Steering Wheel Research Articles

Who is performing the driving tasks after interventions? Investigating drivers' understanding of mode transition logic in automated vehicles

Mode awareness is important for the safe use of automated vehicles, yet drivers' understanding of mode transitions has not been sufficiently investigated. In this study, we administered an online survey to 838 respondents to examine their understanding of control responsibilities in partial and conditional driving automation with four types of interventions (brake pedal, steering wheel, gas pedal, and take-over request). Results show that most drivers understand that they are responsible for speed and distance control after brake pedal interventions and steering control after steering wheel interventions. However, drivers have mixed responses regarding the responsibility for speed and distance control after steering wheel interventions and the responsibility for steering control after gas pedal interventions. With a higher automation level (conditional driving automation), drivers expect automation to remain responsible more often compared to a lower automation level (partial driving automation). Regarding Hands-on requirements, more than 99% of respondents answered that drivers would keep their hands on the steering wheel after all intervention types in partial automation, while 60–95% would place their hands on the wheel after various intervention types in conditional automation. A misalignment between actual logic and drivers' expectations regarding control responsibilities is observed by comparing survey responses to the mode transition logic of commercial partially automated vehicles. To resolve confusion about control responsibilities and ensure consistent expectations, we propose implementing a consistent mode design and providing enhanced information to drivers.

An Asymmetric Independently Steerable Wheel for Climbing Robots and Its Motion Control Method

Climbing robots, with their expansive workspace and flexible deployment modes, have the potential to revolutionize the manufacturing processes of large and complex components. Given that the surfaces to be machined typically exhibit variable curvature, good surface adaptability, load capacity, and motion accuracy are essential prerequisites for climbing robots in manufacturing tasks. This paper addresses the manufacturing requirements of climbing robots by proposing an asymmetric independently steerable wheel (AISW) for climbing robots, along with the motion control method. Firstly, for the adaptability issue of the locomotion mechanism on curved surfaces under heavy load, an asymmetric independently steerable wheel motion module is proposed, which improves the steering difficulty of the traditional independently steerable wheel (ISW) based on the principle of steering assisted by wheels. Secondly, a kinematic model of the AISW chassis is established and, on this basis, a trajectory tracking method based on feedforward and proportional–integral feedback is proposed. Comparative experimental results on large, curved surface components show that the asymmetric independently steerable wheel has lower steering resistance and higher motion accuracy, significantly enhancing the reachability of climbing robots and facilitating their application in the manufacturing of large and complex components.

Machine learning-based real time identification of driver posture during driving

The detection of driver distractions is exceptionally important for driving safety. Driver distraction can originate from various sources such as external tasks (e.g., texting or eating) or mental states (e.g., sleepiness, tiredness, anger, and tension). To detect these conditions, most of the previous studies were based on vision-based techniques. These techniques are affected by environmental factors (e.g., day, night, and facial accessories such as glasses and hats). However, the steering wheel is an interface that provides a direct relationship between the driver and vehicle. The driver’s interaction can effectively reflect this behavior and mental state. This study introduced a new method for detecting driver distractions by utilizing force/torque (F/T) sensor data extracted from the steering wheel. An experimental setup was designed and developed to measure the accuracy of the proposed method. To validate the strategy, a machine learning-based algorithm was developed. It demonstrated remarkable performance in determining the position of the driver’s hand on the steering wheel and in inferring with high precision the hand the driver uses to operate the vehicle. The method produced accurate results in all the grip ranges that could be held by the driver within the range of 0°–360°. The support vector machine (SVM) method was used in machine learning. It predicted with a 91.1% accuracy rate.

A dynamic target tracking framework of UGV for UAV recovery under random disturbances

PurposeDynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.Design/methodology/approachTo mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.FindingsTo validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.Originality/valueThis paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Application Of Onboard PIV To Front Wheelhouse Wake Behavior Under Stationary Crosswind Conditions

When driving a car, the driver and passengers may feel larger drag from a headwind, the steering wheel would be taken away by a crosswind or pushed by a tailwind. Anytime, anywhere, we will encounter various scenes with natural wind under real world conditions such as urban, suburb and highway. We will develop the appropriate vehicle by simulating these conditions to satisfy with various customer requirements. For vehicle aerodynamic research and development, we are simply running simulations in physical- and cyber-space, that is, wind tunnel facility and computational fluid dynamics to reproduce these real worlds. However, it is not easy to simulate these wind inlet conditions, such as the temporal change in crosswind, especially, in wind tunnel facility. The stationary yaw angle by rotating turntable would reproduce a crosswind, if there is no turbulent generator system like in Pininfarina and Swing in FKFS. Therefore, we focus on the unsteady and asymmetrical flow from the front wheelhouse of the vehicle with tire rotation. There is three-dimensional complicated flow that are interacted by the flow from various directions, such as the flow along the front bumper, the flow along the rotating tire and wheel, the blowing flow from the rotating wheel opening area and the flow from the tire house. These complicated flow produces the front wheelhouse wake along the body side that affects the vehicle aerodynamic performance. Furthermore, there are asymmetrical flows around the vehicle by different front wheelhouse wake in windward and leeward sides. The larger side wake increases the aerodynamic drag, and the fluctuating side wake may induce the instability of the vehicle handling stability. Here, the proposed onboard 2D-3C PIV measurement system in the previous study enables us to measure same measurement area along the vehicle body side without change in optical configuration effectively, that indicates to reduce the time by resetting the optical configuration. We will investigate what kind of flow field should be focused on and controlled to prevent the vehicle instability by investigating these asymmetrical flow field around the vehicle under crosswinds.

RELATIONSHIP BETWEEN TRANSITION CURVES (CLOTHOIDS) AND CIRCULAR CURVES IN ROAD ALIGNMENT

<p style="text-align: justify;"><span lang="EN-GB" style="font-size: 12pt; line-height: 200%; font-family: arial, helvetica, sans-serif;">The transition from a straight line to a circular curve is always abrupt, even if this is not readily noticeable at very large radii or low driving speeds. On the straight section the steering wheel angle is zero, on the arc section the steering wheel angle must have a certain value, as stated above, which is greater the tighter the curve. This in turn would mean that you would have to turn the steering wheel jerkily at the transition between the straight section and the circular curve section. Otherwise you would go off track. Even in the case of large radii without transition curves, the lines of a road appear inelastic and can satisfy neither aesthetically nor driving psychologically down to the last detail. In perspective foreshortening - how the road is seen by the driver - sudden kinks appear despite the large curve radii.</span></p>

A Novel Robust H∞ Control Approach Based on Vehicle Lateral Dynamics for Practical Path Tracking Applications

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an H∞ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An H∞ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with H∞ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios.

How the cognitive load of simulated driving affects the brain dynamics underlying auditory attention

Objective Distracted driving is a primary contributor to for motor vehicle crashes, the leading cause for injuries and fatalities for youth. Although attention and working memory clearly underlie driving abilities, few studies explore these functions on the brain-level under the cognitive load of driving. To understand the load driving has on auditory attention processing, we examined the differences in dynamic brain response to auditory stimuli during LOAD (while driving in a high-fidelity driving simulator) and No-LOAD conditions (seated in simulator, parked on the side of the road). Methods Twenty-seven young adult drivers (18–27 y/o; 15 = women) completed a Selective Auditory Attention Task during both a LOAD (driving) and No-LOAD condition in a ½ cab miniSim® high-fidelity driving simulator. During the task, participants responded by pressing the volume control button on the steering wheel when a target tone was presented to a target ear. Electroencephalography-recorded event-related brain responses to the target tones were evaluated through alpha and theta oscillations for two response windows (early: 150–330ms; late: 350–540ms). Results During an early time window, we observed a significant interaction between attended/unattended and LOAD/No-LOAD theta power in the right frontal cortical region (F(1, 24)= 5.4, p=.03, partial η 2=.18). During the later window, we observed a significant interaction between attended/unattended and LOAD/No-LOAD alpha response in the posterior cortical region (F(1, 24)=11.81, p=.002, partial η 2=.15) and in the right temporal cortical region during the window (F(1, 24)=4.3, p=.05, partial η 2=.33). Conclusions Our data provide insight into the demand that driving has on cognitive faculties and how dual task engagement may draw resources away from driving. We suggest future research directly incorporate vehicle control abilities into study design to understand how brain-based measures relate to driving behaviors.

Modeling of Car-to-motorcycle Overtaking Maneuver Based on Comfort Zone Boundaries

Thailand has one of the world's highest road fatality rates, mainly on motorcycles. In mixed traffic, motorcycles coexist with other vehicles. The interaction between cars and motorcycles, such as overtaking due to speed differences, can lead to accidents. This scenario also has implications for autonomous vehicles interacting with motorcycles. To increase safety in such interactions, a model was developed that simulates overtaking maneuvers of car drivers with motorcycles, using the concept of comfort zone boundary and a four-phase classification. In a driving simulator, 648 overtaking maneuvers collected from 36 Thai drivers were recorded with different lateral positions and speeds of the motorcycles. A novel graphical method using steering wheel angle and steering wheel velocity signals facilitated the identification of the phases. Time-to-collision and lateral distance characterized driver comfort zones and served as an indicator for safety measures. The lateral position of the motorcycle has proven to be the most influential factor in the model. The results suggest that overtaking vehicles exhibit non-lane-bound driving characteristics and a risk for the sideswipe accident is identified. These results provide a foundational framework for advanced driver assistance systems and motion planning of autonomous vehicles, contributing to improved road safety.

AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A deep convolutional neural network (DCNN) is employed to process RGB and IR images. The RGB dataset comprises 1200 images (600 with pedestrians and 600 without), while the IR dataset includes 1000 images (500 with pedestrians and 500 without), 85% of which were captured at night. Two distinct DCNNs were trained using these datasets, achieving a validation accuracy of 99.6% with the RGB camera and 97.3% with the IR camera. The radar sensor determines the pedestrian’s range and direction of travel. Experimental evaluations conducted in a vehicle demonstrated that the multi-sensor detection scheme effectively triggers a warning signal to a vibrating motor on the steering wheel and displays a warning message on the passenger’s touchscreen computer when a pedestrian is detected in potential danger. This system operates efficiently both during the day and at night.

Analysis of consumer preferences for new electric vehicle technologies: Can future vehicle steering system steer consumer's purchase intention?

As the mainstream of vehicle industry is moving from internal combustion engine vehicles to electric vehicles (EVs), a multi-perspective analysis of consumer preference for the new EV technology is required. Therefore, this study proposes a new framework for consumer preference analysis—a two-stage choice experiment—and conducts an empirical analysis of the four-wheel independent steering system (4-WISS). In the first stage, we analyzed consumer preference for the availability of 4-WISS in EVs. In the second stage, we investigated consumer preference for the specific technology of 4-WISS in EVs. Stated preference data were collected through a choice experiment survey, and a mixed logit model was used to quantitatively analyze consumer preference and willingness-to-pay. Results show that consumers are willing to pay KRW 19,045,098.6 (USD 13,513.9) to use 4-WISS in high-end EVs. Furthermore, consumers who are female, older, and have more knowledge of autonomous vehicles prefer 4-WISS more. We found that consumers prefer the parallel movement among 4-WISS modes, the touch display for selecting 4-WISS, the steering wheel for operating 4-WISS mode, and dashboard for displaying information. We finally discuss the new business model in the vehicle industry and suggest automakers to establish an effective strategy for the initial market for EVs with 4-WISS.

Towing an Object With a Rover

Abstract The present study investigates the problem of towing an object that is lying on a surface in a given workspace and the applicability to a planetary rover with four steering wheels. A quasi-static method has been introduced and used for path planning and for the synthesis of both object and rover trajectories. The rover uses a tether as the towing medium which is modeled as an elastic unilateral constraint. Moreover, a kinematic model of the rover which includes steering asymmetrical joint limits is taken into account. The dynamics model of the overall system is then derived and a sensitivity analysis is performed over a finite number of different trajectories, in order to evaluate the quasi-static assumption, the effects of the model, and the influence of the elastic constraint. Finally, experiments have been performed using the novel Archimede rover prototype and compared with dynamics simulations; the remarkable adherence shown with the model validates the overall approach.

Introducing the portable AttentionTrip: An engaging tool for measuring the networks of attention

BackgroundMeasurement of the efficacy of the networks of attention is a frequent component of research in cognitive and clinical neuroscience. Developed in 2002, the Attention Network Test (ANT), has become the most widely used tool for this purpose. New methodIn 2017 a more engaging, game-like tool based on the ANT, called the AttentionTrip was described. The network scores from five studies which used AttentionTrip are shown to be robust. Newer methodThat version of AttentionTrip required a steering wheel and desk-top computer. Here we describe a new, portable version of the AttentionTrip that is administered using a hand-held tablet (iPad) ResultsThree samples of participants (total = 44) completed the portable version of AttentionTrip. The network scores generated using the portable AttentionTrip were also robust. Effect sizes compare favourably with those generated by the ANT and the desktop version. ConclusionsThe findings support the use of the portable AttentionTrip as an alternative to the ANT when user engagement is important, such as when participants are prone to boredom, and when repeated administrations are required.

Chassis concept of the individually steerable five-link suspension: a novel approach to maximize the road wheel angle to improve vehicle agility

The Institute of Vehicle Systems Engineering at Ulm University of Applied Sciences is currently developing the autonomous concept vehicle Nimbulus-e in the strategic field “Intelligent Commercial Vehicles” with the aim of maneuvering as agilely as possible in confined spaces. To achieve high agility under the conditions mentioned, large road wheel steering angles are necessary. As part of the basic vehicle concept, the first step is to select a suitable chassis for this purpose. Conventional suspensions cannot be applied due to the mechanical connection of the tie rod to the steering knuckle limiting the road wheel angles. Therefore, the approaches published so far for individual chassis concepts with large steering angles are analyzed and evaluated for use. In this paper, the concept of a novel individually steerable five-link suspension is described. The concept includes a vehicle body mounted steering actuator connected to the chassis via a self-locking worm gear. Due to the body mounted connection of the steering actuator, it does not contribute to the unsprung mass. An analysis of the kinematic and elastokinematic properties and the achievable road wheel steering angle is presented. In the Nimbulus e concept vehicle, the individually steerable corner module is used on both the front and the rear axle. The system is driven by four wheel hub motors. This means that eight control variables are available for the vehicle.

On integrated lateral and longitudinal control of brain-controlled vehicles

A brain-controlled vehicle (BCV) is a vehicle that uses the Brain–Computer interface (BCI) to analyze the driver’s electroencephalogram (EEG) to obtain human control commands, which can help disabled people extend movement range and improve their self-independence. At this stage, research on the control of BCVs usually focuses only on the lateral control or longitudinal control, and few studies have focused on the problem of integrated lateral and longitudinal control. Given this situation, this paper investigates the integrated control of brain-controlled vehicles (BCV) in both lateral and longitudinal directions, and proposes a control method that utilizes speed as the coupling point. Firstly, the vehicle’s speed is determined by the provided road information. To control the vehicle’s longitudinal speed, a hierarchical control method based on model predictive control (MPC) is implemented. This method introduces a threshold σ to speed up the calculation process and reduce resource consumption. Then, a global controller is developed to conduct the motion control. This controller utilizes three local ANFIS intelligent controllers, employing soft-switching combinations, to generate the steering wheel angle signal. The generated steering wheel angle signal is combined with the cornering command issued by the driver. This fusion process produces the final command, which is responsible for the vehicle’s motion control. Finally, a simulation model is developed to test the integrated control of the BCV in terms of both lateral and longitudinal movements. Multiple experiments are conducted under varying conditions, and the results prove that the proposed method is able to complete the integrated control of the BCV well, which fully illustrates its effectiveness.

Study on Design and Fabrication of 4 Wheel Steering Mechanism

This study focuses on the design and fabrication of a four-wheeled steering (4WS) mechanism, a technology that enables all four wheels of a vehicle to turn, thus enhancing control and manoeuvrability. Four-wheel steering allows for significant improvement in a vehicle's turning circle at low speeds by enabling the rear wheels to turn in the opposite direction to the front wheels. At high speeds, this system contributes to increased stability and a reduced risk of over steer. Despite these advantages, 4WS systems are complex and expensive, which has limited their prevalence in the automotive market. The Honda Prelude, introduced in 1987, was the first car equipped with 4WS. However, the technology has not been widely adopted, largely due to driver unfamiliarity with the control dynamics at low speeds. This research aims to explore the potential benefits of 4WS and assess its future adoption prospects as technological advancements and cost reductions are realized. Through detailed analysis and experimental fabrication, this study seeks to contribute to the understanding and potential application of 4WS in modern vehicles.

Research on cooperative control of vehicle path tracking and lateral stability

Abstract This study presents a novel trajectory tracking control approach integrating steering angle and wheel torque output. Unlike conventional hierarchical methods, it considers front wheel steering angle and direct swing torque control simultaneously. Utilizing Model Predictive Control for autonomous vehicles, the system employs path tracking deviation as the state variable, front wheel steering angle, and overall additional yaw moment as control inputs. The coordinated control strategy enhances vehicle stability and contributes to heading angle changes, improving trajectory tracking accuracy. Simulation results in Matlab/Simulink demonstrate the validity and superiority of the proposed method in trajectory tracking and vehicle stability.

Bimanual Continuous Steering Wheel Turning by a Dual-Arm Robot

Bimanual Continuous Steering Wheel Turning by a Dual-Arm Robot

RFDrive: Tagged Human-Vehicle Interaction for All

Human–vehicle interaction is an important factor for safe driving. The driver needs to interact with the in-vehicle steering wheel and infotainment system properly during driving. Specifically, driving guidelines require the driver to hold the steering wheel at the 3 o’clock and 9 o’clock positions. Moreover, the in-vehicle infotainment system should be more adaptive for the driver and front-seat passenger during driving (i.e., the in-vehicle infotainment system should be part and even fully disabled for the driver, whereas the front-seat passenger should be able to enjoy the full in-vehicle infotainment system). However, affordable vehicles are usually designed to achieve basic driving functions without considering safe human–vehicle interactions, which require an add-on, affordable, and ready-to-use human–vehicle interaction monitoring system. In this article, we present RFDrive, a system that can simultaneously locate the driver’s hand positions on the steering wheel and automate in-vehicle infotainment system touch discrimination for safe driving using commodity passive RFID tags. Since these commodity passive RFID tags are low cost (i.e., around 5 cents per tag), battery free, and are small, like a sticker, our design will enable not only safe driving but is also low cost, which can lead to sustainable solutions. To do so, we attach RFID tags on the steering wheel for the driver’s hand position location and attach RFID tags on the roof of the vehicle’s interior for in-vehicle infotainment system touch discrimination (i.e., differentiating the driver’s infotainment system touch and front-seat passenger’s infotainment system touch). However, the wheel steering will distort the wireless channel-based driver’s hand position location on the steering wheel. Thus, we propose a novel tag ID-based algorithm to locate the driver’s hand position on the steering wheel by harnessing the human body as part of the RFID tag’s antenna. Since the in-vehicle infotainment system touch from the driver or front-seat passenger will affect different RFID tags attached to the roof of the vehicle’s interior, we propose to use the differential amplitude of backscattered signals from all the tags to discriminate in-vehicle infotainment system touch sources. Our experiments show that RFDrive can achieve the average accuracy of 0.98 and 0.98 for in-vehicle touch source discrimination and driver’s hand position location, respectively.

Is a robot car still a car? Consumer perceptions of fully automated vehicles and automobility in Canada

Fully automated vehicles (FAVs) could transform private car-based mobility or “automobility”, but the direction of FAV impacts is uncertain and contingent on consumers. We investigate consumer response to FAVs and its relationship to automobility by conducting semi-structured interviews with 34 new car buyers in British Columbia, Canada. First, we assess consumer response through exercises where participants design their “ideal next vehicle”, choosing between FAV versus conventional vehicle (CV) options. We find that two-thirds of participants prefer FAVs over CVs in a scenario where FAVs are available for sale and include a steering wheel. Second, we conduct qualitative content analysis of transcript data to investigate consumer engagement with automobility upon access to privately-owned and shared FAVs. We apply a conceptual framework of consumer “automobility engagement”, considering preferences for car ownership and residential location, car use emotions, symbolic and societal perceptions, and social norms. We find that minorities of participants expect to own fewer cars or change residential preferences following access to FAVs. Results also indicate that FAVs largely reproduce the symbolic and social significance of car ownership. Contrasting with these results, several participants expect FAVs to reduce automobility impacts on society at large. We conclude that FAV adoption may reproduce existing consumer engagement with automobility and discuss implications for transport emissions and policy.