scholarly journals Visual Feedback Position Tracking and Attitude Analysis of Two-Wheeled Vehicles Integrating a Target Vehicle Motion Model

2017 ◽  
Vol 10 (3) ◽  
pp. 204-213 ◽  
Author(s):  
Satoshi NAKANO ◽  
Tatsuya IBUKI ◽  
Mitsuji SAMPEI
Keyword(s):  
Visual Feedback ◽  
Position Tracking ◽  
Motion Model ◽  
Target Vehicle ◽  
Vehicle Motion ◽  
Wheeled Vehicles

scholarly journals Interconnected vehicle suspension

2005 ◽  
Vol 219 (3) ◽  
pp. 295-307 ◽  
Author(s):  
M C Smith ◽  
G W Walker
Keyword(s):  
Vehicle Suspension ◽  
Passive Suspension ◽  
Vehicle Motion ◽  
Kinematic Transformation ◽  
High Degree ◽  
Wheeled Vehicles ◽  
Hydraulic Circuits

This paper introduces a class of passive interconnected suspensions, defined mathematically in terms of their mechanical admittance matrices, with the purpose of providing greater freedom to specify independently bounce, pitch, roll, and warp dynamics than conventional (passive) suspension arrangements. Two alternative realization schemes are described that are capable of implementing this class (under ideal assumptions). The first scheme incorporates an interconnected multilever arrangement consisting of four separate hydraulic circuits, which transforms the separate wheel station displacements to bounce, pitch, roll, and warp motions. Four separate mechanical admittances are connected across the transformed terminals of the multilever. The second scheme is kinematically equivalent to the first but the multilever part consists of four modular subsystems to achieve the same kinematic transformation. The purpose of the class is to allow a high degree of independence between the modes of vehicle motion, e.g. low warp stiffness independent of front and rear anti-roll stiffness. Practical issues that might be involved in implementing the realization schemes are discussed, as well as generalizations to two-and six-wheeled vehicles.

A New Vehicle Motion Model for Improved Predictions and Situation Assessment

2011 ◽  
Vol 12 (4) ◽  
pp. 1209-1219 ◽  
Author(s):  
Joakim Sorstedt ◽  
Lennart Svensson ◽  
Fredrik Sandblom ◽  
Lars Hammarstrand
Keyword(s):  
Motion Model ◽  
Situation Assessment ◽  
Vehicle Motion

scholarly journals Motion State Estimation of Target Vehicle under Unknown Time-Varying Noises Based on Improved Square-Root Cubature Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2620 ◽  
Author(s):  
Shiping Song ◽  
Jian Wu
Keyword(s):  
Kalman Filter ◽  
Time Varying ◽  
Square Root ◽  
Maximum Increase ◽  
Target Vehicle ◽  
Motion State ◽  
Noise Statistic ◽  
Cubature Kalman Filter ◽  
Non Linear ◽  
Vehicle Motion

In the advanced driver assistance system (ADAS), millimeter-wave radar is an important sensor to estimate the motion state of the target-vehicle. In this paper, the estimation of target-vehicle motion state includes two parts: the tracking of the target-vehicle and the identification of the target-vehicle motion state. In the unknown time-varying noise, non-linear target-vehicle tracking faces the problem of low precision. Based on the square-root cubature Kalman filter (SRCKF), the Sage–Husa noise statistic estimator and the fading memory exponential weighting method are combined to derive a time-varying noise statistic estimator for non-linear systems. A method of classifying the motion state of the target vehicle based on the time window is proposed by analyzing the transfer mechanism of the motion state of the target vehicle. The results of the vehicle test show that: (1) Compared with the Sage–Husa extended Kalman filtering (SH-EKF) and SRCKF algorithms, the maximum increase in filtering accuracy of longitudinal distance using the improved square-root cubature Kalman filter (ISRCKF) algorithm is 45.53% and 59.15%, respectively, and the maximum increase in filtering the accuracy of longitudinal speed using the ISRCKF algorithm is 23.53% and 29.09%, respectively. (2) The classification and recognition results of the target-vehicle motion state are consistent with the target-vehicle motion state.

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