3D object detection from LiDAR sensor data is an important topic in the
context of autonomous cars and drones. In this paper, we present the results of
experiments on the impact of backbone selection of a deep convolutional neural
network on detection accuracy and computation speed. We chose the PointPillars
network, which is characterised by a simple architecture, high speed, and
modularity that allows for easy expansion. During the experiments, we paid
particular attention to the change in detection efficiency (measured by the mAP
metric) and the total number of multiply-addition operations needed to process
one point cloud. We tested 10 different convolutional neural network
architectures that are widely used in image-based detection problems. For a
backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a
1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration
of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated
that it is possible to significantly speed up a 3D object detector in LiDAR
point clouds with a small decrease in detection efficiency. This result can be
used when PointPillars or similar algorithms are implemented in embedded
systems, including SoC FPGAs. The code is available at
this https URL

本研究探讨了深度卷积神经网络背骨选择对于基于 LiDAR 传感器数据的 3D 目标检测精度和计算速度的影响，测试了 10 种不同的卷积神经网络架构，并在保证检测效率（以 mAP 度量）的前提下，显著提高 LiDAR 点云中 3D 物体检测速度。

选取 PointPillars 主干类型以实现快速准确的 LiDAR 物体检测

PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object  Detection

One of the critical pieces of the self-driving puzzle is understanding the
surroundings of a self-driving vehicle (SDV) and predicting how these
surroundings will change in the near future. To address this task we propose
MultiXNet, an end-to-end approach for detection and motion prediction based
directly on lidar sensor data. This approach builds on prior work by handling
multiple classes of traffic actors, adding a jointly trained second-stage
trajectory refinement step, and producing a multimodal probability distribution
over future actor motion that includes both multiple discrete traffic behaviors
and calibrated continuous position uncertainties. The method was evaluated on
large-scale, real-world data collected by a fleet of SDVs in several cities,
with the results indicating that it outperforms existing state-of-the-art
approaches.

通过使用激光雷达传感器数据，我们提出了一种名为 MultiXNet 的自动驾驶汽车感知和预测方法，可处理多种交通行为，包括多模态概率分布和轨迹精细矫正等，结果表明它优于现有的最先进方法。

MultiXNet: 多类别、多阶段、多模态运动预测

MultiXNet: Multiclass Multistage Multimodal Motion Prediction

Despite rapid developments in visual image-based road detection, robustly
identifying road areas in visual images remains challenging due to issues like
illumination changes and blurry images. To this end, LiDAR sensor data can be
incorporated to improve the visual image-based road detection, because LiDAR
data is less susceptible to visual noises. However, the main difficulty in
introducing LiDAR information into visual image-based road detection is that
LiDAR data and its extracted features do not share the same space with the
visual data and visual features. Such gaps in spaces may limit the benefits of
LiDAR information for road detection. To overcome this issue, we introduce a
novel Progressive LiDAR Adaptation-aided Road Detection (PLARD) approach to
adapt LiDAR information into visual image-based road detection and improve
detection performance. In PLARD, progressive LiDAR adaptation consists of two
subsequent modules: 1) data space adaptation, which transforms the LiDAR data
to the visual data space to align with the perspective view by applying
altitude difference-based transformation; and 2) feature space adaptation,
which adapts LiDAR features to visual features through a cascaded fusion
structure. Comprehensive empirical studies on the well-known KITTI road
detection benchmark demonstrate that PLARD takes advantage of both the visual
and LiDAR information, achieving much more robust road detection even in
challenging urban scenes. In particular, PLARD outperforms other
state-of-the-art road detection models and is currently top of the publicly
accessible benchmark leader-board.

本文提出 Progressive LiDAR Adaptation-aided Road Detection (PLARD) 方法，通过数据空间适应和特征空间适应两个子模块，将 LiDAR 信息与视觉图像信息融合，显著提高道路监测的性能，在 KITTI 路面检测数据集上性能媲美甚至超过其他先进的道路监测模型。