An Adaptive Tire Model for Enhanced Vehicle Control Systems

2015 ◽  
Vol 8 (1) ◽  
pp. 128-145 ◽  
Author(s):  
Kanwar Bharat Singh ◽  
Srikanth Sivaramakrishnan
Keyword(s):  
Control Systems ◽  
Vehicle Control ◽  
Tire Model

The Influence of In-Vehicle Noise on Speech Recognition for Automotive Voice-Activated Control Systems

2002 ◽  
Vol 33 (9) ◽  
pp. 19-23
Author(s):  
By Dave G. Fish
Keyword(s):  
Speech Recognition ◽  
Control Systems ◽  
Convenient Method ◽  
Vehicle Control ◽  
Climate Control ◽  
High Demand ◽  
Vehicle Noise ◽  
Control Functions ◽  
Wide Range ◽  
Vehicle Systems

Voice activation provides a safe and convenient method of controlling vehicle systems such as in-car entertainment, telecommunications and climate control. In the fullness of time it is likely that there will be a high demand across all vehicle classes for such systems for a wide range of vehicle control functions. One of the challenges facing their development and introduction into vehicles is that of the in-vehicle noise environment.

scholarly journals Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Li ◽  
Jiaxu Zhang
Keyword(s):  
Kalman Filter ◽  
Control Systems ◽  
Interacting Multiple Model ◽  
Square Root ◽  
Tire Model ◽  
Multiple Model ◽  
Sideslip Angle ◽  
Safety Control ◽  
Cubature Kalman Filter ◽  
Vehicle Dynamic Model

Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF), which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF), which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM) approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.

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