Adaptive Unified Monitoring System Design for Tire-Road Information

Abstract

Knowledge of the tire-road information is not only very crucial in many active safety applications but also significant for self-driving cars. The tire-road information mainly consists of tire-road friction coefficient and road-tire friction forces. However, precise measurement of tire-road friction coefficient and tire forces requires expensive equipment. Therefore, the monitoring of tire-road information utilizing either accurate models or improved estimation algorithms is essential. Considering easy availability and good economy, this paper proposes a novel adaptive unified monitoring system (AUMS) to simultaneously observe the tire-road friction coefficient and tire forces, i.e., vertical, longitudinal, and lateral tire forces. First, the vertical tire forces can be calculated considering vehicle body roll and load transfer. The longitudinal and lateral tire forces are estimated by an adaptive unified sliding mode observer (AUSMO). Then, the road-tire friction coefficient is observed through the designed mode-switch observer (MSO). The designed MSO contains two modes: when the vehicle is under driving or brake, a slip slope method (SSM) is used, and a recursive least-squares (RLS) identification method is utilized in the SSM; when the vehicle is under steering, a comprehensive friction estimation method is adopted. The performance of the proposed AUMS is verified by both the matlab/simulinkCarSim co-simulation and the real car experiment. The results demonstrate the effectiveness of the proposed AUMS to provide accurate monitoring of tire-road information.

Acceleration‐based wheel slip control realized with decentralised electric drivetrain systems

“…Therefore, the reference torque adjustment according to Ref. (14) is done for many time-steps. At the end of the first step, the traction torque is not further reduced, and the driven wheel rotational acceleration is also kept around _ ω 0 .…”

“…Many difficulties present when conducting these three steps. Firstly, various road/tire conditions cause many uncertainties regarding the slip ratio to achieve the maximum friction coefficient [14]. The tire non-linear characteristics make the performance of some friction coefficient estimators even worse [15,16].…”

Lane‐keeping system design considering driver's nervousness via scene analysis

“…In the meantime, knowledge of the tire–road information was significant for self‐driving cars. To simultaneously observe the tire–road friction coefficient and tire forces, a novel adaptive unified monitoring system (AUMS) was proposed in [18]. When the vehicle reaches a steady‐state, the yaw rate no longer changes.…”

Active Steering and Driving/Braking Coupled Control Based on Flatness Theory and a Novel Reference Calculation Method

“…The relationship that the equilibrium points vary with the front wheel angle is affected by different longitudinal velocities and tire-road adhesion coefficients. The estimation of vehicle dynamics states such as velocity, side-slip angle and road-tire friction coefficient is important and has been researched extensively [16]- [18]. However, vehicle dynamics states estimation is not the main theme of this paper.…”