which however suffer from the domain shift problem as other deep learning approaches. …”
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Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments by Daniel Medina, Haoqing Li, Jordi Vilà-Valls, Pau Closas

“…Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. …”
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A Novel Upsampling and Context Convolution for Image Semantic Segmentation by Khwaja Monib Sediqi, Hyo Jong Lee

“…Semantic segmentation, which refers to pixel-wise classification of an image, is a fundamental topic in computer vision owing to its growing importance in the robot vision and autonomous driving sectors. It provides rich information about objects in the scene such as object boundary, category, and location. …”
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Performance of Mobile LiDAR in Real Road Driving Conditions by Jisoo Kim, Bum-jin Park, Chang-gyun Roh, Youngmin Kim

“…These results demonstrate that LiDAR must overcome the challenges posed by inclement weather to be applicable in the production of road facilities that improve the effectiveness of autonomous driving sensors.…”
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Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning by Huanbiao Zhuang, Chaofan Lei, Yuanhang Chen, Xiaojun Tan

“…Ultimately, the comparative experiments were conducted to demonstrate that the proposed approach was more adaptive and generalized than the heuristic rule-based model, which revealed its great potential for reinforcement learning in the decision-making of autonomous driving.…”
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STAF: Spatio-Temporal Attention Framework for Understanding Road Agents Behaviors by R. Trabelsi, R. Khemmar, B. Decoux, J.-Y. Ertaud, R. Boutteau

“…Experiments have been carried out on two different scenarios over data from Joint Attention in Autonomous Driving (JAAD) and Honda Research Institute Driving Dataset (HDD), both datasets devoted to understanding the behavior of road agents. …”
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