Coordination recognition and subtle pattern prediction of future trajectories play a significant role when modeling interactive behaviors of multiple agents. Due to the essential property of uncertainty in the future evolution, deterministic predictors are not sufficiently safe and robust. In order to tackle the task of probabilistic prediction for multiple, interactive entities, we propose a coordination and trajectory prediction system (CTPS), which has a hierarchical structure including a macro-level coordination recognition module and a micro-level subtle pattern prediction module which solves a probabilistic generation task. We illustrate two types of representation of the coordination variable: categorized and real-valued, and compare their effects and advantages based on empirical studies. We also bring the ideas of Bayesian deep learning into deep generative models to generate diversified prediction hypotheses. The proposed system is tested on multi-ple driving datasets in various traffic scenarios, which achieves better performance than baseline approaches in terms of a set of evaluation metrics. The results also show that using categorized coordination can better capture multi-modality and generate more diversified samples than the real-valued coordination, while the latter can generate prediction hypotheses with smaller errors with a sacrifice of sample diversity. Moreover, employing neural networks with weight uncertainty is able to generate samples with larger variance and diversity.
The parameters of deep neural networks in most deep learning literature are deterministic, which may not fully represent the model uncertainty. In recent years, more attention has been paid to avoid over-fitting or being over-confident on unseen cases by incorporating distributions over network parameters @cite_17 @cite_2 . In @cite_18 , a stochastic dynamical system was modeled by a Bayesian neural network (BNN) and used for model-based reinforcement learning. Moreover, in real-world applications such as autonomous driving, the surrounding environment is highly dynamic and full of uncertainty. @cite_8 suggested employing Bayesian deep learning to address safety issues. In this work, we treat the parameters in the proposed system as sampled instances from the learned distributions, which improves uncertainty modeling and increases the diversity of prediction hypotheses.
@cite_8 @cite_18 @cite_2 @cite_17
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Adrian Weller acknowledges support by the Alan Turing Institute under the EPSRC grant EP N510129 1, and by the Leverhulme Trust via the CFI. We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning. The BNNs are trained by minimizing @math -divergences, allowing us to capture complicated statistical patterns in the transition dynamics, e.g. multi-modality and heteroskedasticity, which are usually missed by other common modeling approaches. We illustrate the performance of our method by solving a challenging benchmark where model-based approaches usually fail and by obtaining promising results in a real-world scenario for controlling a gas turbine. Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input @math to a sample @math that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input @math to a sample @math . Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outperforms the state-of-the-arts.