Herman A. Hamersma

Accidents caused by wheeled mining machines contribute to approximately 30% of injuries and fatalities in the global mining industry. Wheeled mining machines have limited driver assist features when compared to the passenger vehicle market and are typically limited to collision avoidance by braking. These products are often subject to false positive interventions leading to production losses, increased wear, and resistance to adopt the technology by end users. This study proposes a sampling-based method to expand the collision avoidance by braking approach to include steering. The sampling method is based on the vehicle’s kinematics and the application of a Gaussian distribution to the steering rate to determine the probability of a collision occurring. Initial results indicate that the inclusion of steering rate on the collision prediction model may increase the operator’s situational awareness, leading to fewer false positives.

Controllable suspension systems have the capability of changing suspension forces. One control approach is to define a cost function with the aim of optimising either ride comfort or handling. Such controllers are usually reactive and not pro-active. Controllers can benefit significantly by having a priori knowledge of the effect that changing the suspension settings will have on the suspension forces. This is especially true for vehicles traversing very rough terrain. This paper addresses the a priori knowledge needed by predicting what the suspension forces will be before changing the suspension setting. The proposed approach involves estimating sprung and unsprung mass acceleration, estimating the road excitation, and then predicting the suspension forces. A quarter car model is used to illustrate the concept. Thereafter, the concept is extended to a nonlinear multibody dynamics model and finally validated experimentally. Results indicate that the suspension force in a different suspension modes can be predicted before switching suspension modes.

As one of the major external influences on moving ground vehicles, the road surface shape forms a significant input to the suspension system. Especially cars operating on rough terrain are heavily affected by a fast changing environment and sufficient knowledge of the surrounding conditions becomes a matter of safety. This work examines a road profile estimation approach applying modulating function based estimation methods and series expansion of the unknown signals in order to obtain the input trajectory and the respective state information for the suspension adjustment. A conventional setup of accelerometers, displacement transducers and angular measurements is considered. The presented framework is validated by simulation based on the structure of available experimental data.

Extensive research was done on the rolling radius of a tyre from the 1960s to the 1980s, specifically looking at the tractive performance of tractors and agricultural vehicles. The research was limited to investigating slip conditions at low vehicle speed where it was safe to assume that the rolling radius is static or quasi-static. This paper revisits some of these investigations, and looks at the validity of the static or quasi-static rolling radius assumption on hard, high friction and uneven surfaces at higher speeds. The aim is to establish whether the rolling radius defined under static conditions can be used to estimate tyre contact patch velocity, a state needed to determine longitudinal tyre slip. Longitudinal tyre slip plays a crucial role in the performance of advanced driver assist systems. It is known that these systems' performance decreases on rough roads when the static rolling radius assumption may become inaccurate.