Simultaneous Localization And Mapping (SLAM) has become a crucial aspect in the fields of autonomous driving and robotics. One crucial component of visual SLAM is the Field-of-View (FoV) of the camera, as a larger FoV...
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Learning driving policies using an end-to-end network has been proved a promising solution for autonomous driving. Due to the lack of a benchmark driver behavior dataset that contains both the visual and the LiDAR dat...
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Learning driving policies using an end-to-end network has been proved a promising solution for autonomous driving. Due to the lack of a benchmark driver behavior dataset that contains both the visual and the LiDAR data, existing works solely focus on learning driving from visual sensors. Besides, most works are limited to predict steering angle yet neglect the more challenging vehicle speed control problem. In this paper, we propose a novel end-to-end network, FlowDriveNet, which takes advantages of sequential visual data and LiDAR data jointly to predict steering angle and vehicle speed. The main challenges of this problem are how to efficiently extract driving-related information from images and point clouds, and how to fuse them effectively. To tackle these challenges, we propose a concept of point flow and declare that image optical flow and LiDAR point flow are significant motion cues for driving policy learning. Specifically, we first create an enhanced dataset that consists of images, point clouds and corresponding human driver behaviors. Then, in FlowDriveNet, a deep but efficient visual feature extraction module and a point feature extraction module are utilized to extract spatial features from optical flow and point flow, respectively. Additionally, a novel temporal fusion and prediction module is designed to fuse temporal information from the extracted spatial feature sequences and predict vehicle driving commands. Finally, a series of ablation experiments verify the importance of optical flow and point flow and comparison experiments show that our flow-based method outperforms the existing image-based approaches on the task of driving policy learning.
Boundary equilibrium generative adversarial networks(BEGANs)are the improved version of generative adversarial networks(GANs).In this paper,an improved BEGAN with a skip-connection technique in the generator and the d...
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Boundary equilibrium generative adversarial networks(BEGANs)are the improved version of generative adversarial networks(GANs).In this paper,an improved BEGAN with a skip-connection technique in the generator and the discriminator is ***,an alternative time-scale update rule is adopted to balance the learning rate of the generator and the ***,the performance of the proposed method is quantitatively evaluated by Fréchet inception distance(FID)and inception score(IS).The test results show that the performance of the proposed method is better than that of the original BEGAN.
We propose a high-performance glass-plastic hybrid minimalist aspheric panoramic annular lens (ASPAL) to solve several major limitations of the traditional panoramic annular lens (PAL), such as large size, high weight...
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Temporal information plays a pivotal role in Bird’s-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the b...
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Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computational costs and prolonged training d...
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Quantum machine learning is considered one of the current research fields with great potential. In recent years, Havlíček et al. [Nature 567, 209-212 (2019)] have proposed a quantum machine learning algorithm wit...
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Deep Learning has greatly advanced the performance of semantic segmentation, however, its success relies on the availability of large amounts of annotated data for training. Hence, many efforts have been devoted to do...
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Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potent...
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Semantic scene understanding with Minimalist Optical Systems (MOS) in mobile and wearable applications remains a challenge due to the corrupted imaging quality induced by optical aberrations. However, previous works o...
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