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Found 12 papers, 2 papers with code
Residual Chain Prediction for Autonomous Driving Path Planning
Our findings highlight the potential of Residual Chain Loss to revolutionize planning component of autonomous driving systems, marking a significant step forward in the quest for level 5 autonomous driving system.
Path Planning based on 2D Object Bounding-box
The implementation of Autonomous Driving (AD) technologies within urban environments presents significant challenges.
GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes and Minimalist Workflow
However, the availability of free and open-source simulators is limited, and the installation and configuration process can be daunting for beginners and interdisciplinary researchers.
YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object Detection
Vehicle perception systems strive to achieve comprehensive and rapid visual interpretation of their surroundings for improved safety and navigation.
Inverted Non-maximum Suppression for more Accurate and Neater Face Detection
Our method performs well on low-quality and tiny face samples.
Fast and Accurate Object Detection on Asymmetrical Receptive Field
To address the above challenges, based on extensive literature research, this paper analyzes methods for improving and optimizing mainstream object detection algorithms from the perspective of evolution of one-stage and two-stage object detection algorithms.
BCSSN: Bi-direction Compact Spatial Separable Network for Collision Avoidance in Autonomous Driving
In this paper, we propose a CNN-based method that overcomes the limitation by establishing feature correlations between regions in sequential images using variants of attention.
Sequential Spatial Network for Collision Avoidance in Autonomous Driving
Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area.
Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation
Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive.
Autonomous Driving Simulator based on Neurorobotics Platform
There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive.
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals
To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training.
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS
Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i. e., aggregation in layers of roadside units and cloud.
