Applying tanh, softsign, or scaling smoothing to ResNet activations yields stylization quality comparable to softmax-based SWAG, though the evidence is only qualitative.
Prioritized experience replay-based DDQN for Unmanned Vehicle Path Planning
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abstract
Path planning module is a key module for autonomous vehicle navigation, which directly affects its operating efficiency and safety. In complex environments with many obstacles, traditional planning algorithms often cannot meet the needs of intelligence, which may lead to problems such as dead zones in unmanned vehicles. This paper proposes a path planning algorithm based on DDQN and combines it with the prioritized experience replay method to solve the problem that traditional path planning algorithms often fall into dead zones. A series of simulation experiment results prove that the path planning algorithm based on DDQN is significantly better than other methods in terms of speed and accuracy, especially the ability to break through dead zones in extreme environments. Research shows that the path planning algorithm based on DDQN performs well in terms of path quality and safety. These research results provide an important reference for the research on automatic navigation of autonomous vehicles.
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cs.CV 1years
2024 1verdicts
REJECT 1representative citing papers
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Artistic Neural Style Transfer Algorithms with Activation Smoothing
Applying tanh, softsign, or scaling smoothing to ResNet activations yields stylization quality comparable to softmax-based SWAG, though the evidence is only qualitative.