Sensors

Research

12 pages, 3339 KiB

Open AccessArticle

Supervised Object-Specific Distance Estimation from Monocular Images for Autonomous Driving

by Yury Davydov, Wen-Hui Chen and Yu-Chen Lin

Sensors 2022, 22(22), 8846; https://doi.org/10.3390/s22228846 - 16 Nov 2022

Cited by 4 | Viewed by 2158

Accurate distance estimation is a requirement for advanced driver assistance systems (ADAS) to provide drivers with safety-related functions such as adaptive cruise control and collision avoidance. Radars and lidars can be used for providing distance information; however, they are either expensive or provide [...] Read more.

Accurate distance estimation is a requirement for advanced driver assistance systems (ADAS) to provide drivers with safety-related functions such as adaptive cruise control and collision avoidance. Radars and lidars can be used for providing distance information; however, they are either expensive or provide poor object information compared to image sensors. In this study, we propose a lightweight convolutional deep learning model that can extract object-specific distance information from monocular images. We explore a variety of training and five structural settings of the model and conduct various tests on the KITTI dataset for evaluating seven different road agents, namely, person, bicycle, car, motorcycle, bus, train, and truck. Additionally, in all experiments, a comparison with the Monodepth2 model is carried out. Experimental results show that the proposed model outperforms Monodepth2 by 15% in terms of the average weighted mean absolute error (MAE). Full article

(This article belongs to the Special Issue Sensing and Semantic Perception in Autonomous Driving)

► Show Figures

Figure 1

15 pages, 4986 KiB

Open AccessArticle

An Efficient Ensemble Deep Learning Approach for Semantic Point Cloud Segmentation Based on 3D Geometric Features and Range Images

by Muhammed Enes Atik and Zaide Duran

Sensors 2022, 22(16), 6210; https://doi.org/10.3390/s22166210 - 18 Aug 2022

Cited by 8 | Viewed by 2421

Abstract

Mobile light detection and ranging (LiDAR) sensor point clouds are used in many fields such as road network management, architecture and urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. Semantic segmentation of mobile point clouds is critical for these [...] Read more.

Mobile light detection and ranging (LiDAR) sensor point clouds are used in many fields such as road network management, architecture and urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. Semantic segmentation of mobile point clouds is critical for these tasks. In this study, we present a robust and effective deep learning-based point cloud semantic segmentation method. Semantic segmentation is applied to range images produced from point cloud with spherical projection. Irregular 3D mobile point clouds are transformed into regular form by projecting the clouds onto the plane to generate 2D representation of the point cloud. This representation is fed to the proposed network that produces semantic segmentation. The local geometric feature vector is calculated for each point. Optimum parameter experiments were also performed to obtain the best results for semantic segmentation. The proposed technique, called SegUNet3D, is an ensemble approach based on the combination of U-Net and SegNet algorithms. SegUNet3D algorithm has been compared with five different segmentation algorithms on two challenging datasets. SemanticPOSS dataset includes the urban area, whereas RELLIS-3D includes the off-road environment. As a result of the study, it was demonstrated that the proposed approach is superior to other methods in terms of mean Intersection over Union (mIoU) in both datasets. The proposed method was able to improve the mIoU metric by up to 15.9% in the SemanticPOSS dataset and up to 5.4% in the RELLIS-3D dataset. Full article

(This article belongs to the Special Issue Sensing and Semantic Perception in Autonomous Driving)

► Show Figures

Figure 1

17 pages, 31428 KiB

Open AccessArticle

Fast and Accurate Lane Detection via Graph Structure and Disentangled Representation Learning

by Yulin He, Wei Chen, Chen Li, Xin Luo and Libo Huang

Sensors 2021, 21(14), 4657; https://doi.org/10.3390/s21144657 - 7 Jul 2021

Cited by 4 | Viewed by 3181

Abstract

It is desirable to maintain high accuracy and runtime efficiency at the same time in lane detection. However, due to the long and thin properties of lanes, extracting features with both strong discrimination and perception abilities needs a huge amount of calculation, which [...] Read more.

It is desirable to maintain high accuracy and runtime efficiency at the same time in lane detection. However, due to the long and thin properties of lanes, extracting features with both strong discrimination and perception abilities needs a huge amount of calculation, which seriously slows down the running speed. Therefore, we design a more efficient way to extract the features of lanes, including two phases: (1) Local feature extraction, which sets a series of predefined anchor lines, and extracts the local features through their locations. (2) Global feature aggregation, which treats local features as the nodes of the graph, and builds a fully connected graph by adaptively learning the distance between nodes, the global feature can be aggregated through weighted summing finally. Another problem that limits the performance is the information loss in feature compression, mainly due to the huge dimensional gap, e.g., from 512 to 8. To handle this issue, we propose a feature compression module based on decoupling representation learning. This module can effectively learn the statistical information and spatial relationships between features. After that, redundancy is greatly reduced and more critical information is retained. Extensional experimental results show that our proposed method is both fast and accurate. On the Tusimple and CULane benchmarks, with a running speed of 248 FPS, F1 values of 96.81% and 75.49% were achieved, respectively. Full article

(This article belongs to the Special Issue Sensing and Semantic Perception in Autonomous Driving)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sensing and Semantic Perception in Autonomous Driving

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (3 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI