TY - GEN
T1 - Collision-Free Path Generation for Teleoperation of Unmanned Vehicles
AU - Kashwani, Fatima
AU - Hassan, Bilal
AU - Khonji, Majid
AU - Dias, Jorge
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Teleoperation, or remote driving, constitutes a crucial transitional phase toward the widespread adoption of fully autonomous vehicles. Nevertheless, to enable seamless real-time teleoperation, it is imperative to address the time delay between the driver and the vehicle. Collision-free path generation has emerged as a vital technique facilitating both teleoperation and autonomous driving, particularly in high-level path planning for vehicles. In the context of real-time teleoperation, a generated collision-free path serves as a valuable guide for the teleoperator, effectively mitigating the impact of time delay. In this research, we present a framework dubbed dual transformer network (DTNet), designed to cater to the needs of teleoperation by addressing road scene understanding. The proposed DTNet employs two transformer-based networks to effectively segment the road free space and detect road objects. Additionally, we introduce an innovative fusion mechanism that leverages the combined information from both networks to predict a collision-free path. The efficacy of the DTNet is extensively evaluated using a large-scale BDD100k dataset, substantiating its superior performance in road free space segmentation and road object detection tasks. Remarkably, DTNet achieves a mean intersection over union score of 83.89% for road free space segmentation and an impressive mean average precision score of 34.20% for road object detection. The experimental findings affirm the effectiveness of the DT-Net framework in addressing the challenges of road scene understanding, making it a promising solution to provide a robust and efficient approach for collision-free path generation, with broader implications for the advancement of autonomous driving technologies.
AB - Teleoperation, or remote driving, constitutes a crucial transitional phase toward the widespread adoption of fully autonomous vehicles. Nevertheless, to enable seamless real-time teleoperation, it is imperative to address the time delay between the driver and the vehicle. Collision-free path generation has emerged as a vital technique facilitating both teleoperation and autonomous driving, particularly in high-level path planning for vehicles. In the context of real-time teleoperation, a generated collision-free path serves as a valuable guide for the teleoperator, effectively mitigating the impact of time delay. In this research, we present a framework dubbed dual transformer network (DTNet), designed to cater to the needs of teleoperation by addressing road scene understanding. The proposed DTNet employs two transformer-based networks to effectively segment the road free space and detect road objects. Additionally, we introduce an innovative fusion mechanism that leverages the combined information from both networks to predict a collision-free path. The efficacy of the DTNet is extensively evaluated using a large-scale BDD100k dataset, substantiating its superior performance in road free space segmentation and road object detection tasks. Remarkably, DTNet achieves a mean intersection over union score of 83.89% for road free space segmentation and an impressive mean average precision score of 34.20% for road object detection. The experimental findings affirm the effectiveness of the DT-Net framework in addressing the challenges of road scene understanding, making it a promising solution to provide a robust and efficient approach for collision-free path generation, with broader implications for the advancement of autonomous driving technologies.
UR - http://www.scopus.com/inward/record.url?scp=85185825717&partnerID=8YFLogxK
U2 - 10.1109/ICAR58858.2023.10406579
DO - 10.1109/ICAR58858.2023.10406579
M3 - Conference contribution
AN - SCOPUS:85185825717
T3 - 2023 21st International Conference on Advanced Robotics, ICAR 2023
SP - 21
EP - 27
BT - 2023 21st International Conference on Advanced Robotics, ICAR 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st International Conference on Advanced Robotics, ICAR 2023
Y2 - 5 December 2023 through 8 December 2023
ER -