Trajectory prediction modules are key enablers for safe and efficient
planning of autonomous vehicles (AVs), particularly in highly interactive
traffic scenarios. Recently, learning-based trajectory predictors have
experienced considerable success in providing state-of-the-art performance due
to their ability to learn multimodal behaviors of other agents from data. In
this paper, we present an algorithm called multi-predictor fusion (MPF) that
augments the performance of learning-based predictors by imbuing them with
motion planners that are tasked with satisfying logic-based rules. MPF
probabilistically combines learning- and rule-based predictors by mixing
trajectories from both standalone predictors in accordance with a belief
distribution that reflects the online performance of each predictor. In our
results, we show that MPF outperforms the two standalone predictors on various
metrics and delivers the most consistent performance.

本文介绍了一种称为 MPF 的算法，它通过将遵循基于逻辑的规则的运动规划和基于学习的预测器相结合，在高度交互式交通场景中提高了学习基础预测器的性能。结果显示，在各种指标上，MPF 优于两个独立预测器，并提供最一致的性能。

多预测器融合：结合基于学习和基于规则的轨迹预测器

Multi-Predictor Fusion: Combining Learning-based and Rule-based  Trajectory Predictors

We present iGibson 1.0, a novel simulation environment to develop robotic
solutions for interactive tasks in large-scale realistic scenes. Our
environment contains 15 fully interactive home-sized scenes with 108 rooms
populated with rigid and articulated objects. The scenes are replicas of
real-world homes, with distribution and the layout of objects aligned to those
of the real world. iGibson 1.0 integrates several key features to facilitate
the study of interactive tasks: i) generation of high-quality virtual sensor
signals (RGB, depth, segmentation, LiDAR, flow and so on), ii) domain
randomization to change the materials of the objects (both visual and physical)
and/or their shapes, iii) integrated sampling-based motion planners to generate
collision-free trajectories for robot bases and arms, and iv) intuitive
human-iGibson interface that enables efficient collection of human
demonstrations. Through experiments, we show that the full interactivity of the
scenes enables agents to learn useful visual representations that accelerate
the training of downstream manipulation tasks. We also show that iGibson 1.0
features enable the generalization of navigation agents, and that the
human-iGibson interface and integrated motion planners facilitate efficient
imitation learning of human demonstrated (mobile) manipulation behaviors.
iGibson 1.0 is open-source, equipped with comprehensive examples and
documentation. For more information, visit our project website:
this http URL

iGibson 1.0 是一个新型模拟环境，包含 15 个完全互动的具有 108 个房间的真实场景，为交互任务开发机器人解决方案提供了便利，同时集成了多个关键功能，包括动作规划和感官信号生成等，旨在为下游处理任务提供加速训练的可视化表示学习方案。