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. / Doctor of Philosophy / This dissertation revolves around the teleoperation of autonomous vehicles (AVs), with the objective of formulating a comprehensive teleoperation system that encompasses two critical aspects: direct control and indirect control. In the initial segment of the dissertation, we introduce a real-time teleoperation direct control system based on neural networks. This framework plays a pivotal role in assisting operators in navigating AVs efficiently, especially in the face of challenges such as communication delays and complex external environments. Following this, we present a novel saliency-based object detection (OD) algorithm. This algorithm empowers the AV to recognize potential objects beyond its prior knowledge, thereby enhancing its level of autonomous control, particularly when operators opt not to exercise direct control over the remote AV. Our research findings delve into the essential facets of AV teleoperation. The developed teleoperation system serves as a valuable reference for future researchers and engineers dedicated to advancing autonomous vehicle technology.