Multiple future predictions are a modern discipline in the study of a broadened future exploration of alternative models of action. It involves the use of case studies derived from institutional and organizational foresight to illustrate methods and theories. It is a systematic study of probable, preferable, and possible futures of worldviews that underlie the expectations (Kim & Won, 2018). The context of Multiple Futures Prediction is laid on an environment that aims at shaping the desired future and mapping the alternative futures at external joint levels as well as the inner level of an individual. The major problem being solved by the paper entails those that are related to the external objective through a layered approach that relates to how the world shapes the future perspective (Kim & Won, 2018). The article relates to problems that are concerned with serious future approaches and the post-structural turns that communicate how the external world gets informed through the actions that are taken by those responsible.
The Multiple Futures Prediction proposes a system that can be used in predicting multiple solutions within a stochastic active environment. Its approach includes extending the sequence-to-sequence learning to create an RNN agents population to predict multimodal statuses (Sun et al, 2019). The article provides a novel technique for the future prediction of trajectories in a scene of multiple vehicles. The general approach is validated based on two datasets which include CARLA and NGSIM where it sets a new state-of-the-art approach. Also, it provides RNN that can each be used to represent a specific agent within the environment and indirectly develop agent-to-agent connections and integrate other behaviors of other agents directly in future predictions. Further, it demonstrates the SOTA results upon the NGSIM dataset while experimenting on their approach from data acquired from CARLA environment simulation (Sun et al., 2019). The authors also provide supplementary materials that ensure the description of details that facilitate the reproduction of interesting qualitative results.
It is critical in making robust and intelligent decisions within complex dynamic environments. This is because it emphasizes the fact that motion prediction should be modeled for the inherently uncertain future that is often characterized by potential multiple. This is outcomes are due to multi-agent relations and latent objectives of other people (Chen et al., 2018). Multiple future prediction article introduces a probabilistic outline that efficiently detects latent variables and jointly models multiple-step future signals of agents in a given scene. It depicts a framework that is data-driven with the capability to learn semantically to present a multimodal future without explicit labels. The proposal can encode both past and future agent interactions and efficiently scale any multiple amounts of agents (Chen et al., 2018). It also provides a platform for computational planning in the analysis of restrictive probability density.
It focuses on learning so that it can align present with future distributions. It promptly describes the existing work that is specific to the prediction of vehicle trajectory (Chen et al., 2018). The authors portray that good results of the benchmark dataset were obtained in the domain that promotes the accuracy of predictive analysis. However, the approach of the paper differs from past work in a way that multiple future possibilities were presented. The multi-modal trajectory prediction of the surrounding vehicles represented was valid and highly recommendable. It also has a diverse baseline of datasets which is highly reasonable and the experiments are well executed. However, it is not easy to understand the mathematical concepts that are presented in the paper without a prior knowledge base (Chen et al., 2018). Similarly, the author has not provided simpler and explanatory translations that can aid in knowledge acquisition.
To make the paper solid and easy to understand and implement the given case issues, the document approach ought to have a case analysis particular that deals with decision-making presentations (Bruaset & Sægrov, 2018). This is because its motivation is based on the influence of temporal prediction in intelligent decision-making within complex and dynamic environments. The provision of a simulation environment with complicated dynamics could have given a better application of the approach. Similarly, the paper only deals with experiments that are based on the perfect advancement of issues in modeling the pre-recorded dataset in metrics log-likelihood estimation. The experiments are important especially since they compare with a huge body of prevailing work through the use of NGSIM (Bruaset & Sægrov, 2018). For the essay to create a greater impact in its general application, it should have considered the application of the technique to the practical tasks where performance could be measured in terms of the task objectives and rewards.
Similarly, the use of the CARLA environment should have been considered in the definition of the actual hard tasks since it is also part of the RL environment (Rudenko et al., 2020). Similarly, it can help in facilitating the approach in solving difficult tasks that are important for self-driving agents to tackle. The prevailing bulk of work does not directly deal with the autonomous domain in driving. Motion prediction should be able to model an inherently unclear future that often contains multiple potential results because of multi-agent interactions (Rudenko et al., 2020). This should be in line with the document proposed techniques that build upon the generative latent simulations of the environment from data collected. The sample future trajectories of multi-model and address the critical question on how the models solve problems.
The discussion should have been able to link the projected line of work of the essay with the literature in its probabilistic models. In general, the paper’s suggested approach is highly thoughtful and, therefore, qualifies for recommendation especially if the authors obtain RL tasks results and report the performance on the actual task (Rudenko et al., 2020). The article has been produced professionally with high quality and proper considerations of facts. The authors have critically considered key factors that are solutions in the achievement of key multiple predictions that can facilitate actions for the achievement of the given objective. As such, with the improvement of its general critical conceptual framework that allows it to explicitly portray its main models, it has succeeded in communicating the goals of the authors. Moreover, it has considered the basic pillars that provide theory to future thinking which are linked to tools and methods that are established through praxis. The key pillars that it has successfully considered in its application include; mapping, timing, deepening, anticipation, and creating and transforming alternatives. These theories have been used to portray a future setting to provide a linear sequential sense originating from mapping to transformation. As such, it can be concluded that the paper is of high quality and can be used as a source of reference by experts during their considerations.
Bruaset, S., & Sægrov, S. (2018). Using the multiple scenario approach for envisioning plausible futures in long-term planning and management of the urban water pipe systems. European Journal of Futures Research, 6(1), 7.
Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., & Stanley, H. E. (2018). Which artificial intelligence algorithm better predicts the Chinese stock market? IEEE Access, 6, 48625-48633.
Kim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25-37.
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020). Human motion trajectory prediction: A survey. The International Journal of Robotics Research, 39(8), 895-935.
Sun, C., Karlsson, P., Wu, J., Tenenbaum, J. B., & Murphy, K. (2019). Stochastic prediction of multi-agent interactions from partial observations.