A commonly-used representation for motion prediction of actors is a sequence
of waypoints (comprising positions and orientations) for each actor at discrete
future time-points. While this approach is simple and flexible, it can exhibit
unrealistic higher-order derivatives (such as acceleration) and approximation
errors at intermediate time steps. To address this issue we propose a simple
and general representation for temporally continuous probabilistic trajectory
prediction that is based on polynomial trajectory parameterization. We evaluate
the proposed representation on supervised trajectory prediction tasks using two
large self-driving data sets. The results show realistic higher-order
derivatives and better accuracy at interpolated time-points, as well as the
benefits of the inferred noise distributions over the trajectories. Extensive
experimental studies based on existing state-of-the-art models demonstrate the
effectiveness of the proposed approach relative to other representations in
predicting the future motions of vehicle, bicyclist, and pedestrian traffic
actors.

本文提出一种基于多项式轨迹参数化的时态连续概率轨迹预测表述，并在两个大型自动驾驶数据集上评估了该提出表述。结果表明，该方法预测车辆、自行车和行人未来运动的效果优于其他表述方式。

使用多项式轨迹参数化的时间连续概率预测

Temporally-Continuous Probabilistic Prediction using Polynomial  Trajectory Parameterization

In audio-visual navigation, an agent intelligently travels through a complex,
unmapped 3D environment using both sights and sounds to find a sound source
(e.g., a phone ringing in another room). Existing models learn to act at a
fixed granularity of agent motion and rely on simple recurrent aggregations of
the audio observations. We introduce a reinforcement learning approach to
audio-visual navigation with two key novel elements: 1) waypoints that are
dynamically set and learned end-to-end within the navigation policy, and 2) an
acoustic memory that provides a structured, spatially grounded record of what
the agent has heard as it moves. Both new ideas capitalize on the synergy of
audio and visual data for revealing the geometry of an unmapped space. We
demonstrate our approach on two challenging datasets of real-world 3D scenes,
Replica and Matterport3D. Our model improves the state of the art by a
substantial margin, and our experiments reveal that learning the links between
sights, sounds, and space is essential for audio-visual navigation. Project:
this http URL

本文介绍了一种基于强化学习的音视觉导航方法，通过动态设置和学习的航点和声音记忆，利用视觉和声音数据揭示了未映射空间的几何结构，实验结果表明，学习视听空间之间的联系对于音视觉导航至关重要。