Steering Assistance Research Articles

Vehicle Dynamic Maneuverability through Data-driven Six Sigma Analysis for Enhanced Driver Assistance

This paper presents the concept of a vision-based system to provide intelligent driving assistance for steering, braking, and acceleration maneuvers. The system utilizes vision systems to detect the vehicle's surroundings, including the road, other vehicles, pedestrians, and obstacles. Based on this environmental awareness, the system can intervene to assist the driver to enhance the safety and efficiency of the driving experience by maneuverability access or alert to driver in steering, brake, and acceleration. The steering assist function analyzes the road geometry and trajectory and provides subtle steering adjustments alerts to keep the vehicle centered in the lane thereby smoothly navigate curves. The braking assist function detects potential collision threats and can assist driver on application status of brakes to avoid or mitigate impacts. Similarly, the acceleration assist function monitors the driving conditions and traffic flow to optimize throttle input for improved fuel economy, traffic flow thereby reducing the impact to dynamic parts of vehicles. A detailed six sigma analysis is done by DMAIC method to prove the proposed methodology significance compared to existing techniques.

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi-Task Time-Series Transformer

Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.

Parameter Setting and Driver Acceptability Evaluation of Steering Assistance System Using Impedance Control by Damping Ratio

In this paper, we introduce an adaptation to steering assistance systems for providing impedance control. We also suggest using the damping ratio as a design guideline for the parameters of the impedance control. In our research, experiments were conducted with experimental participants using a driving simulator implementing the suggested steering assistance system. As a result of both subjective and objective evaluations, it was found that the suggested steering assistance system is better accepted by drivers than the conventional system. Moreover, the use of the damping ratio as an evaluation index facilitates the design of the impedance parameters. These results indicate the effectiveness of our suggested system.

Motorcycle emergency steering assistance: A systematic approach from system definition to benefit estimation and exploratory field testing

Braking assistance systems are already contributing to improving motorcyclists’ safety, however, research on emergency systems acting on the steering is lacking. These systems, already available for passenger cars, could prevent or mitigate motorcycle crashes in which safety functions based only on braking are ineffective. The first research question was to quantify the safety impact of diverse emergency assistance systems acting on the steering of a motorcycle. For the most promising system, the second research question was to assess the feasibility of its intervention using a real motorcycle.Three emergency steering assistance systems were defined in terms of Functionality, Purpose, and Applicability: Motorcycle Curve Assist (MCA), Motorcycle Stabilisation (MS), and Motorcycle Autonomous Emergency Steering (MAES). Experts evaluated each system’s applicability and effectiveness based on the specific crash configuration (using Definitions for Classifying Accidents — DCA), the Knowledge-Based system of Motorcycle Safety (KBMS), and the In-Depth Crash Reconstruction (IDCR). An experimental campaign was conducted with an instrumented motorcycle to assess the rider’s reaction to external steering input. A surrogate method for an active steering assistance system imparted external steering torques in correspondence with a lane change to analyse the effect of the steering inputs on motorcycle dynamics and rider controllability.MAES globally got the best score for each assessment method. MS received better evaluations than MCA in two out of three methods. The union of the three systems covered a sizeable fraction of the crashes considered (maximum score in 22.8% of the cases). An estimation of the injury potential mitigation, based on injury risk functions for motorcyclists, was made for the most promising system (MAES). The field test data and video footage showed no instability or loss of control, despite the high intensity (>20Nm) of the external steering input. The rider interviews confirmed that the external action was intense but manageable.For the first time, this study presents an exploratory assessment of the applicability, benefits, and feasibility of motorcycle safety functions acting on the steering. MAES, in particular, was found applicable to a relevant share of crashes involving motorcycles. Remarkably, applying an external action to produce a lateral avoidance manoeuvre proved feasible in a real-world test setting.

A Shared Control Design for Steering Assistance System Considering Driver Behaviors

With the rapid development of advanced driver assistance systems, driver-automation cooperative control becomes more challenging due to the uncertainty of driver behaviors. To this end, this paper proposes a shared control approach to assist drivers in path-tracking and collision avoidance. A high-fidelity CarSim vehicle model embedded driver simulator is built to collect the driver data, based on which the normalized steering angle input and the time to collision are defined to quantify the driving performance. Then, these performance indices are employed to develop the fuzzy controller and determine the control authority for the steering assistance system (SAS). A multi-constraints model predictive control is designed to follow the desired path, and simultaneously ensure the vehicle stability. Different from the traditional constraints handling method, the state constraint is transformed into the input constraint considering the nonlinearity. The effectiveness of the proposed method is verified with two less experienced drivers. The test results show that the path-tracking performance and vehicle stability can be guaranteed with less control effort.

The obstacle avoidance assistance control of multi-axle distributed heavy vehicles

Based on the artificial potential field theory, a hierarchical obstacle avoidance assisted driving framework is proposed in this paper, in order to solve the obstacle avoidance assisted driving problem of distributed heavy vehicles. Initially, a desired lateral position potential field (DLPPF) that reflects the driver’s driving intention is applied based on the constant turn rate and velocity (CTRV) model. And a real-time obstacle avoidance path can be obtained and further updated through a comprehensive consideration of the safety of obstacle avoidance and the obstacle repulsion potential field (ORPF). In addition, a differential drive assistance steering (DDAS) on the heavy vehicle steering bridge has been adopted to realize avoiding obstacles, and additional yaw moment to the vehicle’s non-steering axle has been utilized to ensure the maneuverability and stability of vehicle. Aiming to solve the lower layer control problem, a variable weight controller based on model predictive control (MPC) is designed to coordinate path tracking performance and dynamics control. Finally, through simulations and comparisons, the effectiveness of the obstacle avoidance strategy is verified.

A Novel Control Strategy of Crosswind Disturbance Compensation for Rack-Type Motor Driven Power Steering (R-MDPS) System

The wind suddenly blown to the side of the car (i.e, cross-wind effect) is considered as one of the major lateral disturbances, which causes the unstable motion of the vehicle and the persistent driving fatigue for the driver who tracks the desired travel path. In particular, a commercial vehicle having a large side area is greatly affected by this effect. Therefore, many related automotive/car manufacturers still wish to equip their Advanced Driving Assistant System (ADAS) with the crosswind disturbance compensation control system. Meanwhile, in recent advanced vehicle systems, a rack-type motor-driven power steering (R-MDPS) system is more widely used than a column-type MDPS (C-MDPS) due to the structure advantage and the effective steering assistance. Recognizing those two issues, this paper investigates a novel anti-control and estimation strategy of crosswind disturbance for the R-MDPS system of vehicles with non-negligible side surfaces. Specifically, an adaptive disturbance observer (D.O.B) has been proposed to estimate the crosswind effect. Furthermore, using optimal control theory, the compensation control system is designed to assist the driver in two possible situations. One is for when the driver continues to steer the vehicle under the effect of the crosswind, and the other is for when the driver temporarily loses steering control due to the effect. In addition, the control mode selection conditions between two controls are clearly presented to maximize the efficiency and performance of the proposed control system. Finally, the effectiveness of the proposed control system has been evaluated based on the Simulink/Carsim Co-simulation and HILS environments.

저속 무한궤도 차량의 클러치/브레이크 방식 조향 제어에 관한 연구

Various steering systems are used for agricultural automation especially ridge planting machines. The flexible continuous tracks with skid steering are suitable solutions. Considering the size of the machines, the tracks must be controlled using the clutch/brake steering system, which causes discontinuity. In order to use autonomous steering, the fast on-off control on the clutch should be applied. Knowing the turning radius during steering by the on-off operation is essential, which can be obtained with the detailed system simulations by Amesim. It has been found that the on-off frequencies and the turning radius has linear relationship in most usable high frequency spectrum. Thus, the fast on-off control of clutch, as an autonomous steering assistance system, makes it easier to steer tracked vehicles with suitable sensors.

Multimodal sensing and intuitive steering assistance improve navigation and mobility for people with impaired vision.

Globally, more than 250 million people have impaired vision and face challenges navigating outside their homes, affecting their independence, mental health, and physical health. Navigating unfamiliar routes is challenging for people with impaired vision because it may require avoiding obstacles, recognizing objects, and wayfinding indoors and outdoors. Existing approaches such as white canes, guide dogs, and electronic travel aids only tackle some of these challenges. Here, we present the Augmented Cane, a white cane with a comprehensive set of sensors and an intuitive feedback method to steer the user, which addresses navigation challenges and improves mobility for people with impaired vision. We compared the Augmented Cane with a white cane by having sighted and visually impaired participants complete navigation challenges while blindfolded: walking along hallways, avoiding obstacles, and following outdoor waypoints. Across all experiments, the Augmented Cane increased the walking speed for participants with impaired vision by 18 ± 7% and sighted participants by 35 ± 12% compared with a white cane. The increase in walking speed may be due to accurate steering assistance, reduced cognitive load, fewer contacts with the environment, and higher participant confidence. We also demonstrate advanced navigation capabilities of the Augmented Cane: indoor wayfinding, recognizing and steering the participant to a key object, and navigating a sequence of indoor and outdoor challenges. The open-source and low-cost design of the Augmented Cane provides a platform that may improve the mobility and quality of life of people with impaired vision.

A robust H∞-based steering assistance system for the wheeled tractor.

Agricultural machine automatic navigation poses great challenge to the precise agricultural technology system nowadays. To this end, this paper proposes a novel steering assistance system (SAS) to assist drivers in the path-tracking. First, the driver steering model is investigated through the driver simulator tests. Combining the wheeled tractor kinematics model, a driver-vehicle model is developed. Then, a polytopic linear parameter-varying (LPV) system is adopted to describe the uncertainties, including time-varying driver model parameters and velocity, in the model, based on which an output-feedback robust controller is developed to ensure robust stability within the polytope space. Moreover, a regional pole placement method is adopted to improve the transient performance of the system. Finally, driver-in-the-loop and field tests conducted to value the controller. The results show the effectiveness of the proposed method to improve the path-tracking performance for the agricultural machine navigation, while reducing the physical and mental workload of drivers. This control method is expected to be a paradigm for the precise navigation system of the agricultural machinery.

Design and Test of Auxiliary Steering System of High Clearance Sprayer Chassis Based on Self-steering Structure

A hydraulic assisted steering method was proposed to solve the problem of steering instability caused by insufficient reverse resistance moment of wheel hub motor in four-wheel independently electrically driven high clearance sprayer. Firstly, the principle of steering chassis structure of four-wheel independently electrically driven high clearance sprayer is briefly introduced. On this basis, hydraulic auxiliary steering system is designed. Then a simplified 2-DOF vehicle steering model is established to analyze the Angle control of the auxiliary steering system. Finally, simulation and road surface test are carried out respectively to verify the performance of steering and auxiliary steering coordination control. Under the working conditions of the steering system alone and the steering system and the auxiliary steering system together, the four-wheel electric drive sprayer carried out the obstacle crossing test and the downslope test with the slope of 15° respectively at the speed of 1 m/s on the smooth road surface. The test results show that in the downhill test, the maximum tracking deviation of the steering system alone is 6.1°, the maximum tracking deviation of the steering and auxiliary steering coordination is 0.9°, the maximum tracking deviation of the steering system alone is 12.0°, and the maximum tracking deviation of the steering and auxiliary steering coordination is 1.2° in the obstacle breaking test. The test results verify the feasibility and stability of the hydraulic steering system proposed in this paper. The system has good test performance and can meet the practical requirements.

Surface Electromyography-Controlled Pedestrian Collision Avoidance: A Driving Simulator Study

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.

Robust steering assistance control for tracking large-curvature path considering uncertainties of driver’s steering behavior

A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

Driver steering assistance for collision avoidance and turning performance optimization by constrained MPC

Driver steering assistance for collision avoidance and turning performance optimization by constrained MPC

Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System

Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver's lane change intention. By using a deep learning-based method to model a driver's decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver's intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.

Modelling and Simulation of a Brushless Motor DC for Electric Power Steering Assistance

Modelling and Simulation of a Brushless Motor DC for Electric Power Steering Assistance

차량 횡슬립 추정 기반 운전자 충돌회피보조 연구

차량 횡슬립 추정 기반 운전자 충돌회피보조 연구

Electric power steering nonlinear problem based on proportional–integral–derivative parameter self-tuning of back propagation neural network

Aiming at the problem of nonlinear power steering in the automobile electric power steering system, an advanced control algorithm is required for the practical system. This paper introduces back propagation neural network arbitrary nonlinear approximations to discretize the vehicle’s power assistance characteristic. Steering power is also realized in the whole range of speed, which overcomes the steering blind zone and lays a foundation for the design of subsequent controllers. In addition, considering the nonlinear frictional resistance problem of the electric power steering system, the traditional proportional–integral–derivative remote control algorithm will result in poor dynamic performance or system instability. Therefore, this paper proposes a control algorithm based on back propagation neural network proportional–integral–derivative parameter self-tuning. Using the error between the expected current and the actual motor current, the back propagation neural network algorithm is used to learn and realize the adaptive adjustment of proportional–integral–derivative parameters. Simulation results show that the proposed control system effectively realizes the nonlinear steering assistance in the whole vehicle range speed, eliminates the influence of nonlinear friction in the electric power steering system, and improves the robustness of the control system.

Active Disturbance Rejection Control of Differential Drive Assist Steering for Electric Vehicles

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.

Multi‐mode human–machine cooperative control for lane departure prevention based on steering assistance and differential braking

Both steering assistance and differential braking have an irreplaceable advantage for man-machine cooperative control of lane departure prevention. To realise the human-machine cooperative control of lane departure prevention with minimal human-machine conflict, a multi-mode human-machine cooperative control algorithm based on steering assistance and differential braking is proposed. Taking the longitudinal velocity and road curvature as input and considering the driver torque, the fuzzy controller used to determine the virtual lane boundary is designed. The control system is divided into the driver control mode, the steering assistance cooperative control mode and the differential braking cooperative control mode by the driver state and the virtual lane boundary width. Next, the model predictive controller is developed to determine the front steering angle in the steering assistance cooperative mode and the additional yaw moment in the differential braking cooperative mode, respectively. Finally, the active disturbance rejection controller is used to determine the assistance torque, and single rear wheel brake control strategy is developed to calculate the wheel braking force. The effectiveness of the proposed multi-mode human-machine cooperative control algorithm is evaluated with numerical simulation and experiments on a human-in-the-loop platform.