Forecasting the motion of surrounding vehicles is a critical ability for an
autonomous vehicle deployed in complex traffic. Motion of all vehicles in a
scene is governed by the traffic context, i.e., the motion and relative spatial
configuration of neighboring vehicles. In this paper we propose an LSTM
encoder-decoder model that uses convolutional social pooling as an improvement
to social pooling layers for robustly learning interdependencies in vehicle
motion. Additionally, our model outputs a multi-modal predictive distribution
over future trajectories based on maneuver classes. We evaluate our model using
the publicly available NGSIM US-101 and I-80 datasets. Our results show
improvement over the state of the art in terms of RMS values of prediction
error and negative log-likelihoods of true future trajectories under the
model's predictive distribution. We also present a qualitative analysis of the
model's predicted distributions for various traffic scenarios.

使用卷积社交汇集的 LSTM 编码器 - 解码器模型可以使得机器人更好地学习掌握复杂路况下的周围车辆的运动，并在多种交通场景中获得了不错的预测效果。

基于卷积社交池化的车辆轨迹预测

Convolutional Social Pooling for Vehicle Trajectory Prediction

We present a self-supervised approach to ignoring "distractors" in camera
images for the purposes of robustly estimating vehicle motion in cluttered
urban environments. We leverage offline multi-session mapping approaches to
automatically generate a per-pixel ephemerality mask and depth map for each
input image, which we use to train a deep convolutional network. At run-time we
use the predicted ephemerality and depth as an input to a monocular visual
odometry (VO) pipeline, using either sparse features or dense photometric
matching. Our approach yields metric-scale VO using only a single camera and
can recover the correct egomotion even when 90% of the image is obscured by
dynamic, independently moving objects. We evaluate our robust VO methods on
more than 400km of driving from the Oxford RobotCar Dataset and demonstrate
reduced odometry drift and significantly improved egomotion estimation in the
presence of large moving vehicles in urban traffic.

本文提出了一种自监督方法，可忽略城市环境下摄像头图像中的 “干扰物”，从而鲁棒地估计车辆运动情况。我们利用离线多会话映射方法自动生成每个输入图像的像素级时间变化掩模和深度图，用于训练深度卷积网络和视觉里程计管道，最终实现了在动态、独立运动对象遮挡的情况下使用单目摄像头完成度量尺度的 VO，并且在超过 400 公里的牛津机器人数据集中获得了显著改善的运动估计精度。

自我监督干扰学习驱动的城市环境单目视觉里程计鲁棒性

Driven to Distraction: Self-Supervised Distractor Learning for Robust  Monocular Visual Odometry in Urban Environments

Motion is an important signal for agents in dynamic environments, but
learning to represent motion from unlabeled video is a difficult and
underconstrained problem. We propose a model of motion based on elementary
group properties of transformations and use it to train a representation of
image motion. While most methods of estimating motion are based on pixel-level
constraints, we use these group properties to constrain the abstract
representation of motion itself. We demonstrate that a deep neural network
trained using this method captures motion in both synthetic 2D sequences and
real-world sequences of vehicle motion, without requiring any labels. Networks
trained to respect these constraints implicitly identify the image
characteristic of motion in different sequence types. In the context of vehicle
motion, this method extracts information useful for localization, tracking, and
odometry. Our results demonstrate that this representation is useful for
learning motion in the general setting where explicit labels are difficult to
obtain.

提出了一个以变换的群属性为基础的运动模型，使用它训练了一个图像运动的表示方法。使用此方法训练的深度神经网络对合成的 2D 序列和真实世界的车辆运动序列都捕捉到了运动特征；在车辆运动的背景下，此方法提取了用于定位、跟踪和测距的信息。这一表示方法在很难获取明确标签的情况下学习运动非常有用。