Modeling Pipeline Driving Behaviors: A Hidden Markov Model Approach

Driving behaviors at intersection are complex because drivers have to perceive more traffic events than normal road driving and thus are exposed to more errors with safety consequences. Drivers make real-time responsesin a stochastic manner. This paper presents our study using Hidden Markov Models (HMM) to model driving behaviors at intersections. Observed vehicle movement data are used to build up the model. A single HMM is used to cluster the vehicle movements when they are close to intersection. The re-estimated clustered HMMs provide better prediction of the vehicle movements compared to traditional car-following models. Only through vehicles on major roads are considered in this paper.

Multiple Approaches to Absenteeism Analysis

Absenteeism research has often been criticized for using inappropriate analysis. Characteristics of absence data, notably that it is usually truncated and skewed, violate assumptions of OLS regression; however, OLS and correlation analysis remain the dominant models of absenteeism research. This piece compares eight models that may be appropriate for analyzing absence data. Specifically, this piece discusses and uses OLS regression, OLS regression with a transformed dependent variable, the Tobit model, Poisson regression, Overdispersed Poisson regression, the Negative Binomial model, Ordinal Logistic regression, and the Ordinal Probit model. A simulation methodology is employed to determine the extent to which each model is likely to produce false positives. Simulations vary with respect to the shape of the dependent variable\u27s distribution, sample size, and the shape of the independent variables\u27 distributions. Actual data,based on a sample of 195 manufacturing employees, is used to illustrate how these models might be used to analyze a real data set. Results from the simulation suggest that, despite methodological expectations, OLS regression does not produce significantly more false positives than expected at various alpha levels. However, the Tobit and Poisson models are often shown to yield too many false positives. A number of other models yield less than the expected number of false positives, thus suggesting that they may serve well as conservative hypothesis tests

Influence of personal mobile phone ringing and usual intention to answer on driver error

Given evidence of effects of mobile phone use on driving, and also legislation, many careful drivers refrain from answering their phones when driving. However, the distracting influence of a call on driving, even in the context of not answering, has not been examined. Furthermore, given that not answering may be contrary to an individual’s normal habits, this study examined whether distraction caused by the ignored call varies according to normal intention to answer whilst driving. That is, determining whether the effect is more than a simple matter of noise distraction. Participants were 27 young drivers (18-29 years), all regular mobile users. A Theory of Planned Behaviour questionnaire examined predictors of intention to refrain from answering calls whilst driving. Participants provided their mobile phone number and were instructed not to answer their phone if it were to ring during a driving simulation. The simulation scenario had seven hazards (e.g. car pulling out, pedestrian crossing) with three being immediately preceded by a call. Infractions (e.g. pedestrian collisions, vehicle collisions, speed exceedances) were significantly greater when distracted by call tones than with no distraction. Lower intention to ignore calls whilst driving correlated with a larger effect of distraction, as was feeling unable to control whether one answered whilst driving (Perceived Behavioural Control). The study suggests that even an ignored call can cause significantly increased infractions in simulator driving, with pedestrian collisions and speed exceedances being striking examples. Results are discussed in relation to cognitive demands of inhibiting normal behaviour and to drivers being advised to switch phones off whilst driving

Guidance and Navigation Challenges for a Mars Ascent Vehicle

This work presents studies and analysis in support of a Mars Ascent Vehicle as part of a Martian Sample Return campaign. The vehicle design has been ongoing, with rapid development of a 6 Degree of Freedom simulation to capture full vehicle dispersions and integrated performance of vehicle, guidance, navigation and control. The maturation of this simulation is presented to provide an overview of its capabilities added over the past year of effort. The results describe in detail guidance algorithm development to increase the systems robustness to thrust sensitivities. Navigation performance and sensitivity analysis are included to describe the capabilities of the current design as well as identify primary drivers of insertion performance. Lastly, integrated vehicle 6DOF statistical results are presented to provide insight into the nominal performance of the current vehicle and insight into system-level drivers. Future work is described to outline the continuing maturation and development of the MSR MAV ascent vehicle

Sensation seeking, non-contextual decision making, and driving abilities as measured through a moped simulator.

The general aim of the present study was to explore the relations between driving style (assessed through a moped riding simulator) and psychological variables such as sensation seeking and decision making. Because the influences of sensation seeking and decision making on driving styles have been studied separately in the literature, we have tried to investigate their mutual relations so as to include them in a more integrated framework. Participants rode the Honda Riding Trainer (HRT) simulator, filled in the Sensation Seeking Scale V (SSS V), and performed the Iowa Gambling Task (IGT). A cluster analysis of the HRT riding indexes identified three groups: Prudent, Imprudent, and Insecure riders. First, the results showed that Insecure males seek thrills and adventure less than both Prudent males and Insecure females, whereas Prudent females are less disinhibited than both Prudent males and Insecure females. Moreover, concerning the relations among SSS, decision making as measured by the IGT, and riding performance, high thrill and adventure seekers performed worse in the simulator only if they were also bad decision makers, indicating that these two traits jointly contribute to the quality of riding performance. From an applied perspective, these results also provide useful information for the development of protocols for assessing driving abilities among novice road users. Indeed, the relation between risk proneness and riding style may allow for the identification of road-user populations who require specific training

Pedestrians\u27 Receptivity Toward Fully Autonomous Vehicles

Fully Autonomous Vehicles (FAVs) have the potential to provide safer vehicle operation and to enhance the overall transportation system. However, drivers and vehicles are not the only components that need to be considered. Research has shown that pedestrians are among the most unpredictable and vulnerable road users. To achieve full and successful implementation of FAVs, it is essential to understand pedestrian acceptance and intended behavior regarding FAVs. Three studies were developed to address this need: (1) development of a standardized framework to investigate pedestrians’ behaviors for the U.S. population; (2) development of a framework to evaluate their receptivity of FAVs; and (3) investigation of the influence of the external interacting interfaces of FAVs on pedestrian receptivity toward them. The pedestrian behavior questionnaire (PBQ) categorized pedestrian general behaviors into five factors: violations, errors, lapses, aggressive behaviors, and positive behaviors. The first four factors were found to be both valid and reliable; the positive behavior scale was not found to be reliable nor valid. A long (36-item) and a short (20-items) versions of the PBQ were validated by regressing scenario-based survey responses to the fiveactor PBQ subscale scores. The pedestrian receptivity questionnaire for FAVs (PRQF) consisted of three subscales: safety, interaction, and compatibility. This factor structure was verified by a confirmatory factor analysis and the reliability of each subscale was confirmed. Regression analyses showed that pedestrians’ intention to cross the road in front of a FAV was significantly predicted by both safety and interaction scores, but not by the compatibility score. On the other hand, acceptance of FAVs in the existing traffic system was predicted by all three subscale scores. Finally, an experimental study was performed to expose pedestrians to a simulated environment where they could experience a FAV. The FAV in the simulated environment was either equipped with external features (audible and/or visual) or had no external (warning) feature. The least preferred options were the FAVs with no features and those with a smiley face but no audible cue. The most preferred interface option, which instilled confidence for crossing in front of the FAV, was the walking silhouette

Exploring Predictors of Older Adults\u27 Performance on a Novel Driving Simulator Task

On a per-mile driven basis, older adults are at increased risk of being involved in an automobile accident. The development and implementation of driving assessment tools is necessary to inform decisions about driving reduction and cessation. Driving simulators are one method of assessing driving performance and safety, however many simulators are cost-prohibitive for most researchers and clinicians. Additionally, while driving performance has been previously explored with respect to clinical populations (e.g., Alzheimer’s Disease), less work has evaluated this topic in a cognitively healthy sample. The present study sought to determine whether a novel, cost-effective driving simulator (Assetto Corsa (AC)) might be useful in the evaluation of driving performance in a sample of cognitively healthy older adults. A total of 53 participants completed a battery of paper-and-pencil and computerized cognitive performance measures and self-reports regarding their driving safety and behaviors, and a subset of participants (n = 35) completed the driving simulator task. Hierarchical regressions revealed that paper-and-pencil measures of simple attention and executive functioning and a computerized measure of processing speed were associated with aspects of driving simulator performance. Pearson correlation coefficients revealed that lower self-rated driving was associated with slower completion of the simulator task, and decrements in several cognitive domains were associated with greater self-reported difficulty driving in various conditions, greater aberrant driving behaviors, and higher likelihood of having legal difficulties as a result of driving (e.g., traffic tickets). Implications for future work are discussed