Purpose

To the industrial application of intelligent and connected vehicles (ICVs), the robustness and accuracy of environmental perception are critical in challenging conditions. However, the accuracy of perception is closely related to the performance of sensors configured on the vehicle. To enhance sensors’ performance further to improve the accuracy of environmental perception, this paper aims to introduce an obstacle detection method based on the depth fusion of lidar and radar in challenging conditions, which could reduce the false rate resulting from sensors’ misdetection.

Design/methodology/approach

Firstly, a multi-layer self-calibration method is proposed based on the spatial and temporal relationships. Next, a depth fusion model is proposed to improve the performance of obstacle detection in challenging conditions. Finally, the study tests are carried out in challenging conditions, including straight unstructured road, unstructured road with rough surface and unstructured road with heavy dust or mist.

Findings

The experimental tests in challenging conditions demonstrate that the depth fusion model, comparing with the use of a single sensor, can filter out the false alarm of radar and point clouds of dust or mist received by lidar. So, the accuracy of objects detection is also improved under challenging conditions.

Originality/value

A multi-layer self-calibration method is conducive to improve the accuracy of the calibration and reduce the workload of manual calibration. Next, a depth fusion model based on lidar and radar can effectively get high precision by way of filtering out the false alarm of radar and point clouds of dust or mist received by lidar, which could improve ICVs’ performance in challenging conditions.

中文翻译：

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挑战性条件下基于激光雷达和雷达深度融合的障碍物检测

目的

对于智能网联汽车 (ICV) 的工业应用，环境感知的鲁棒性和准确性在具有挑战性的条件下至关重要。然而，感知的准确性与车辆上配置的传感器的性能密切相关。为了进一步增强传感器的性能以提高环境感知的准确性，本文旨在引入一种基于激光雷达和雷达在具有挑战性的条件下深度融合的障碍物检测方法，该方法可以降低传感器误检测导致的误报率。

设计/方法/方法

首先，提出了一种基于空间和时间关系的多层自校准方法。接下来，提出了一种深度融合模型，以提高具有挑战性的条件下障碍物检测的性能。最后，研究测试是在具有挑战性的条件下进行的，包括笔直的非结构化道路、粗糙表面的非结构化道路以及有大量灰尘或雾气的非结构化道路。

发现

在具有挑战性的条件下的实验测试表明，与使用单个传感器相比，深度融合模型可以过滤掉雷达的误报和激光雷达接收到的灰尘或雾气点云。因此，在具有挑战性的条件下，物体检测的准确性也得到了提高。

原创性/价值

多层自校准方式有利于提高校准的准确性，减少人工校准的工作量。其次，基于激光雷达和雷达的深度融合模型通过滤除雷达误报和激光雷达接收到的灰尘或雾气点云，可以有效地获得高精度，从而提高ICV在具有挑战性的条件下的性能。