Teleoperation System for Autonomous Vehicles

Despite the advancements in the development of autonomous vehicles (AVs), there are still numerous complex situations in which AVs may encounter challenges. In recent years, the concept of teleoperation, which entails establishing a connection between a remote operator and the AV, has garnered substantial attention from both AV companies and governmental bodies as a viable safety backup method. However, a research gap is apparent when it comes to the remote manipulation of AVs positioned at a considerable distance. This gap involves a) AV with a temporal delay through real-time direct control within the constraints of current wireless communication technology in an unpredictable road environment, and b) enhancing the AV's inherent detection capabilities to augment its autonomous control abilities, thereby reducing the operator's workload. To address this research gap, this dissertation introduces an innovative teleoperation system. Initially, we devise a control system utilizing the wave variable approach as a communication method to alleviate the impact of signal latency. And Radial Basis Function Networks (RBFN) are employed to effectively manage the uncertain nonlinear dynamics of the vehicle. Subsequently, a saliency-based object detection (OD) algorithm, named SalienDet, is proposed to identify objects not present in the training sample set. SalienDet incorporates saliency maps generated without prior information into the neural network, enhancing image features for unfamiliar objects. This augmentation significantly aids the OD algorithm in detecting previously unknown objects, thereby empowering the AV to possess an improved perception ability. This advancement is particularly valuable when the operator imparts driving advice to the AV instead of exercising direct control. In conclusion, this dissertation makes a noteworthy contribution to AV teleoperation by furnishing a comprehensive solution that spans various aspects of AV teleoperation.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Ding, Ning

Chair dc:contributor.committeechair

• Eskandarian, Azim

Committee members dc:contributor.committeemember

• Taheri, Saied
• Abbas, Montasir M.
• Akbari Hamed, Kaveh

• Teleoperation
• Radial Basis Function Network
• Open-Set Object Detection
• Open-World Object Detection
• Autonomous Vehicle

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40847

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119048