Machine Learning in Autonomous Driving

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 1807

Special Issue Editor


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Guest Editor
Department of Computer Science and Engineering, Konkuk University, Seoul, Korea
Interests: machine learning; deep learning; big data; autonomous driving; intelligent systems

Special Issue Information

Dear Colleagues,

Due to the recent advances in artificial intelligence technology, autonomous driving vehicles (a.k.a. autonomous vehicles) are becoming more popular and having a greater impact on our everyday life. Modern autonomous vehicles are equipped with various sensors, such as cameras, LiDAR, and ultrasonic sensors, to perceive environmental data and that use machine learning techniques to detect surrounding objects, predict trajectory, aware driving situation, decide appropriate actions, and control vehicle actuators based on these perceived data. However, even the latest autonomous vehicles are still experiencing difficulties in interpretation and decision when facing unpredictable situations and unknown environments.

This Special Issue aims to present recent advances and challenges in the application of machine learning technology in autonomous driving, including in driving data preparation, object detection, trajectory prediction, driving situation awareness, vehicle localization, driving action planning, and vehicle control. This would be a good opportunity to gather researchers in developing machine learning models and algorithms for autonomous driving to discuss and share original research works and practical experiences.

Prof. Dr. Young-guk Ha
Guest Editor

Manuscript Submission Information

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Keywords

  • preprocessing of driving data
  • anonymity and privacy of driving data
  • vehicle, pedestrian and lane detection
  • vehicle tracking and trajectory prediction
  • pedestrian and crowd trajectory prediction
  • driving situation awareness
  • vehicle SLAM and path planning
  • traffic prediction and route optimization
  • driving action planning and control
  • vehicle anomaly detection and recovery
  • driver status detection and interaction

Published Papers (1 paper)

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Research

14 pages, 9443 KiB  
Article
StairWave Transformer: For Fast Utilization of Recognition Function in Various Unmanned Vehicles
by Donggyu Choi, Chang-eun Lee, Jaeuk Baek, Seungwon Do, Sungwoo Jun, Kwang-yong Kim and Young-guk Ha
Machines 2023, 11(12), 1068; https://doi.org/10.3390/machines11121068 - 04 Dec 2023
Viewed by 866
Abstract
Newly introduced vehicles come with various added functions, each time utilizing data from different sensors. One prominent related function is autonomous driving, which is performed in cooperation with multiple sensors. These sensors mainly include image sensors, depth sensors, and infrared detection technology for [...] Read more.
Newly introduced vehicles come with various added functions, each time utilizing data from different sensors. One prominent related function is autonomous driving, which is performed in cooperation with multiple sensors. These sensors mainly include image sensors, depth sensors, and infrared detection technology for nighttime use, and they mostly generate data based on image processing methods. In this paper, we propose a model that utilizes a parallel transformer design to gradually reduce the size of input data in a manner similar to a stairway, allowing for the effective use of such data and efficient learning. In contrast to the conventional DETR, this model demonstrates its capability to be trained effectively with smaller datasets and achieves rapid convergence. When it comes to classification, it notably diminishes computational demands, scaling down by approximately 6.75 times in comparison to ViT-Base, all the while maintaining an accuracy margin of within ±3%. Additionally, even in cases where sensor positions may exhibit slight misalignment due to variations in data input for object detection, it manages to yield consistent results, unfazed by the differences in the field of view taken into consideration. The proposed model is named Stairwave and is characterized by a parallel structure that retains a staircase-like form. Full article
(This article belongs to the Special Issue Machine Learning in Autonomous Driving)
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