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Found 6 papers, 0 papers with code
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving
In autonomous driving tasks, scene understanding is the first step towards predicting the future behavior of the surrounding traffic participants.
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features
In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach.
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
Our approach is based on a novel technique to defend against adversarial evidence (i. e. faulty evidence due to artefacts) by filtering gradients during optimization.
Functionally Modular and Interpretable Temporal Filtering for Robust Segmentation
Our filter module splits the filter task into multiple less complex and more interpretable subtasks.
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
Generating a robust representation of the environment is a crucial ability of learning agents.
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent.
