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Found 8 papers, 2 papers with code
Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions
Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people.
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets
Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment.
Sparse Dictionary Learning by Dynamical Neural Networks
A dynamical neural network consists of a set of interconnected neurons that interact over time continuously.
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact.
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings.
Dictionary Learning by Dynamical Neural Networks
A dynamical neural network consists of a set of interconnected neurons that interact over time continuously.
Local Information with Feedback Perturbation Suffices for Dictionary Learning in Neural Circuits
While the sparse coding principle can successfully model information processing in sensory neural systems, it remains unclear how learning can be accomplished under neural architectural constraints.
Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results
With a moderate but well-defined assumption, we prove that the SNN indeed solves sparse coding.
