Opinion Dynamics-Based Vehicle Velocity Estimation and Diagnosis

An opinion dynamics approach is proposed to enhance the reliability of the vehicle velocity estimators, which are required for autonomous driving as well as advanced vehicle active safety systems, such as traction and stability control. The corners’ estimates of a velocity observer, which is formed by combining the kinematic and model-based estimation schemes, are used as opinions with different levels of confidence in the developed algorithm. This is to find more reliable estimates robust to disturbances and time delay via solving a convex optimization problem. To bypass the effect of failure in velocity estimation, a fault rejection policy is used concurrently with the opinion dynamics. Road tests confirm the validity and robustness of the algorithm on slippery and dry roads independent of the powertrain configuration in different driving scenarios, especially for combined-slip and low-excitation maneuvers, which are demanding for the current vehicle state estimators.

[1]  Shreyas Sundaram,et al.  Resilient Asymptotic Consensus in Robust Networks , 2013, IEEE Journal on Selected Areas in Communications.

[2]  Sean N. Brennan,et al.  Lateral Vehicle State and Environment Estimation Using Temporally Previewed Mapped Lane Features , 2015, IEEE Transactions on Intelligent Transportation Systems.

[3]  J. Christian Gerdes,et al.  Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control , 2004 .

[4]  Xiaoyu Huang,et al.  Robust Sideslip Angle Estimation for Lightweight Vehicles Using Smooth Variable Structure Filter , 2013 .

[5]  Rajesh Rajamani,et al.  Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction Coefficients , 2012 .

[6]  Keith J. Burnham,et al.  Dual extended Kalman filter for vehicle state and parameter estimation , 2006 .

[7]  Huei Peng,et al.  Robust Vehicle Sideslip Angle Estimation Through a Disturbance Rejection Filter That Integrates a Magnetometer With GPS , 2014, IEEE Transactions on Intelligent Transportation Systems.

[8]  Yong Xu,et al.  A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations , 2014 .

[9]  S. Glaser,et al.  Vehicle lateral dynamics estimation using unknown input proportional-integral observers , 2006, 2006 American Control Conference.

[10]  Shreyas Sundaram,et al.  Coherence and convergence rate in networked dynamical systems , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[11]  Noah E. Friedkin,et al.  The Problem of Social Control and Coordination of Complex Systems in Sociology: A Look at the Community Cleavage Problem , 2015, IEEE Control Systems.

[12]  Rainer Hegselmann,et al.  Opinion dynamics and bounded confidence: models, analysis and simulation , 2002, J. Artif. Soc. Soc. Simul..

[13]  Shreyas Sundaram,et al.  Robustness of Leader–Follower Networked Dynamical Systems , 2016, IEEE Transactions on Control of Network Systems.

[14]  Yoichi Hori,et al.  Lateral Stability Control of In-Wheel-Motor-Driven Electric Vehicles Based on Sideslip Angle Estimation Using Lateral Tire Force Sensors , 2012, IEEE Transactions on Vehicular Technology.

[15]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[16]  Amir Khajepour,et al.  Real-time estimation of the road bank and grade angles with unknown input observers , 2017 .

[17]  J. Christian Gerdes,et al.  Estimation of Tire Slip Angle and Friction Limits Using Steering Torque , 2010, IEEE Transactions on Control Systems Technology.

[18]  Amir Khajepour,et al.  Integrated estimation structure for the tire friction forces in ground vehicles , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[19]  Amir Khajepour,et al.  Integrated stability and traction control for electric vehicles using model predictive control , 2016 .

[20]  Huei Peng,et al.  A Cost-Effective Sideslip Estimation Method Using Velocity Measurements From Two GPS Receivers , 2014, IEEE Transactions on Vehicular Technology.

[21]  Carlos Canudas-de-Wit,et al.  Dynamic Friction Models for Road/Tire Longitudinal Interaction , 2003 .

[22]  J. Christian Gerdes,et al.  Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability , 2016, IEEE Transactions on Intelligent Transportation Systems.

[23]  Tor Arne Johansen,et al.  Vehicle velocity estimation using nonlinear observers , 2006, Autom..

[24]  Ali Charara,et al.  Estimation of vehicle sideslip, tire force and wheel cornering stiffness , 2009 .

[25]  Amir Khajepour,et al.  Longitudinal vehicle state estimation using nonlinear and parameter-varying observers , 2017 .

[26]  Shreyas Sundaram,et al.  A Notion of Robustness in Complex Networks , 2015, IEEE Transactions on Control of Network Systems.

[27]  Ali Charara,et al.  Observers for vehicle tyre/road forces estimation: experimental validation , 2010 .

[28]  Tobias Ortmaier,et al.  State estimation of vehicle's lateral dynamics using unscented Kalman filter , 2014, 53rd IEEE Conference on Decision and Control.

[29]  M. Buslowicz,et al.  Simple stability criterion for a class of delay differential systems , 1987 .

[30]  Amir Khajepour,et al.  Corner-based estimation of tire forces and vehicle velocities robust to road conditions , 2017 .

[31]  Amir Khajepour,et al.  Robust Estimation and Experimental Evaluation of Longitudinal Friction Forces in Ground Vehicles , 2014 .

[32]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

[33]  Liang Tong.  An Approach for Vehicle State Estimation Using Extended Kalman Filter , 2012 .