Design of an improved predictive LTR for rollover warning systems

Affiliation of Author(s):能源与动力学院

Journal:JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING

Key Words:Rollover warning system Rollover index Preview driver model IPLTR

Abstract:Rollover warning system predicts whether the vehicle has a risk of rollover in a period of time based on the current driving state of the vehicle. Most previous studies on rollover evaluation index have only used vehicle's current states and often ignored the judgment of the overall tendency of the motion. In the calculation of the rollover early warning algorithm, it is assumed that the vehicle input was known. Based on the above reasons, this paper introduces the derivative of LTR into the rollover index, using an improved predictive LTR (IPLTR) as the rollover index, and realized the real-time calculation through an 8-DOF nonlinear vehicle model. The vehicle rollover prediction system is achieved through the preview driver model and the rollover prediction index calculation model. To achieve a proper preview time for the driver model, a comprehensive evaluation formula that considers the trajectory deviation, the steering wheel rotation speed and lateral acceleration is used. Finally, an IPLTR is developed, which utilizes a driver model to predict the steering angle and several other sensor signals available from the electronic stability control system as inputs. The new IPLTR index can provide a time-advanced measure of rollover propensity and its effectiveness is verified by virtual tests and experimental test data. Simulation results by double lane change (DLC) and "Highway Merge" road maneuvers show that the proposed rollover warning algorithm is rather satisfying. Experimental test by pylon course slalom maneuver also proves its effectiveness and has more advantages of such a system in rollover prevention.

ISSN No.:1678-5878

Translation or Not:no

Date of Publication:2017-10-01

Co-author:李海青,王浩宇,l

Correspondence Author:Zhao Youqun

Date of Publication:2017-10-01