Steering Wheel Reversal Rate Research Articles

Comparing the Effects of Visual Distraction in a High-Fidelity Driving Simulator and on a Real Highway

Driving simulators have been widely used in driving behavior analysis and intelligent driving algorithm development. However, the validity of driving behavior data derived from driving simulators remains unclear. In this study, 30 Chinese drivers were recruited to participate in two experiments: on-road and simulator experiments. An instrumented vehicle and a high-fidelity simulator were used in the on-road and simulator experiments, respectively, to investigate the effects of high speed (60, 80, and 100 km/h) and a visual distraction task on the lateral driving performance, including lane deviation (LD), standard deviation of the lane position (SDLP) rate, standard deviation of the steering wheel angle (SDSWA) rate, and steering wheel reversal rates (SRRs) (at levels of 1.3° and 2.5°). It was found that the visual distraction task impaired the drivers’ lane-keeping ability. Furthermore, the driving task had similar effects on the LD, SDLP rate, and SRRs (2.5°) in the on-road and simulator experiments. The effects of the driving speed on the LD, SDLP rate, and SDSWA rate were comparable in both driving environments. However, the results confirmed that even a high-fidelity driving simulator could not achieve perfect absolute validity. The results provided preliminary evidence that the high-fidelity driving simulator used in this study might be an effective tool for investigating the effects of visual distractions task on lateral driving behavior.

A Study on the Entire Take-Over Process-Based Emergency Obstacle Avoidance Behavior.

Nowadays, conditional automated driving vehicles still need drivers to take-over in the scenarios such as emergency hazard events or driving environments beyond the system's control. This study aimed to explore the changing trend of the drivers' takeover behavior under the influence of traffic density and take-over budget time for the entire take-over process in emergency obstacle avoidance scenarios. In the driving simulator, a 2 × 2 factorial design was adopted, including two traffic densities (high density and low density) and two kinds of take-over budget time (3 s and 5 s). A total of 40 drivers were recruited, and each driver was required to complete four simulation experiments. The driver's take-over process was divided into three phases, including the reaction phase, control phase, and recovery phase. Time parameters, dynamics parameters, and operation parameters were collected for each take-over phase in different obstacle avoidance scenarios. This study analyzed the variability of traffic density and take-over budget time with take-over time, lateral behavior, and longitudinal behavior. The results showed that in the reaction phase, the driver's reaction time became shorter as the scenario urgency increased. In the control phase, the steering wheel reversal rate, lateral deviation rate, braking rate, average speed, and takeover time were significantly different at different urgency levels. In the recovery phase, the average speed, accelerating rate, and take-over time differed significantly at different urgency levels. For the entire take-over process, the entire take-over time increased with the increase in urgency. The lateral take-over behavior tended to be aggressive first and then became defensive, and the longitudinal take-over behavior was defensive with the increase in urgency. The findings will provide theoretical and methodological support for the improvement of take-over behavior assistance in emergency take-over scenarios. It will also be helpful to optimize the human-machine interaction system.

Effects of request-to-intervene message contents on driver performance and safety under conditionally automated driving system

Abstract The current study addressed the issue of designing request-to-intervene (RtI) of a level 3 automated driving system. We hypothesized that providing the driver with step-by-step operating instructions and surrounding traffic environmental information in the RtI message could smooth driver takeover performance and enhance safety. To test this hypothesis, we conducted an experiment with a driving simulator. In the experiment, 40 participants were divided into four Human-Machine Interface (HMI) groups: Beeps HMI (Baseline), Operating Instruction HMI, Environmental HMI, and Combined HMI. The results revealed that both additional operating instructions and environmental information could lower the steering wheel reversal rate. The combination of operating instructions and surrounding traffic environmental information contributed to reducing the accident rate.

What is the difference in driver’s lateral control ability during naturalistic distracted driving and normal driving? A case study on a real highway

Driver distraction is widely recognized as a major contributor to traffic crashes. Although the effect of distraction on simulated driving performance has been studied extensively, comparatively little research based on field tests has been performed on the effects of high driving speeds on lateral driving performance during naturalistic distraction (the driver was unaware of the research topic). In this study, an instrumented vehicle is used to examine the impact of speed and naturalistic visual distraction (rear vehicle’s velocity and relative distance estimation) on a driver’s ability to keep in the lane. Similar to results from previous studies, visual distraction resulted in an impaired ability to keep in a lane compared to normal driving. Further investigation of steering control parameters showed an increase in steering wheel reversal rates (SRRs at 1.3° and 2.5° levels) and the standard deviation of steering wheel acceleration (SDSWA). The results of this study indicated that the standard deviation of lane positioning (SDLP) and trajectory offset (TO) increased as speed increased. As speed increased, the growth rates of SDLP and TO in the visual distraction task were the same as that in normal driving. Moreover, the SRRs and steering wheel acceleration (SWA) decreased with increased speed. As speed increased, the growth rates of SRRs and SWA during a visual distraction task were the same as that during normal driving. These results suggest that driving speed has a similar effect on driving performance during both distracted driving and normal driving.

Detection of Cognitive and Visual Distraction Using Radial Basis Probabilistic Neural Networks

This paper suggests a real-time method for detecting a driver’s cognitive and visual distraction using lateral driving performance measures. The algorithm adopts radial basis probabilistic neural networks (RBPNNs) to construct classification models. In this study, combinations of two driving performance data measures, including the standard deviation of lane position (SDLP) and steering wheel reversal rate (SRR), were considered as measures of distraction. Data for training and testing the RBPNN models were collected under simulated conditions in which fifteen participants drove on a highway. While driving, they were asked to complete auditory recall tasks or arrow search tasks to create cognitively or visually distracted driving periods. As a result, the best performing model could detect distraction with an average accuracy of 78.0 %, which is a relatively high accuracy in the human factors domain. The results demonstrated that the RBPNN model using SDLP and SRR could be an effective distraction detector with easy-to-obtain and inexpensive inputs.

Visual-Manual Distraction Detection Using Driving Performance Indicators With Naturalistic Driving Data

This paper investigates the problem of driver distraction detection using driving performance indicators from onboard kinematic measurements. First, naturalistic driving data from the integrated vehicle-based safety system program are processed, and cabin camera data are manually inspected to determine the driver’s state (i.e., distracted or attentive). Second, existing driving performance metrics, such as steering entropy, steering wheel reversal rate, and lane offset variance, are reviewed against the processed naturalistic driving data. Furthermore, a nonlinear autoregressive exogenous (NARX) driving model is developed to predict vehicle speed based on the range (distance headway), range rate, and speed history. For each driver, the NARX model is then trained on the attentive driving data. We show that the prediction error is correlated with driver distraction. Finally, two features, steering entropy and mean absolute speed prediction error from the NARX model are selected, and a support vector machine is trained to detect driving distraction. Prediction performances are reported.

Driving with central field loss III: vehicle control

BackgroundVisual impairment associated with central field loss may make vehicle control more difficult due to the degraded view of the road. We evaluated how central field loss affects vehicle control in a driving simulator.MethodsNineteen participants with binocular central field loss (acuity 6/9 to 6/60) and 15 controls with normal vision drove 10 scenarios, each about eight to 12-minutes. Speed, lane offset and steering wheel reversal rate were measured on straights, left and right curves, along city (approximately 50-km/h) and rural highway (approximately 100-km/h) routes. Following distance was measured on two city straight segments.ResultsSubjects with central field loss had higher steering wheel reversal rates (0.55 versus 0.45 reversals per second, p = 0.015), suggesting that the steering task was more demanding for them, requiring more steering corrections; however, they did not differ in other performance measures. Nearly all maintained a safe following distance, although they were more likely than controls with normal vision to lose sight of the lead car in scenarios that required following a car.ConclusionsMost measures of vehicle control did not significantly differ between participants with central field loss and those with normal vision; however, the higher steering wheel reversal rates suggest that, in compensating for their vision impairment, drivers with central field loss had to allocate extra steering effort to maintain their lane position, which in turn could reduce attentional resources for other driving tasks.

Identifying cognitive distraction using steering wheel reversal rates

The influence of driver distraction on driving performance is not yet well understood, but it can have detrimental effects on road safety. In this study, we examined the effects of visual and non-visual distractions during driving, using a high-fidelity driving simulator. The visual task was presented either at an offset angle on an in-vehicle screen, or on the back of a moving lead vehicle. Similar to results from previous studies in this area, non-visual (cognitive) distraction resulted in improved lane keeping performance and increased gaze concentration towards the centre of the road, compared to baseline driving, and further examination of the steering control metrics indicated an increase in steering wheel reversal rates, steering wheel acceleration, and steering entropy. We show, for the first time, that when the visual task is presented centrally, drivers’ lane deviation reduces (similar to non-visual distraction), whilst measures of steering control, overall, indicated more steering activity, compared to baseline. When using a visual task that required the diversion of gaze to an in-vehicle display, but without a manual element, lane keeping performance was similar to baseline driving. Steering wheel reversal rates were found to adequately tease apart the effects of non-visual distraction (increase of 0.5° reversals) and visual distraction with offset gaze direction (increase of 2.5° reversals). These findings are discussed in terms of steering control during different types of in-vehicle distraction, and the possible role of manual interference by distracting secondary tasks.

Impact of Repeated Exposure to a Multilevel Working Memory Task on Physiological Arousal and Driving Performance

Drivers are increasingly confronted with a multitude of simultaneous secondary tasks. Consequently, the ability to manage this elevated workload is a rising concern. A growing body of evidence has suggested that changes in physiology, such as heart rate (HR) and skin conductance level (SCL), along with driving metrics, such as velocity and steering wheel reversal rates, could allow a real-time monitoring of this workload. However, laboratory research often does not evaluate drivers' experience of secondary tasks over multiple repetitions or time. The present study examined the sensitivity of a variety of performance and physiological measures to changes in workload and assessed the impact of repeated exposure to a set of n-back calibration tasks. When averaged across repeated exposures, the results closely followed previous literature: increased working memory load from the n-back tasks resulted in significant increases in HR and SCL. Although each repetition of the task showed similar changes as workload increased (for example, increased HR during more difficult tasks), this effect was consistently higher for the initial compared with second and third exposures to each level of the task. Furthermore, some measures (HR, SCL) were more reliable over time than were others (steering wheel reversals, velocity). These findings may be related in part to the emotional arousal of being asked to do a task for the first time. The implications are discussed.

Analysis of human-machine cooperation when driving with different degrees of haptic shared control.

This study investigated human-machine cooperation when driving with different degrees of a shared control system. By means of a direct intervention on the steering wheel, shared control systems partially correct the vehicle's trajectory and, at the same time, provide continuous haptic guidance to the driver. A crucial point is to determine the optimal level of steering assistance for effective cooperation between the two agents. Five system settings were compared with a condition in which no assistance was present. In addition, road visibility was manipulated by means of additional fog or self-controlled visual occlusions. Several performance indicators and subjective assessments were analyzed. The results show that the best repartition of control in terms of cooperation between human and machine can be identified through an analysis of the steering wheel reversal rate, the steering effort and the mean lateral position of the vehicle. The best cooperation was achieved with systems of relatively low-level haptic authority, although more intervention may be preferable in poor visibility conditions. Increasing haptic authority did not yield higher benefits in terms of steering behavior, visual demand or subjective feeling.

An investigation of the relationship between the driving behavior questionnaire and objective measures of highway driving behavior

The Driver Behavior Questionnaire (DBQ) is a well-documented instrument for obtaining self-report information on aberrant driving behaviors. The DBQ contains three subscales to capture different aspects of driver behavior: errors, lapses and violations. Previous research has demonstrated a relationship between DBQ scores and retrospective and prospective crash involvement. There is little or no published information, however, on the relationship between DBQ scores and actual driving behavior that may bear some relationship to crash risk. The present study focuses on the relationship between DBQ subscales and observed highway driving behaviors. A sample of 108 drivers in self-reported good health and with a safe recent driving history was balanced by gender across three age groups (20–29, 40–49, 60–69). Prior to driving, participants completed a 24-item U.S. version of the DBQ. The relationships between subscales of the DBQ and driving behavior were examined. The results indicated that drivers with high violations scores drove faster, had poorer lateral control, changed lanes more frequently, spent more time in the left lane, and had more sudden unidirectional accelerations. High lapses scores were related to high steering wheel reversal rates and less consistent throttle control. No main effects were observed between errors scores and any of the driving behavior measures. The magnitude of the relationships observed here are likely conservative because drivers involved in accidents in the past year were excluded.

Sharing Control With Haptics

Haptic shared control was investigated as a human-machine interface that can intuitively share control between drivers and an automatic controller for curve negotiation. As long as automation systems are not fully reliable, a role remains for the driver to be vigilant to the system and the environment to catch any automation errors. The conventional binary switches between supervisory and manual control has many known issues, and haptic shared control is a promising alternative. A total of 42 respondents of varying age and driving experience participated in a driving experiment in a fixed-base simulator, in which curve negotiation behavior during shared control was compared to during manual control, as well as to three haptic tunings of an automatic controller without driver intervention. Under the experimental conditions studied, the main beneficial effect of haptic shared control compared to manual control was that less control activity (16% in steering wheel reversal rate, 15% in standard deviation of steering wheel angle) was needed for realizing an improved safety performance (e.g., 11% in peak lateral error). Full automation removed the need for any human control activity and improved safety performance (e.g., 35% in peak lateral error) but put the human in a supervisory position. Haptic shared control kept the driver in the loop, with enhanced performance at reduced control activity, mitigating the known issues that plague full automation. Haptic support for vehicular control ultimately seeks to intuitively combine human intelligence and creativity with the benefits of automation systems.

Impact of traffic environment and cognitive workload on older drivers’ behavior in simulated driving

As the use of in-vehicle technologies became more popular, there is concern about a concomitant increase in driver distraction arising from their use. While the introduction of voice recognition systems is intended to reduce the distraction due to manual operation of these units, a significant proportion of the distraction associated with their use may arise not from the manual manipulation but rather the cognitive consequences. It is also known that the risk of inattentive driving varies with age. In this study, the impact of cognitive workload and traffic environments on older drivers’ behavior was investigated in a driving simulator. To assess the impact of advancing age on driving performance degradation under a dual task condition, the performance of 63 drivers, divided into younger (20–29) and older (60–69) age groups, was evaluated. The authors also considered driving behavioral differences in the context of urban and highway driving, appropriately counterbalanced. At a specified location in the two scenarios, subjects were asked to complete a series of auditory tasks of increasing complexity. Comparisons of younger and older drivers’ driving performance, including forward velocity, speed control, standard deviation of lane position and steering wheel reversal rate, were conducted. Results indicated that age and traffic environment impact both driving performance and compensatory behavior during dual task conditions.

A Method to Distinguish Safe from Less Safe Driving

Recording biological parameters of the driver, parameters of the car, and parameters of the environment it is possible to calculate the difference between normal safe and less normal less safe driving. This could be proved in case of driving in a tired state with the parameters pulse rate, skin resistance, and steering wheel reversal rate. A method which allows to calculate a normal pattern of driving as a comparing criterion for actual driving condition is proposed using inductive entropy and inductive information.

Review of Relationships Between Steering Wheel Reversal Rate and Driving Task Demand

Previous literature and some new data on the relationship between steering wheel reversal rate (SRR) and driving task demand are discussed in terms of a set of theoretical assumptions proposed by Macdonald and Hoffmann (1978). SRR is generally expected to increase with increasing task demand; however, several recent studies found a significant decrease in SRR. It is argued that whether the relationship is positive or negative depends on the level of task difficulty relative to the driver's capacity to cope with it.