We present a new algorithm for predicting the near-term trajectories of road-agents in dense traffic videos. Our approach is designed for heterogeneous traffic, where the road-agents may correspond to buses, cars, scooters, bicycles, or pedestrians. We model the interactions between different road-agents using a novel LSTM-CNN hybrid network for trajectory prediction. In particular, we take into account heterogeneous interactions that implicitly accounts for the varying shapes, dynamics, and behaviors of different road agents. In addition, we model horizon-based interactions which are used to implicitly model the driving behavior of each road-agent. We evaluate the performance of our prediction algorithm, TraPHic, on the standard datasets and also introduce a new dense, heterogeneous traffic dataset corresponding to urban Asian videos and agent trajectories. We outperform state-of-the-art methods on dense traffic datasets by 30 .
Methods that do not model road-agent interactions are regarded as sub-optimal or as less accurate than methods that model the interactions between road-agents in the scene @cite_3 . Examples of methods that explicitly model road-agent interaction include techniques based on social forces @cite_28 @cite_18 , velocity obstacles @cite_11 , LTA @cite_32 , etc. Many of these models were designed to account for interactions between pedestrians in a crowds (i.e. homogeneous interactions) and to improve the prediction accuracy @cite_35 . Techniques based on velocity obstacles have been extended using kinematic constraints to model the interactions between heterogeneous road-agents @cite_29 . Our learning approach does not use any explicit pairwise motion model. Rather, we model the heterogeneous interactions between road-agents implicitly.
@cite_35 @cite_18 @cite_28 @cite_29 @cite_32 @cite_3 @cite_11
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Effective norms, emerging from sustained individual interactions over time, can complement societ al rules and significantly enhance performance of individual agents and agent societies. Researchers have used a model that supports the emergence of social norms via learning from interaction experiences where each interaction is viewed as a stage game. In this social learning model, which is distinct from an agent learning from repeated interactions against the same player, an agent learns a policy to play the game from repeated interactions with multiple learning agents. The key research question is to characterize when and how the entire population of homogeneous learners converge to a consistent norm when multiple action combinations yield the same optimal payoff. In this paper we study two extensions to the social learning model that significantly enhances its applicability. We first explore the effects of heterogeneous populations where different agents may be using different learning algorithms. We also investigate norm emergence when agent interactions are physically constrained. We consider agents located on a grid where an agent is more likely to interact with other agents situated closer to it than those that are situated afar. The key new results include the surprising acceleration in learning with limited interaction ranges. We also study the effects of pure-strategy players, i.e., nonlearners in the environment. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. In this paper, we propose a new concept - the "Reciprocal Velocity Obstacle"- for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept [3], which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillation- free motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios.