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The aim of this study was to explore the vibration challenges caused by the crankshaft design in the air pump of the power integration device for steering and brake assistance in new energy buses. Three distinct crankshaft structures were designed and transient dynamic analysis was conducted using ANSYS software to assess the stress conditions and obtain the torque curves at the center points of these structures. Three newly fabricated crankshaft prototypes were subjected to vibration intensity tests on the integrated device. The experimental results revealed that the assistance device using the new crankshaft significantly reduced vibration intensity. [ABSTRACT FROM AUTHOR]

Drivers with disabilities such as hemiplegia or unilateral upper limb amputation restricting steering wheel operation to one arm could encounter the challenge of stabilizing vehicles during pedestrian collision avoidance. An sEMG-controlled steering assistance system was developed for these drivers to enable rapid steering wheel rotation with only one healthy arm. Test drivers were recruited to use the Myo armband as a sEMG-based interface to perform pedestrian collision avoidance in a driving simulator. It was hypothesized that the sEMG-based interface would be comparable or superior in vehicle stability to manual takeover from automated driving and conventional steering wheel operation. The Myo armband interface was significantly superior to manual takeover from automated driving and comparable to manual steering wheel operation. The results of the driving simulator trials confirm the feasibility of the sEMG-controlled system as a safe alternative that could benefit drivers with the aforesaid disabilities. [ABSTRACT FROM AUTHOR]

The differential drive assist steering (DDAS) system makes full use of the advantages of independent control of wheel torque of electric vehicle driven by front in-wheel motors to achieve steering assistance and reduce the steering effort of the driver, as the electric power steering (EPS) system does. However, as an indirect steering assist technology that applies steering system assistance via differential drive, its linear control algorithm, like existing proportion integration differentiation (PID) controllers, cannot take the nonlinear characteristics of the tires’ dynamics into account which results in poor performance in road feeling and tracking accuracy. This paper introduces an active disturbance rejection control (ADRC) method into the control issue of the DDAS. First, the third-order ADRC controller of the DDAS is designed, and the simulated annealing algorithm is used to optimize the parameters of ADRC controller offline considering that the parameters of ADRC controller are too many and the parameter tuning is complex. Finally, the 11-DOF model of the electric vehicle driven by in-wheel motors is built, and the standard working conditions are selected for simulation and experimental verification. The results show that the ADRC controller designed in this paper can not only obviously reduce the steering wheel effort of the driver like PID controller, but also have better nonlinear control performance in tracking accuracy and smooth road feeling of the driver than the traditional PID controller.

This work examines whether it is possible to compensate for the loss of vestibular information that is usually felt by drivers of modern cars when negotiating bends and which is associated with the load transfer of the car. Indeed, the absence of this information causes drivers to underestimate their speed and consequently increases the risk of road departure. This study tries to visually compensate for this loss of vestibular information through a tilt movement of the frame of reference of the driver. This tilt is the same as that perceived in a bend because of the load transfer of the car. The results indicate that most participants are influenced by this visual information and modify their speed accordingly whilst taking a bend. Nevertheless, only those participants uninformed about the presence of this assistance did slow down, whereas informed participants accelerated. This effect has been interpreted as a consequence of the level of processing implicated in the steering task (implicit versus explicit processing). [Copyright &y& Elsevier]

Abstract: Collision analysis indicates that many car accidents occur when negotiating a bend. Excessive speed and steering wheel errors are often given by way of explanation. Nevertheless, the underlying origin of these dramatic errors could be, at least in part, a poor estimation of bend curvature. The aim of this study was to investigate both the assessment of bend curvature by drivers and the impact of symbolic road signs that indicate a hazardous bend on this assessment. Thus, participants first viewed a video recording showing approaching bends of different curvature before being asked to assess the curvature of these bends. This assessment could either be a verbal (symbolic control) estimation of the bend''s curvature and risk, or a sensorimotor (subsymbolic control) estimation of the bend''s curvature (participants were asked to turn a steering wheel to mimic the position that would be necessary to accurately negotiate the bend). Results show that very severe bends (with a radius of less than 80m) were actually underestimated. This was associated with an underestimation of risk corresponding to these bends and a poor sensorimotor anticipation of bend curvature. Road signs, which indicate risk significantly improve bend assessment, but this was of no use for sensorimotor anticipation. Thus, other indicators need to be envisaged in order to also improve this level of control. [Copyright &y& Elsevier]

The differential drive assist steering (DDAS) system makes full use of the advantages of independent control of wheel torque of electric vehicle driven by front in-wheel motors to achieve steering assistance and reduce the steering effort of the driver, as the electric power steering (EPS) system does. However, as an indirect steering assist technology that applies steering system assistance via differential drive, its linear control algorithm, like existing proportion integration differentiation (PID) controllers, cannot take the nonlinear characteristics of the tires' dynamics into account which results in poor performance in road feeling and tracking accuracy. This paper introduces an active disturbance rejection control (ADRC) method into the control issue of the DDAS. First, the third-order ADRC controller of the DDAS is designed, and the simulated annealing algorithm is used to optimize the parameters of ADRC controller offline considering that the parameters of ADRC controller are too many and the parameter tuning is complex. Finally, the 11-DOF model of the electric vehicle driven by in-wheel motors is built, and the standard working conditions are selected for simulation and experimental verification. The results show that the ADRC controller designed in this paper can not only obviously reduce the steering wheel effort of the driver like PID controller, but also have better nonlinear control performance in tracking accuracy and smooth road feeling of the driver than the traditional PID controller. [ABSTRACT FROM AUTHOR]