Measurement of tyre longitudinal slip-ratio is often estimated from three independent measurements devices namely wheel rotation speed, vehicle speed and tyre rolling radius. This produces an expensive measurement system to indirectly determine the slip-ratio. This paper presents a method by which the slip-ratio is determined from a video camera using digital image correlation techniques. The camera, mounted in such a way that the contact patch region is captured, enables the system to measure the tyre tread speed and ground speed at the contact patch. The slip-ratio is then determined from these two measurements.
This paper presents inexpensive methods whereby the vehicle side-slip angle can be measured accurately at low speeds on any terrain using cameras. Most commercial side-slip angle sensor systems and estimation techniques rely on smooth terrain and high vehicle speeds, typically above 20km/h, to provide accurate measurements. However, during certain in-situ tyre and vehicle testing on off-road conditions, the vehicle may be traveling at speeds slower than required for current sensors and estimation techniques to provide sufficiently accurate results. Terramechanics tests are typical case in point. Three algorithms capable of determining the side-slip angle from overlapping images are presented. The first is a simple fast planar method. The second is a more complex algorithm which can extract not only the side-slip angle but also its rotational velocities and scaled translational velocities. The last uses a calibrated stereo-rig to obtain all rotations and translational movement in world coordinates. The last two methods are aimed more at rough terrain applications, where the terrain induces motion components other than typical predominant yaw-plane motion. The study however found no discernible difference in measured side-slip angle of the methods. The system allows for accurate measurement at low and higher speeds depending on camera speed and lighting.2
This paper proposes a novel concept for the modelling of a vehicle steering driver model for path following. The proposed steering driver reformulates and applies the Magic Formula, used for tyre modelling, to the vehicle's yaw acceleration vs. steering velocity response as a function of vehicle speed.The path-following driver model was developed for use in gradient-based mathematical optimisation of vehicle suspension characteristics for handling. Successful application of gradient-based optimisation depends on the availability of good gradient information. This requires a robust driver model that can ensure completion of the required handling manoeuvre, even when the vehicle handling is poor.The steering driver is applied to a non-linear full vehicle model of a Sports Utility Vehicle, performing a severe double lane change manoeuvre. Simulation results show excellent correlation with test results. The proposed driver model is robust and well suited to gradient-based optimisation of vehicle handling.
Road profiling is an important aspect of vehicle dynamics simulations especially over rough terrains. The accurate measurement of rough terrains allows for more accurate multi body simulations. Three dimensional road profiles are usually performed by utilising a line scan sensor which measures several points lateral to the road. The sensors range from simple road following wheels to LiDAR sensors. The obtained line scans are longitudinally stitched together using the orientation and position of the sensor to obtain a full three dimensional road profile. The sensor's position and orientation therefore needs to be accurately determined in order to combine the line scans to create an accurate representation of the terrain. The sensor's position and orientation is normally measured using an expensive inertial measurement unit or Inertial Navigation System (INS) with high sensitivity, low noise and low drift. This paper proposes a road profiling technique which utilises stereography, based on two inexpensive digital cameras, to obtain three-dimensional measurements of the road. The system negates the use of an expensive INS system to determine orientation and position. The data sets also require 2 subsampling which can be computationally expensive. A simple subsampling routine is presented which takes advantage of the structure of the data sets to significantly speed up the process.
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