514 papers with code • 1 benchmarks • 27 datasets
Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process.
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.
Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic - surpassing all known model-based controllers to achieve near-optimal performance - and generalize to out-of-distribution traffic densities.
The proposed deep dual-resolution networks (DDRNets) are composed of two deep branches between which multiple bilateral fusions are performed.
And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.