Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

This Is Your Brain on Autopilot: Neural Indices of Driver Workload and Engagement During Partial Vehicle Automation

Objective This research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving. Background There is concern that the introduction of automated technology in vehicles may lead to low driver stimulation and subsequent disengagement from the driving environment. Simulator-based studies have examined the effect of automation on a driver’s cognitive state, but it is unknown how the conclusions translate to on-road driving. Electroencephalographic (EEG) measures of frontal theta and parietal alpha can provide insight into a driver’s mental workload and visual engagement while driving under various conditions. Method EEG was recorded from 71 participants while driving on the roadway. We examined two age cohorts, on two different highway configurations, in four different vehicles, with partial vehicle automation both engaged and disengaged. Results Analysis of frontal theta and parietal alpha power revealed that there was no change in mental workload or visual engagement when driving manually compared with driving under partial vehicle automation. Conclusion Drivers new to the technology remained engaged with the driving environment when operating under partial vehicle automation. These findings suggest that the concern surrounding driver disengagement under vehicle automation may need to be tempered, at least for drivers new to the experience. Application These findings expand our understanding of the effects of partial vehicle automation on drivers’ cognitive states.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

Evaluation of Driver Visual Demand on Three-Dimensional Rural Highway Alignments

This research focuses on evaluating driver visual demand on three-dimensional (3D) highway alignments consisting of combined horizontal and vertical alignments which is an important part of highway design consistency research. Using a driving simulator, ten hypothetical 2D and 3D alignments for two-lane rural highways were developed, following the standard guidelines of the Transportation Association of Canada (TAC) and the American Association of State Highway Transportation Officials (AASHTO). Fifteen driver subjects drove in the simulator. The data relating to visual demand information were processed and analysed using Microsoft Excel and SAS statistical software. The results indicated that visual demand on 3D curves varies widely with the inverse of radius of horizontal curvature and the inverse of K value of vertical curvature. Age played another important role on visual demand. Models for evaluating visual demand on 3D highway alignments were developed for curves and tangents. The models developed in this study have been applied to horizontal and 3D alignments to carry out a design consistency evaluation. In addition, GIS virtual reality technique was applied to present the visual demand results for a real highway on the 3D visualization model. 3D visualization not only offers a better understanding of driver workload along the highway, but also represents an important tool to effectively manage information.

Evaluation of Driver Visual Demand on Three-Dimensional Rural Highway Alignments

This research focuses on evaluating driver visual demand on three-dimensional (3D) highway alignments consisting of combined horizontal and vertical alignments which is an important part of highway design consistency research. Using a driving simulator, ten hypothetical 2D and 3D alignments for two-lane rural highways were developed, following the standard guidelines of the Transportation Association of Canada (TAC) and the American Association of State Highway Transportation Officials (AASHTO). Fifteen driver subjects drove in the simulator. The data relating to visual demand information were processed and analysed using Microsoft Excel and SAS statistical software. The results indicated that visual demand on 3D curves varies widely with the inverse of radius of horizontal curvature and the inverse of K value of vertical curvature. Age played another important role on visual demand. Models for evaluating visual demand on 3D highway alignments were developed for curves and tangents. The models developed in this study have been applied to horizontal and 3D alignments to carry out a design consistency evaluation. In addition, GIS virtual reality technique was applied to present the visual demand results for a real highway on the 3D visualization model. 3D visualization not only offers a better understanding of driver workload along the highway, but also represents an important tool to effectively manage information.

Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory

Rapid and correct estimation of driver lane change intention plays an important role in the advanced driver assistance system (ADAS), which could make the driver improve the reliability of the ADAS system and help to decrease driver workload. In this study, a method based on the long short-term memory network (LSTM) and Dempster–Shafer evidence theory is proposed. The model consists of a preliminary decision-making label and a final decision-making label. Driver visual information, head orientation, and vehicle dynamics are collected by preliminary decision-making label. Then, LSTM is used to calculate the initial probability of the driver lane change (left, right, and lane keeping) maneuver intention. The outputs of LSTM are normalized and assigned a basic probability by the Dempster–Shafer evidence theory. The final decision-making label analyzes the information and outputs the probability of each lane change intention and the decision is to identify the driver's current intention. The experimental results show that the accuracy of the model is 90.7% for the intention of changing left and 89.1% for the intention of changing right. The outcome of this work is an essential component for all levels of road vehicle automation.

Effect of Two Lane Non-Urban Highway Geometry on Workload Profile of Drivers

Transportation engineers play an important role to achieve zero- crash vision of the Government. The onus for occurrence of road crashes at under-designed and poorly constructed roads lies on the shoulders of transportation engineers. To ensure safe and comfortable driving, it is essential and necessary to evaluate the geometric design of roads, especially highways, from the perspective of the vehicle drivers. If the road is of consistent design, the driver can achieve smooth and safe driving. Inconsistent design of roads can confuse a driver and it may lead to unnecessary speed changes and even may result in unfavourable level of crashes. This paper attempts to study how the highway geometry affects the driver workload at horizontal curves and curves with gradient on two lane non-urban highways. The driver workload is assessed by measuring variations in physiological conditions of subject driver while driving in a test car under real field conditions. Heart rate (HR) and galvanic skin response (GSR) of drivers are continuously recorded using sensors attached to the driver’s ear and fingers respectively to develop a continuous profile of driver workload at varying highway geometry. The variations in heart rate from tangent sections to succeeding curve sections are determined to understand the effect of curve geometry on heart rate. The geometrical data such as radius of curvature, superelevation, sight distance, gradient and tangent length are collected from the selected study stretches. The study revealed that the inconsistent design of roads leads to large variations in heart rate and galvanic skin response. Consequently, crash frequency is found to be higher at such locations. The outcome of the study will help highway designers to design safer roads. The outcome of the study throws light on safety evaluation of highway geometry and will be helpful in developing tools and guidelines for designing safer roads.