Heterogeneous trajectory forecasting is critical for intelligent
transportation systems, while it is challenging because of the difficulty for
modeling the complex interaction relations among the heterogeneous road agents
as well as their agent-environment constraint. In this work, we propose a risk
and scene graph learning method for trajectory forecasting of heterogeneous
road agents, which consists of a Heterogeneous Risk Graph (HRG) and a
Hierarchical Scene Graph (HSG) from the aspects of agent category and their
movable semantic regions. HRG groups each kind of road agents and calculates
their interaction adjacency matrix based on an effective collision risk metric.
HSG of driving scene is modeled by inferring the relationship between road
agents and road semantic layout aligned by the road scene grammar. Based on
this formulation, we can obtain an effective trajectory forecasting in driving
situations, and superior performance to other state-of-the-art approaches is
demonstrated by exhaustive experiments on the nuScenes, ApolloScape, and
Argoverse datasets.

本文提出了一种利用风险和场景图学习方法来预测异构道路代理轨迹的方法，该方法包括异构风险图和层次场景图，具有在驾驶情况下更有效的轨迹预测，并且在 nuScenes，ApolloScape 和 Argoverse 数据集上展示了优越的性能。

通过风险和场景图学习进行异构轨迹预测

Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning

Understanding driving situations regardless the conditions of the traffic
scene is a cornerstone on the path towards autonomous vehicles; however,
despite common sensor setups already include complementary devices such as
LiDAR or radar, most of the research on perception systems has traditionally
focused on computer vision. We present a LiDAR-based 3D object detection
pipeline entailing three stages. First, laser information is projected into a
novel cell encoding for bird's eye view projection. Later, both object location
on the plane and its heading are estimated through a convolutional neural
network originally designed for image processing. Finally, 3D oriented
detections are computed in a post-processing phase. Experiments on KITTI
dataset show that the proposed framework achieves state-of-the-art results
among comparable methods. Further tests with different LiDAR sensors in real
scenarios assess the multi-device capabilities of the approach.

本文提出了一种基于 LiDAR 的三维物体检测算法，包含三个阶段：将激光信息投影到鸟瞰图上、通过卷积神经网络估计物体在平面上的位置和朝向、以及在后处理阶段计算 3D 物体检测。实验结果表明该算法在 KITTI 数据集上达到了与同类方法相当的最优结果，并能在不同实际场景下应用于多个 LiDAR 传感器。