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 1886
Special Issue Editor
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
