Prediction of effort and eye movement measures from driving scene components

For transitions of control in automated vehicles, driver monitoring systems (DMS) may need to discern task difficulty and driver preparedness. Such DMS require models that relate driving scene components, driver effort, and eye measurements. Across two sessions, 15 participants enacted receiving control within 60 randomly ordered dashcam videos (3-second duration) with variations in visible scene components: road curve angle, road surface area, road users, symbols, infrastructure, and vegetation/trees while their eyes were measured for pupil diameter, fixation duration, and saccade amplitude. The subjective measure of effort and the objective measure of saccade amplitude evidenced the highest correlations (r = 0.34 and r = 0.42, respectively) with the scene component of road curve angle. In person-specific regression analyses combining all visual scene components as predictors, average predictive correlations ranged between 0.49 and 0.58 for subjective effort and between 0.36 and 0.49 for saccade amplitude, depending on cross-validation techniques of generalization and repetition. In conclusion, the present regression equations establish quantifiable relations between visible driving scene components with both subjective effort and objective eye movement measures. In future DMS, such knowledge can help inform road-facing and driver-facing cameras to jointly establish the readiness of would-be drivers ahead of receiving control.     

Evaluation of an integrated lighting-warning system on motorists’ yielding at unsignalized crosswalks during nighttime

Drivers’ yielding behavior to pedestrians during nighttime was assessed in seven different conditions of crosswalk lighting: (a) baseline condition with standard road lighting; (b) enhanced LED lighting that increased lighting level from 70 to 120 lx; (c) flashing orange beacons on top of the backlit pedestrian crossing sign; (d) in-curb LED strips on the curbsides of the zebra crossing with steady light emission; (e) in-curb LED strips with flashing light emission; (d) all previous devices activated with in-curb LED strips in steady mode; (e) all previous devices activated with in-curb LED strips in flashing mode. For every condition 100 trials were recorded with a staged pedestrian that initiated a standardized crossing when a vehicle was approaching. The frequency of drivers’ yielding was computed for each condition. A significant increase for yielding compliance was recorded from standard road lighting to enhanced dedicated lighting (19–38.21%), and from enhanced dedicated lighting to the seventh condition with the flashing beacons and the flashing in-curb LED strips activated (38.21–63.56%). The results showed that the integrated lighting-warning system for pedestrian crossings was effective in increasing motorists’ yielding to pedestrians during nighttime.     

Cognitive-behavioral treatment of high anger drivers

Relaxation and cognitive-relaxation interventions were compared to a no treatment control in the treatment of high anger drivers. The cognitive portion of the cognitive-relaxation condition adapted the style of Beck's cognitive therapy, particularly use of Socratic questions and behavioral experiments and tryouts, to driving anger reduction. Both interventions lowered indices of driving anger and hostile and aggressive forms of expressing driving anger and increased adaptive/constructive ways of expressing driving anger. The cognitive-relaxation intervention also lowered the frequency of risky behavior. Both interventions lowered trait anger as well. Limitations and implications for treatment and research were discussed.     

A confirmatory factor analysis on the DSM-IV ADHD and ODD symptoms: what is the best model for the organization of these symptoms?

Confirmatory factor analysis (CFA) was used to evaluate five different models for the organization of the DSM-IV ADHD and oppositional defiant disorder (ODD) symptoms (Model 1: a single factor model; Model 2: an ADHD and ODD two factor model; Model 3a: an inattention (INA), hyperactivity/impulsivity (HYP/IMP), and ODD three factor model; Model 3b: an INA, HYP/IMP, and ODD three factor model where the three IMP symptoms cross-load on the ODD factor; Model 4: an INA, HYP, IMP, and ODD four factor model). To evaluate these models, maternal ratings of ADHD and ODD symptoms were obtained at outpatient pediatric clinics on 742 children not in treatment and 91 children in treatment for ADHD. Model 3a resulted in a good fit as well as a significantly better fit than Model 2. Model 3a was also equivalent across treatment status, gender, and age groupings for the most part. Though Models 3b and 4 provided a statistically better fit than Model 3a, the improvement in fit was small and other model selection criteria argued against these more complex models.     

How consumer drivers construe vehicle performance: Implications for electric vehicles

Electric vehicles (EVs) have emerged as potentially important contenders as low carbon vehicles. However from the perspectives of consumer (non-commercial) drivers, all types of EVs have limitations such as short range and higher cost that are significant barriers to widespread uptake. To displace a significant fraction of conventional vehicles, they may need to offer consumer drivers specific advantages that offset these limitations. Better performance might be such an advantage, since electric powertrains can offer performance benefits such as quieter operation and higher torque at low speeds. This qualitative study explored how vehicle performance is construed by consumer drivers, using a repertory grid approach to elicit drivers’ personal constructs. Drivers were found to construe performance in terms of two main dimensions, both situationally specific: dynamic performance (involving acceleration, power, and responsiveness during pulling away, overtaking and hill climbs) and cruising performance (involving smoothness and low noise, during high speed cruising on highways). Users of gasoline fuelled cars emphasised dynamic performance more than did users of diesel fuelled cars, but the opposite was the case for cruising performance. A conceptual model based on the findings could help focus design efforts on those aspects of performance that are most directly salient to drivers.     

Temporal fluctuations in driving demand: The effect of traffic complexity on subjective measures of workload and driving performance

Traffic density has been shown to be a factor of traffic complexity which influences driver workload. However, little research has systematically varied and examined how traffic density affects workload in dynamic traffic conditions. In this driving simulator study, the effects of two dynamically changing traffic complexity factors (Traffic Flow and Lane Change Presence) on workload were examined. These fluctuations in driving demand were then captured using a continuous subjective rating method and driving performance measures. The results indicate a linear upward trend in driver workload with increasing traffic flow, up to moderate traffic flow levels. The analysis also showed that driver workload increased when a lane change occurred in the drivers’ forward field of view, with further increases in workload when that lane change occurred in close proximity. Both of these main effects were captured via subjective assessment and with driving performance parameters such as speed variation, mean time headway and variation in lateral position. Understanding how these traffic behaviours dynamically influence driver workload is beneficial in estimating and managing driver workload. The present study suggests possible ways of defining the level of workload associated with surrounding traffic complexity, which could help contribute to the design of an adaptive workload estimator.     

Anger,aggression and road rage behaviour in Malaysian drivers

This study tested the four factor structure of the Driving Anger Expression Inventory (DAX) in a sample of young Malaysian drivers and the relationship these factors had with several other variables. Confirmatory Factor Analysis broadly supported the four factor solution of the DAX, being: Personal Physical Aggressive Expression, Use of a Vehicle to Express Anger, Verbal Aggressive Expression and Adaptive/Constructive expression. The short version of the Driving Anger Scale was positively correlated with the three types of aggressive responses and not surprisingly with a variable comprised of all three types of aggressive responses (Total Aggressive Expression). Total Aggressive Expression was higher for males and negatively related to age, years licensed and slower preferred driving speed. All three of the aggressive forms of expression had significant relationships with crash-related conditions, such as: loss of concentration, losing control of their vehicle, having received a ticket and involvement in near-misses. In particular, all three of the aggressive forms of expression had significant relationships with losing control of the vehicle and Total Aggressive Expression was correlated with all crash-related conditions. In addition, Personal Physical Aggressive Expression and Total Aggressive Expression were both significantly related to crash involvement.     

Measuring drivers’ visual information needs during braking: A simulator study using a screen-occlusion method

It is commonly accepted that vision plays an important role in car braking, but it is unknown how people brake in the absence of visual information. In this simulator study, we measured drivers’ braking behaviour while they had to stop their car at designated positions on the road. The access to visual information was manipulated by occluding the screen at the start of half of the braking trials, while the temporal demand was manipulated by varying the time-to-arrival (TTA). Results showed that for the longer TTA values (⩾6 s), participants in the occlusion condition stopped too early and at variable positions on the road as compared to the control condition. In the occlusion condition, participants were likely to apply an intermediate brake pedal depression, whereas in the control condition participants more often applied low or high pedal depressions. The results are interpreted in light of a distance estimation test, in which we found that participants underestimated the actual distance by 70%.     

A field investigation of red-light-running in Shanghai,China

Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0–0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed.     

Developing the Impulsive Driver Behavior Scale

The aim of the present study is to develop a driving context specific impulsivity scale. First, a qualitative study was conducted by interviewing 20 individual drivers to develop the scale items based on the definitions of the basic impulsivity dimensions in the literature. Then, a quantitative study with a total of 506 individual drivers to examine the psychometric qualities of the newly developed Impulsive Driver Behavior Scale. In addition, the variance in driver behaviors, namely violations, errors, lapses and positive driver behaviors measured by the Driver Behavior Questionnaire (DBQ; Reason, Manstead, Stradling, Baxter, & Campbell, 1990) explained by the general impulsivity scales in the literature and the newly developed scale were compared. In all the comparisons, Impulsive Driver Behavior Scale explained higher amount of variance than the general impulsivity scales. Finally, it was found that the newly developed driving context specific impulsivity scale had incremental validity over the general impulsivity scales in predicting driver behaviors.