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Special Issue "Environment Perception for Industrial Robotics, Connected and Autonomous Vehicles and Beyond"

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 October 2023 | Viewed by 5223

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Dr. Suzanne Lesecq

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University Grenoble Alpes, CEA, Leti, F-38000 Grenoble, France
Interests: data fusion; environment perception; mobility
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Special Issue Information

Dear Colleagues,

Environment perception using multiple sensor technologies is currently a key enabler in various applications ranging from industrial robotics to mobility for visually impaired and blind people, but it is also used in connected and autonomous vehicles (CAVs). Several environmental perception technologies already exist, including point cloud analysis and occupancy grid calculation. They differ not only from the sensor modalities used to build digital models of the environment but also from the computational techniques used to extract information from the sensors.

Recently, machine learning (ML) has been used for classifying and training CAVs in different aspects. Both supervised and unsupervised ML algorithms are being used in solving different issues in CAVs. Certainly, there is still a huge research gap and there are many opportunities to develop and improve ML-based techniques to solve many existing challenges in CAVs. Some of the challenges facing ML-based techniques for CAVs are efficient computation, neural architecture, reward functions design, adaptability, generalization, verification and validation, safety, etc.

This Special Issue encourages authors from both academia and industry to submit new research results regarding technological innovations and novel ideas for environmental perception, considering both hardware and software, with a special interest in artificial intelligence and advanced sensors and sensing systems. This Special Issue welcomes submission regarding, but not limited to, the following topics:

Autonomous vehicle contexts;
Efficient ML (deep learning, reinforcement learning, etc.)-based CAV computation and architecture;
Environmental perception;
Object detection;
Occupancy grids;
Point cloud;
Bird view;
Deep learning;
Datasets for environment perception ;
Embedded environmental perception;
Advanced sensors for environmental perception;
Perception systems and devices.

Dr. Suzanne Lesecq
Guest Editor

Manuscript Submission Information

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Keywords

machine learning
environmental perception
perception systems and devices

Published Papers (5 papers)

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Research

Open AccessArticle

Grand Theft Auto-Based Cycling Simulator for Cognitive Enhancement Technologies in Dangerous Traffic Situations

and

Sensors 2023, 23(7), 3672; https://doi.org/10.3390/s23073672 - 31 Mar 2023

Viewed by 614

Abstract

While developing traffic-based cognitive enhancement technology (CET), such as bike accident prevention systems, it can be challenging to test and evaluate them properly. After all, the real-world scenario could endanger the subjects’ health and safety. Therefore, a simulator is needed, preferably one that is realistic yet low cost. This paper introduces a way to use the video game Grand Theft Auto V (GTA V) and its sophisticated traffic system as a base to create such a simulator, allowing for the safe and realistic testing of dangerous traffic situations involving cyclists, cars, and trucks. The open world of GTA V, which can be explored on foot and via various vehicles, serves as an immersive stand-in for the real world. Custom modification scripts of the game give the researchers control over the experiment scenario and the output data to be evaluated. An off-the-shelf bicycle equipped with three sensors serves as a realistic input device for the subject’s movement direction and speed. The simulator was used to test two early-stage CET concepts enabling cyclists to sense dangerous traffic situations, such as trucks approaching from behind the cyclist. Thus, this paper also presents the user evaluation of the cycling simulator and the CET used by the subjects to sense dangerous traffic situations. With the knowledge of the first iteration of the user-centered design (UCD) process, this paper concludes by naming improvements for the cycling simulator and discussing further research directions for CET that enable users to sense dangerous situations better. Full article

(This article belongs to the Special Issue Environment Perception for Industrial Robotics, Connected and Autonomous Vehicles and Beyond)

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Open AccessArticle

Vehicle Detection on Occupancy Grid Maps: Comparison of Five Detectors Regarding Real-Time Performance

and

Sensors 2023, 23(3), 1613; https://doi.org/10.3390/s23031613 - 02 Feb 2023

Viewed by 1083

Abstract

Occupancy grid maps are widely used as an environment model that allows the fusion of different range sensor technologies in real-time for robotics applications. In an autonomous vehicle setting, occupancy grid maps are especially useful for their ability to accurately represent the position of surrounding obstacles while being robust to discrepancies between the fused sensors through the use of occupancy probabilities representing uncertainty. In this article, we propose to evaluate the applicability of real-time vehicle detection on occupancy grid maps. State of the art detectors in sensor-specific domains such as YOLOv2/YOLOv3 for images or PIXOR for LiDAR point clouds are modified to use occupancy grid maps as input and produce oriented bounding boxes enclosing vehicles as output. The five proposed detectors are trained on the Waymo Open automotive dataset and compared regarding the quality of their detections measured in terms of Average Precision (AP) and their real-time capabilities measured in Frames Per Second (FPS). Of the five detectors presented, one inspired from the PIXOR backbone reaches the highest

A P_{0.7}

of 0.82 and runs at 20 FPS. Comparatively, two other proposed detectors inspired from YOLOv2 achieve an almost as good, with a

A P_{0.7}

of 0.79 while running at 91 FPS. These results validate the feasibility of real-time vehicle detection on occupancy grids. Full article

(This article belongs to the Special Issue Environment Perception for Industrial Robotics, Connected and Autonomous Vehicles and Beyond)

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Open AccessArticle

ConcentrateNet: Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant Region Locating Technique

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Sensors 2022, 22(19), 7371; https://doi.org/10.3390/s22197371 - 28 Sep 2022

Viewed by 775

Abstract

This paper proposes a deep learning based object detection method to locate a distant region in an image in real-time. It concentrates on distant objects from a vehicular front camcorder perspective, trying to solve one of the common problems in Advanced Driver Assistance Systems (ADAS) applications, which is, to detect the smaller and faraway objects with the same confidence as those with the bigger and closer objects. This paper presents an efficient multi-scale object detection network, termed as ConcentrateNet to detect a vanishing point and concentrate on the near-distant region. Initially, the object detection model inferencing will produce a larger scale of receptive field detection results and predict a potentially vanishing point location, that is, the farthest location in the frame. Then, the image is cropped near the vanishing point location and processed with the object detection model for second inferencing to obtain distant object detection results. Finally, the two-inferencing results are merged with a specific Non-Maximum Suppression (NMS) method. The proposed network architecture can be employed in most of the object detection models as the proposed model is implemented in some of the state-of-the-art object detection models to check feasibility. Compared with original models using higher resolution input size, ConcentrateNet architecture models use lower resolution input size, with less model complexity, achieving significant precision and recall improvements. Moreover, the proposed ConcentrateNet architecture model is successfully ported onto a low-powered embedded system, NVIDIA Jetson AGX Xavier, suiting the real-time autonomous machines. Full article

(This article belongs to the Special Issue Environment Perception for Industrial Robotics, Connected and Autonomous Vehicles and Beyond)

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Open AccessArticle

Ultra-Wideband Communication and Sensor Fusion Platform for the Purpose of Multi-Perspective Localization

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Sensors 2022, 22(18), 6880; https://doi.org/10.3390/s22186880 - 12 Sep 2022

Viewed by 1089

Abstract

Localization is a keystone for a robot to work within its environment and with other robots. There have been many methods used to solve this problem. This paper deals with the use of beacon-based localization to answer the research question: Can ultra-wideband technology be used to effectively localize a robot with sensor fusion? This paper has developed an innovative solution for creating a sensor fusion platform that uses ultra-wideband communication as a localization method to allow an environment to be perceived and inspected in three dimensions from multiple perspectives simultaneously. A series of contributions have been presented, supported by an in-depth literature review regarding topics in this field of knowledge. The proposed method was then designed, built, and tested successfully in two different environments exceeding its required tolerances. The result of the testing and the ideas formulated throughout the paper were discussed and future work outlined on how to build upon this work in potential academic papers and projects. Full article

(This article belongs to the Special Issue Environment Perception for Industrial Robotics, Connected and Autonomous Vehicles and Beyond)

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Open AccessArticle

Reconstructing Superquadrics from Intensity and Color Images

and

Sensors 2022, 22(14), 5332; https://doi.org/10.3390/s22145332 - 16 Jul 2022

Viewed by 1124

Abstract

The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters. Recent research has shown that deep learning methods can be used to accurately reconstruct random superquadrics from both 3D point cloud data and simple depth images. In this paper, we extended these reconstruction methods to intensity and color images. Specifically, we used a dedicated convolutional neural network (CNN) model to reconstruct a single superquadric from the given input image. We analyzed the results in a qualitative and quantitative manner, by visualizing reconstructed superquadrics as well as observing error and accuracy distributions of predictions. We showed that a CNN model designed around a simple ResNet backbone can be used to accurately reconstruct superquadrics from images containing one object, but only if one of the spatial parameters is fixed or if it can be determined from other image characteristics, e.g., shadows. Furthermore, we experimented with images of increasing complexity, for example, by adding textures, and observed that the results degraded only slightly. In addition, we show that our model outperforms the current state-of-the-art method on the studied task. Our final result is a highly accurate superquadric reconstruction model, which can also reconstruct superquadrics from real images of simple objects, without additional training. Full article

(This article belongs to the Special Issue Environment Perception for Industrial Robotics, Connected and Autonomous Vehicles and Beyond)

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