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Advances in Intelligent Autonomous Vehicle L4&5 Technologies: Localization and Mapping in Challenging Road Structures and Adverse Weather Conditions

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

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 5725

Special Issue Editor

Dr. Mohammad Aldibaja

E-Mail Website
Guest Editor
Sensing and Perception, SMART Mechatronics Research Group, Saxion University of Applied Sciences, Enschede, The Netherlands
Interests: autonomous vehicles; LIDAR/radar-based localization systems; mapping systems; SLAM technologies; eye-based human‒machine interface systems; driver monitoring systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mapping and localization are important pillars to enable autonomous driving. In levels four and five, the precise mapping of critical environments, such as high buildings, dense trees, long tunnels, multilayer junctions, underpasses, and bridges, is very challenging and must deploy autonomous vehicles in modern cities regardless of the complexity of road structures. In addition, implementing online mapping modules is very important to reduce the cost of building maps and significantly enables extending the map size using many autonomous agents. Thus, the accurate localization inside the precisely generated maps in adverse weather factors and critical environmental conditions, such as snow, wet, old, grass, foggy, and shoveled pavements, is a dominant demand to allow for safe autonomous driving and elevate the quality into levels four and five.

Obviously, deep learning techniques can play a significant role to improve mapping and localization accuracy. On the other hand, the integration is very tricky due to the safety concerns. Accordingly, the ultimate goal is to make this issue (via publications) as a reference to highlight the requirements, problems and the relevant solutions to implement robust and reliable mapping and localization systems for levels four and five of autonomous driving while taking into account the importance of the online and cloud source functionalities as well as the deep learning integration. Some suggested outlines (but not limited to) can be as follows:

  • Online Mapping Systems.
  • Cloud-source-based Mapping Systems.
  • Precise Mapping in Unstructured Roads.
  • Deep-Learning-based Localization Systems.
  • Accurate Localization in Adverse Weather Conditions.
  • Simultaneous Mapping and Localization Systems (SLAM).
  • Safety Analysis and Requirements of Localization and Mapping in Levels Four and Five of Autonomous Driving.
  • Road Pavement Assessment. 
  • Map Quality Assessment.

Dr. Mohammad Aldibaja
Guest Editor

Manuscript Submission Information

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Published Papers (2 papers)

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19 pages, 16359 KiB  
Article
Waypoint Transfer Module between Autonomous Driving Maps Based on LiDAR Directional Sub-Images
by Mohammad Aldibaja, Ryo Yanase and Naoki Suganuma
Sensors 2024, 24(3), 875; https://doi.org/10.3390/s24030875 - 29 Jan 2024
Cited by 3 | Viewed by 1831
Abstract
Lane graphs are very important for describing road semantics and enabling safe autonomous maneuvers using the localization and path-planning modules. These graphs are considered long-life details because of the rare changes occurring in road structures. On the other hand, the global position of [...] Read more.
Lane graphs are very important for describing road semantics and enabling safe autonomous maneuvers using the localization and path-planning modules. These graphs are considered long-life details because of the rare changes occurring in road structures. On the other hand, the global position of the corresponding topological maps might be changed due to the necessity of updating or extending the maps using different positioning systems such as GNSS/INS-RTK (GIR), Dead-Reckoning (DR), or SLAM technologies. Therefore, the lane graphs should be transferred between maps accurately to describe the same semantics of lanes and landmarks. This paper proposes a unique transfer framework in the image domain based on the LiDAR intensity road surfaces, considering the challenging requirements of its implementation in critical road structures. The road surfaces in a target map are decomposed into directional sub-images with X, Y, and Yaw IDs in the global coordinate system. The XY IDs are used to detect the common areas with a reference map, whereas the Yaw IDs are utilized to reconstruct the vehicle trajectory in the reference map and determine the associated lane graphs. The directional sub-images are then matched to the reference sub-images, and the graphs are safely transferred accordingly. The experimental results have verified the robustness and reliability of the proposed framework to transfer lane graphs safely and accurately between maps, regardless of the complexity of road structures, driving scenarios, map generation methods, and map global accuracies. Full article
(This article belongs to the Special Issue Advances in Intelligent Autonomous Vehicle L4&5 Technologies: Localization and Mapping in Challenging Road Structures and Adverse Weather Conditions)
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17 pages, 1587 KiB  
Article
Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System
by Xue-Bo Jin, Wei Chen, Hui-Jun Ma, Jian-Lei Kong, Ting-Li Su and Yu-Ting Bai
Sensors 2023, 23(20), 8650; https://doi.org/10.3390/s23208650 - 23 Oct 2023
Cited by 3 | Viewed by 3361
Abstract
GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is [...] Read more.
GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications. Full article
(This article belongs to the Special Issue Advances in Intelligent Autonomous Vehicle L4&5 Technologies: Localization and Mapping in Challenging Road Structures and Adverse Weather Conditions)
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