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Multimodal Sensing for Vehicle Detection and Tracking
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Multimodal Sensing for Vehicle Detection and Tracking

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 6665

Special Issue Editor

Dr. Stefano Quer

E-Mail Website
Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: high-performance computing; formal methods; autonomous vehicles; SIMD and SIMT architectures; algorithms for path planning and connectivity; software applications; algorithms and data structures (divide-and-conquer, optimization, estimation, etc.)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vehicle location and vehicle tracking are significant topics. However, if only single modal data are available, bias and limited accuracy can be difficult to tackle. Biological organisms use eyes and ears to understand the world. Similarly, different types of sensors may provide distinct observations of ongoing activities.

Microphone arrays have a 360-degree field of sensing and can provide geometrical information on the location. However, acoustic devices offer limited tracking accuracy and have difficulties identifying the number of objects in the area. Video cameras provide accurate estimates of the direction of arrival and can easily discover the number of moving objects. However, they have a limited field of view, and their use is difficult when the image size is small or not enough features can be extracted from it. As a consequence, fusing multimodal sensor data has recently attracted a lot of attention in vehicle detection and tracking. However, many problems in this framework (e.g., how to efficiently fuse audio and visual data, how to effectively use low-quality sensors in outdoor scenarios) are still unsolved and require further analysis. This Special Issue will focus on the above topics with the purpose of gathering new ideas in the area and collecting relevant practical experiences.

Dr. Stefano Quer
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensor fusion
  • multimodal data fusion
  • vehicle detection
  • vehicle tracking
  • surveillance
  • imaging
  • acoustics

Published Papers (3 papers)

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Research

27 pages, 6779 KiB  
Article
Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions
by Isaac Ogunrinde and Shonda Bernadin
Sensors 2023, 23(14), 6255; https://doi.org/10.3390/s23146255 - 09 Jul 2023
Cited by 1 | Viewed by 2240
Abstract
AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and [...] Read more.
AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persists. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image onto the camera image. Using the attention mechanism, we emphasized and improved the important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our results show that the proposed method significantly enhances the detection of small and distant objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed, with an accuracy of 0.849 at 69 fps. Full article
(This article belongs to the Special Issue Multimodal Sensing for Vehicle Detection and Tracking)
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16 pages, 1684 KiB  
Article
Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization
by Suresh Kolekar, Shilpa Gite, Biswajeet Pradhan and Abdullah Alamri
Sensors 2022, 22(24), 9677; https://doi.org/10.3390/s22249677 - 10 Dec 2022
Cited by 7 | Viewed by 2596
Abstract
The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant [...] Read more.
The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models. Full article
(This article belongs to the Special Issue Multimodal Sensing for Vehicle Detection and Tracking)
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20 pages, 2363 KiB  
Article
Multi-Target State and Extent Estimation for High Resolution Automotive Sensor Detections
by Andinet Hunde
Sensors 2022, 22(21), 8415; https://doi.org/10.3390/s22218415 - 02 Nov 2022
Viewed by 1195
Abstract
This paper discusses the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. Target tracking problem is formulated as a two hierarchical layer problem—on the first layer, a multi-target tracking problem based on multiple detections is [...] Read more.
This paper discusses the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. Target tracking problem is formulated as a two hierarchical layer problem—on the first layer, a multi-target tracking problem based on multiple detections is distinguished in the measurement space, and on the second (top) layer, group target tracking with birth and death as well as merging and splitting of group target tracks as they evolve in a dynamic scene is represented. This configuration enhances the multi-target tracking performance in situations including but not limited to target initialization(birth), target occlusion, missed detections, unresolved measurement, target maneuver, etc. In addition, group tracking exposes complex individual target interactions to help in situation assessment which is challenging to capture otherwise. Full article
(This article belongs to the Special Issue Multimodal Sensing for Vehicle Detection and Tracking)
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