Identifying and Incorporating Driver Behavior Variables into Crash Prediction Models

All travelers are exposed to the risk for crashes on the road, as none of the roadways are entirely safe. Under Vision Zero, improving traffic safety on our nation’s highways is and will continue to be one of the most pivotal tasks on the national transportation agenda. For decades, researchers and transportation professionals have strived to identify causal relationships between crash occurrence and roadway geometry, and traffic-related variables on the mission of creating a safe environment for the traveling public. Although great achievements have been witnessed such as the publication of the Highway Safety Manual (HSM), research is rather limited in the area of incorporating driver behavior variables into safety modeling. As driver errors are responsible for more than 90 percent of crashes occurred, excluding such important information could cause ineffective, inaccurate, and incorrect prediction results and parameter inferences. The primary reasons for this research void are the lack of driver information and methods for integrating driver data with roadway and traffic characteristics. Standard procedures for collecting and archiving driver behavior data do not exist, as highway agencies are not obligated to collect them. The most relevant source for driver behavior information is perhaps the crash report where police officers may record driver conditions and the possible driver factors contributing to the crash. However, such information is not available to near misses, traffic conflicts and non-crash traffic events where good behaviors prevail. As a result, unobserved data heterogeneity will induce data overdispersion issues which are a significant limiting factor to safety modeling. Furthermore, the conventional approach to treating crashes as originated from a single risk source also induces heterogeneity in crash data and yields biased parameter estimates. Thus, a statistically rigorous methodology is in urgent need to consider the consequence of missing critical driver information in a crash model as well as to distinguish between distinct risk generating sources of a crash event when the driver information is available. This dissertation contributes to the prediction of crash frequency and severity by explicitly considering human factors and driver behaviors in the modeling process. This endeavor began with a comprehensive literature review that identified and addressed data needs, technical issues, and latest development on the incorporation of human factors in safety analysis; and concluded with analytical framework and modeling alternatives to quantify driver behavior being proposed, developed and evaluated. Given myriads of data elements to be explored, availability of contributing factors and crash data issues, a three-pronged modeling approach was adopted to accommodate a broad spectrum of data aggregated over areas, sites and crash events. This approach was informed by the complex nature of crashes involving highway geometry, traffic exposure, contextual factors, driver characteristics, vehicle factors, as well as the interactions among them. The availability of direct or surrogate measures of crash contributing factors varies by spatial unit. To give an example, socioeconomic and demographic features of the driving population are available at census tract; roadway geometry and traffic variables are available for segments and intersections; while specific driver conditions are only collected when a crash took place. With the flexibility in spatial context and risk generating sources, the three-pronged approach provides direct benefits to guide different safety applications such as planning, design, and operations; and informs different programs such as engineering and enforcement. The area-based crash models were developed to incorporate human factors and driver behavior in the form of socioeconomic and demographic data. In particular, behavior-based crash prediction models for speed and alcohol-related crashes were developed, respectively. Results showed that driver behavior-related crashes were more correlated with socioeconomic and demographic variables than traffic and trip-related explanatory variables. The site-specific crash models were exploited to address the effect of human factors and driver behaviors in two fronts: 1) developing rigorous statistical models to account for unobserved heterogeneity induced overdispersion when driver behavior information is not available, 2) treating behavior variables as a separate risk source in a prediction model. The first pursuit leads to the development of a mixed distribution random parameter model to explicitly account for unobserved heterogeneity. The second pursuit results in the development of a multivariate multiple risk source regression model to simultaneously predict crash count and severity. Modeling results show better model performance and valid model inferences for quantifying the effect of driver factors on crash occurrence can be achieved with proposed multiple risk source models. The event-oriented models were utilized to evaluate the interaction between human factors and engineering variables in a crash event. Driver errors were categorized by the driver’s action during a crash on a roadway segment. The modeling results identified many highway geometric features, traffic conditions, and driver characteristics as statistically correlated to different types of driver mistakes. An exploratory analysis was followed to evaluate the effect of driver mistakes on the crash injury outcomes. The dissertation demonstrates the strength of using diverse methods and models under various circumstances to incorporate human factors and driver behavior in crash prediction. The safety professionals can choose appropriate models based on their own data availability, unit of analysis, and design effective treatments or training programs. This research shares new insights to reinforce informed decision support for cumulative safety improvement of roadway network, recognizes the opportunities to address high priority safety issue areas, and determines the appropriate countermeasures

Conservation, Variability and the Modeling of Active Protein Kinases

The human proteome is rich with protein kinases, and this richness has made the kinase of crucial importance in initiating and maintaining cell behavior. Elucidating cell signaling networks and manipulating their components to understand and alter behavior require well designed inhibitors. These inhibitors are needed in culture to cause and study network perturbations, and the same compounds can be used as drugs to treat disease. Understanding the structural biology of protein kinases in detail, including their commonalities, differences and modes of substrate interaction, is necessary for designing high quality inhibitors that will be of true use for cell biology and disease therapy. To this end, we here report on a structural analysis of all available active-conformation protein kinases, discussing residue conservation, the novel features of such conservation, unique properties of atypical kinases and variability in the context of substrate binding. We also demonstrate how this information can be used for structure prediction. Our findings will be of use not only in understanding protein kinase function and evolution, but they highlight the flaws inherent in kinase drug design as commonly practiced and dictate an appropriate strategy for the sophisticated design of specific inhibitors for use in the laboratory and disease therapy

Probabilistic Framework for Behavior Characterization of Traffic Participants Enabling Long Term Prediction

This research aims at developing new methods that predict the behaviors of the human driven traffic participants to enable safe operation of autonomous vehicles in complex traffic environments. Autonomous vehicles are expected to operate amongst human driven conventional vehicles in the traffic at least for the next few decades. For safe navigation they will need to infer the intents as well as the behaviors of the human traffic participants using extrinsically observable information, so that their trajectories can be predicted for a time horizon long enough to do a predictive risk analysis and gracefully avert any risky situation. This research approaches this challenge by recognizing that any maneuver performed by a human driver can be divided into four stages that depend on the surrounding context: intent determination, maneuver preparation, gap acceptance and maneuver execution. It builds on the hypothesis that for a given driver, the behavior not only spans across these four maneuver stages, but across multiple maneuvers. As a result, identifying the driver behavior in any of these stages can help characterize the nature of all the subsequent maneuvers that the driver is likely to perform, thus resulting in a more accurate prediction for a longer time horizon. To enable this, a novel probabilistic framework is proposed that couples the different maneuver stages of the observed traffic participant together and associates them to a driving style. To realize this framework two candidate Multiple Model Adaptive Estimation approaches were compared: Autonomous Multiple Model (AMM) and Interacting Multiple Model(IMM) filtering approach. The IMM approach proved superior to the AMM approach and was eventually validated using a trajectory extracted from a real world dataset for efficacy. The proposed framework was then implemented by extending the validated IMM approach with contextual information of the observed traffic participant. The classification of the driving style of the traffic participant (behavior characterization) was then demonstrated for two use case scenarios. The proposed contextual IMM (CIMM) framework also showed improvements in the performance of the behavior classification of the traffic participants compared to the IMM for the identified use case scenarios. This outcome warrants further exploration of this framework for different traffic scenarios. Further, it contributes towards the ongoing endeavors for safe deployment of autonomous vehicles on public roads

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Why we interact : on the functional role of the striatum in the subjective experience of social interaction

Acknowledgments We thank Neil Macrae and Axel Cleeremans for comments on earlier versions of this manuscript. Furthermore, we are grateful to Dorothé Krug and Barbara Elghahwagi for their assistance in data acquisition. This study was supported by a grant of the Köln Fortune Program of the Medical Faculty at the University of Cologne to L.S. and by a grant “Other Minds” of the German Ministry of Research and Education to K.V.Peer reviewedPreprin