Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Datasets, Methods, and Challenges

Chamola, Vinay; Chougule, Amit; Sam, Aishwarya; Hussain, Amir; Yu, F. Richard

Abstract

The field of autonomous driving research has made significant strides towards achieving full automation, endowing vehicles with self-awareness and independent decision-making. However, integrating automation into vehicular operations presents formidable challenges, especially as these vehicles must seamlessly navigate public roads alongside other cars and pedestrians. An intriguing yet relatively underexplored domain within autonomous driving is overtaking. Overtaking involves a dynamic interplay of complex tasks, including precise steering and speed control, rendering it one of the most intricate operations for implementing augmented intelligence driving technologies. Surprisingly, the overtaking of autonomous vehicles remains largely uncharted territory in the context of augmented intelligence for autonomous systems. This void in knowledge beckons researchers to embark on explorations and investigations in this nascent field. Our review paper systematically synthesises overtaking methodologies hinging on computer vision techniques tailored for augmented intelligence autonomous driving scenarios in response to this pressing need. Our analysis encompasses an array of domains central to overtaking in augmented intelligence autonomous vehicles, encompassing Object Detection, Lane/Line Detection, Depth Estimation, Obstacle Detection, Segmentation, and Pedestrian Detection. We meticulously analyze each domain using well-established Multimodal datasets. We assess different models’ performance across various parameters by employing graphical structures, enabling visual comparative analyses. In object detection, YOLOv4 achieves a top performance with 0.90 mAP on the BDD100K dataset. For lane detection, CLRNET excels with the highest F1 score of around 0.96 on the LLAMAS dataset. ViT-Adapter-L leads in segmentation tasks, boasting an impressive mIoU score of 83 on Cityscapes. The Hierarchical Model achieves a superior mAP of 0.90 in road sign detection on the Tsinghua-Tence...

Citation

Chamola, V., Chougule, A., Sam, A., Hussain, A., & Yu, F. R. (2024). Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Datasets, Methods, and Challenges. IEEE Internet of Things, 11(10), 17911-17933. https://doi.org/10.1109/jiot.2024.3362851

• Journal Article Type: Article
• Acceptance Date: Jan 25, 2024
• Online Publication Date: Apr 3, 2024
• Publication Date: 2024
• Deposit Date: May 9, 2024
• Publicly Available Date: May 16, 2024
• Journal: IEEE Internet of Things Journal
• Print ISSN: 2327-4662
• Electronic ISSN: 2327-4662
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 10
• Pages: 17911-17933
• DOI: https://doi.org/10.1109/jiot.2024.3362851
• Keywords: Autonomous driving, Overtaking mechanism, Computer vision, Augmented Intelligence