Multiple Object Detection Based on Clustering and Deep Learning Methods by Huu Thu Nguyen, Eon-Ho Lee, Chul Hee Bae, Sejin Lee

“…Results indicate the potential application of the proposed method in the fields of object detection, autonomous driving system, and so forth.…”
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Mobip: a lightweight model for driving perception using MobileNet by Minghui Ye, Jinhua Zhang

“…The visual perception model is critical to autonomous driving systems. It provides the information necessary for self-driving cars to make decisions in traffic scenes. …”
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Five Maturity Levels of Managing AI: From Isolated Ignorance to Integrated Intelligence by Ulrich Lichtenthaler

“…To guide future discussions about managing artificial intelligence (AI), this article suggests an AI management framework with five maturity levels, which are comparable to the five levels of the autonomous driving framework from no automation to complete automation. …”
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A commentary of Hyper-accurate position in MIT Technology Review 2021 by Xia Ren, Yuanxi Yang

“…These hyper-accurate positioning technologies will strongly support the development of autonomous driving, precision agriculture, geological hazard monitoring, etc.①① Original source in Chinese: Y. …”
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Neuroscope: An Explainable AI Toolbox for Semantic Segmentation and Image Classification of Convolutional Neural Nets by Christian Schorr, Payman Goodarzi, Fei Chen, Tim Dahmen

“…Trust in artificial intelligence (AI) predictions is a crucial point for a widespread acceptance of new technologies, especially in sensitive areas like autonomous driving. The need for tools explaining AI for deep learning of images is thus eminent. …”
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A Curb-Detection Network with a Tri-Plane BEV Encoder Module for Autonomous Delivery Vehicles by Lu Zhang, Jinzhu Wang, Xichan Zhu, Zhixiong Ma

“…Curb detection tasks play a crucial role in the perception of the autonomous driving environment for logistics vehicles. With the popularity of multi-modal sensors under the BEV (Bird’s Eye View) paradigm, curb detection tasks are increasingly being integrated into multi-task perception networks, achieving robust detection results. …”
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ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network by Songyan Zhao, Lingshan Chen, Yongchao Huang

“…The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. …”
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Reinforcement Learning Path Planning Method with Error Estimation by Feihu Zhang, Can Wang, Chensheng Cheng, Dianyu Yang, Guang Pan

“…Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. …”
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PCCN‐RE: Point cloud colourisation network based on relevance embedding by Feiran Wang, Xiaoqiang Li, Jitao Liu

“…Abstract The colour of the point cloud provides abundant information for perception tasks such as autonomous driving and virtual reality. However, few prior work studied the automatic colourisation of colourless point clouds. …”
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Exploring Smart Tires as a Tool to Assist Safe Driving and Monitor Tire–Road Friction by Maria Pomoni

“…The current challenges and research prospects are highlighted in terms of tire manufacturers’ perspectives as well as future mobility patterns with autonomous driving modes. Overall, smart tires are commented as a tool able to enhance drivers’ safety for both current and future mobility patterns, help to control pavement deterioration and complement existing practices for infrastructure condition assessment.…”
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LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames by Hao Fu, Hanzhang Xue, Xiaochang Hu, Bokai Liu

“…In autonomous driving scenarios, the point cloud generated by LiDAR is usually considered as an accurate but sparse representation. …”
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Two-Stage Framework for Faster Semantic Segmentation by Ricardo Cruz, Diana Teixeira e Silva, Tiago Gonçalves, Diogo Carneiro, Jaime S. Cardoso

“…The framework is evaluated in four datasets (autonomous driving and biomedical), across four state-of-the-art architectures. …”
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Assessment of Machine Learning algorithms for automated monitoring by Carmen-Ionela ROTUNĂ, Mihail DUMITRACHE, Ionuț-Eugen SANDU

“…Language, computer vision, autonomous driving, robotics, and automated application monitoring, which have been core problems in AI ever since 1950, have reached a new technological peak nowadays. …”
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Deep Learning-Based Frameworks for Semantic Segmentation of Road Scenes by Haneen Alokasi, Muhammad Bilal Ahmad

“…Semantic segmentation using machine learning and computer vision techniques is one of the most popular topics in autonomous driving-related research. With the revolution of deep learning, the need for more efficient and accurate segmentation systems has increased. …”
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Robust day and night object detection based on heterogeneous sensors and information fusion by Yun, Yanpu

“…Object detection and localization is now an important component in autonomous driving-related applications, in which the technology based on traditional RGB cameras has become increasingly mature. …”
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Available parking spaces detection with deep learning by Li, Xiaochen

“…With the great development and improvement of the Artificial Intelligence, we believe that automated driving will happen in the near future, and autonomous parking is a crucial step towards future autonomous driving. This report will give a study of the deep learning algorithms for image semantic segmentation of available parking spaces and CARLA Simulator. …”
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VidLoc: A deep spatio-temporal model for 6-DoF video-clip relocalization by Clark, R, Wang, S, Markham, A, Trigoni, N, Wen, H

“…We evaluate our method on openly-available real-world autonomous driving and indoor localization datasets.…”

Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles by Wu, M, Louw, T, Lahijanian, M, Ruan, W, Huang, X, Merat, N, Kwiatkowska, M

“…The method is evaluated on a data set of 124 experiments from 75 drivers collected in a safety-critical semi-autonomous driving scenario. The results illustrate the efficacy of the framework by correctly anticipating the drivers’ intentions about 3 seconds beforehand with over 90% accuracy.…”

Addressing appearance change in outdoor robotics with adversarial domain adaptation by Wulfeier, M, Bewley, A, Posner, H

“…The distilled insights are applied to the problem of free-space segmentation for motion planning in autonomous driving.…”

1 year, 1000 km: the Oxford RobotCar dataset by Maddern, W, Pascoe, G, Linegar, C, Newman, P

“…We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on average using the Oxford RobotCar platform, an autonomous Nissan LEAF. …”