Teleoperation Enhancement for Autonomous Vehicles Using Estimation Based Predictive Display

Abstract

Teleoperation is increasingly used in the operation of delivery robots and is beginning to be utilized for certain autonomous vehicle intervention applications. This paper addresses the challenges in teleoperation of an autonomous vehicle due to latencies in wireless communication between the remote vehicle and the teleoperator station. Camera video images and Lidar data are typically delayed during wireless transmission but are critical for proper display of the remote vehicle's real-time road environment to the teleoperator. Data collected with experiments in this project show that a 0.5 second delay in real-time display makes it extremely difficult for the teleoperator to control the remote vehicle. This problem is addressed in the paper by using a predictive display (PD) system which provides intermediate updates of the remote vehicle's environment while waiting for actual camera images. The predictive display utilizes estimated positions of the ego vehicle and of other vehicles on the road computed using model-based extended Kalman filters. A crucial result presented in the paper is that vehicle motion models need to be inertial rather than relative and so tracking of other vehicles requires accurate localization of the ego vehicle itself. An experimental study using 5 human teleoperators is conducted to compare teleoperation performance with and without predictive display. A 0.5 second time-delay in camera images makes it impossible to control the vehicle to stay in its lane on curved roads, but the use of the developed predictive display system enables safe remote vehicle control with almost as accurate a performance as the delay-free case.