Guardauto: a decentralized runtime protection system for autonomous driving by Cheng, Kun, Zhou, Yuan, Chen, Bihuan, Wang, Rui, Bai, Yuebin, Liu, Yang

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Contrastive learning-based knowledge distillation for RGB-thermal urban scene semantic segmentation by Guo, Xiaodong, Zhou, Wujie, Liu, Tong

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Robust and low complexity obstacle detection and tracking by Wu, Meiqing, Zhou, Chengju, Srikanthan, Thambipillai

“…Obstacle detection and tracking is essential module for autonomous driving. Vision based obstacle detection and tracking faces huge challenges due to factors like cluttered background, partial occlusion, inconsistent illumination, etc. …”
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A framework for fast and robust visual odometry by Wu, Meiqing, Lam, Siew-Kei, Srikanthan, Thambipillai

“…Knowledge of the ego-vehicle's motion state is essential for assessing the collision risk in advanced driver assistance systems or autonomous driving. Vision-based methods for estimating the ego-motion of vehicle, i.e., visual odometry, face a number of challenges in uncontrolled realistic urban environments. …”
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6G internet of things: a comprehensive survey by Nguyen, Dinh C., Ding, Ming, Pathirana, Pubudu N., Seneviratne, Aruna, Li, Jun, Niyato, Dusit, Dobre, Octavia, Poor, H. Vincent

“…Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely Healthcare Internet of Things, Vehicular Internet of Things and Autonomous Driving, Unmanned Aerial Vehicles, Satellite Internet of Things, and Industrial Internet of Things. …”
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Autonomous CNN (AutoCNN): a data-driven approach to network architecture determination by Aradhya, Abhay M S, Ashfahani, Andri, Angelina, Fienny, Pratama, Mahardhika, de Mello, Rodrigo Fernandes, Sundaram, Suresh

“…Therefore, the AutoCNN is a highly versatile CNN architecture determination tool that has a wide range of applications in the field of autonomous driving, medical image analysis, image enhancement, camera based security monitoring and image based fault detection.…”
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Learning temporal variations for 4D point cloud segmentation by Shi, Hanyu, Wei, Jiacheng, Wang, Hao, Liu, Fayao, Lin, Guosheng

“…LiDAR-based 3D scene perception is a fundamental and important task for autonomous driving. Most state-of-the-art methods on LiDAR-based 3D recognition tasks focus on single-frame 3D point cloud data, ignoring temporal information. …”
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SpSequenceNet : semantic segmentation network on 4D point clouds by Shi, Hanyu, Lin, Guosheng, Wang, Hao, Hung, Tzu-Yi, Wang, Zhenhua

“…Point clouds are useful in many applications like autonomous driving and robotics as they provide natural 3D information of the surrounding environments. …”
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Unsupervised point cloud representation learning with deep neural networks: a survey by Xiao, Aoran, Huang, Jiaxing, Guan, Dayan, Zhang, Xiaoqin, Lu, Shijian, Shao, Ling

“…Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving. The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data. …”
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Generalized out-of-distribution detection: a survey by Yang, Jingkang, Zhou, Kaiyang, Li, Yixuan, Liu, Ziwei

“…For instance, in autonomous driving, we would like the driving system to issue an alert and hand over the control to humans when it detects unusual scenes or objects that it has never seen during training time and cannot make a safe decision. …”
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Towards robust monocular depth estimation: a new baseline and benchmark by Xian, Ke, Cao, Zhiguo, Shen, Chunhua, Lin, Guosheng

“…Before deploying a monocular depth estimation (MDE) model in real-world applications such as autonomous driving, it is critical to understand its generalization and robustness. …”
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Transferable deep reinforcement learning framework for autonomous vehicles with joint radar-data communications by Nguyen, Quang Hieu, Dinh, Thai Hoang, Niyato, Dusit, Wang, Ping, Kim, Dong In, Yuen, Chau

“…With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation.…”
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Spectrum-learning-aided reconfigurable intelligent surfaces for 'Green' 6G networks by Yang, Bo, Cao, Xuelin, Huang, Chongwen, Guan, Yong Liang, Yuen, Chau, Di Renzo, Marco, Niyato, Dusit, Debbah, Mérouane, Hanzo, Lajos

“…In the sixth generation (6G) era, emerging large-scale computing-based applications (e.g., processing enormous amounts of images in real time in autonomous driving) tend to lead to excessive energy consumption for end users, whose devices are usually energy-constrained. …”
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