Human Intervention Detection on a Steering Actuation System in Autonomous Vehicles Chang'an University and Clemson University

Format:

Conference/Event

Author/Creator:

Wang, Wang, author.

Contributor:

Guo, Longxiang

Jia, Yunyi

Conference Name:

WCX World Congress Experience (2018-04-10 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2018

Summary:

AbstractHuman steering intervention is an important factor for the safety and control performance of autonomous vehicles. Accurate identification of human steering torque will enable human drivers to take over the controls from the autonomous driving system whenever they require or intend to. However, in the take-over process, both the human driver and actuator motor will apply active torques simultaneously on the steering wheel, thus the human torque cannot be detected by using a torque sensor due to the coupled torques. Therefore, effective estimation though the system dynamics can be an alternative measure to achieve the detection and a comparatively accurate quantification of the human steering intervention torque. In this paper, an online estimation strategy of human steering intervention torque for the steering actuation system of an autonomous vehicle is presented. The dynamic model of the steering actuation system is firstly established. The human steering torque estimation algorithm is then devised. To eliminate the usage of angular acceleration signal, an auxiliary variable is introduced to modify the algorithm. The stability condition of the algorithm and its assumption are analyzed afterwards. As a critical parameter, the influence of the estimation gain upon the estimation performance is discussed. Furthermore, three typical steering intervention cases are designed and simulated to evaluate the performance of the proposed estimator. Simulation results validate the effectiveness of the proposed approach to estimate human steering invention torques. These results show that the designed estimator can be applied in autonomous driving systems for multiple purposes, such as driver intent inference, Advanced Driver Assistance System, and other applications regarding human-vehicle interaction

Notes:

Vendor supplied data

Publisher Number:

2018-01-0767

Access Restriction:

Restricted for use by site license