Investigating the transition from normal driving to safety-critical scenarios

Investigation of the correlation between factors associated with crash development has enabled the implementation of methods aiming to avert and control crash causation at various points within the crash sequence (Evans, 2006). Partitioning the crash sequence is important because intricated crash causation sequences can be deconstructed and effective prevention strategies can be suggested (Wu & Thor, 2015). Towards this purpose, Tingvall et al. (2009) documented the so-called integrated safety chain which described the change of crash risk on the basis of a developing sequence of events that led to a collision. This thesis examines the crash sequence development and thus, the transition from normal driving to safety critical scenarios. [Continues.

Detecting deviation from normal driving using SHRP2 NDS data

Normal driving is naturally the first stage of the crash development sequence. Investigating normal driving can be proved useful for comparisons with safety critical scenarios and also crash prevention. The better we understand it, the more effectively we can detect deviations and stop them before they culminate in crashes. This study utilises Naturalistic driving data from the Strategic Highway Research Program 2 (SHRP2) to look into normal driving scenarios. Indicators’ thresholds were assumed with influence by the literature and then the values were validated based on real world data. The paper focuses on the methodology for deriving indicators representative of baseline, uneventful driving. With the approach that is presented here, reliable thresholds for variables can be introduced, capable of detecting the deviation on its very early onset

Modeling drivers’ naturalistic driving behavior on rural two-lane curves

This dissertation examined drivers’ naturalistic driving behavior on rural two-lane curves using the Strategic Highway Research Program 2 Naturalistic Driving Study data. It is a state-of-the-art naturalistic driving study that collected more than 3,000 drivers’ daily driving behavior over two years in the U.S. The major data sources were vehicle network, lane tracking system, front and rear radar, driver demographics, driver surveys, vehicle characteristics, and video cameras. This dissertation has three objectives: 1) examine the contributing factors to crashes and near-crashes on rural two-lane curves; 2) understand drivers’ normal driving behavior on rural two lane curves; 3) evaluate how drivers continuously interact with curve geometries using functional data analysis. The first study analyzed the crashes and near-crashes on rural two-lane curves using logistic regression model. The model was used to predict the binary event outcomes using a number of explanatory variables, including driver behavior variables, curve characteristics, and traffic environments. The odds ratio of getting involved in safety critical events was calculated for each contributing factor. Furthermore, the second study focused on the analysis of drivers’ normal curve negotiation behavior on rural two-lane curves. Significant relationships were found between curve radius, lateral acceleration, and vehicle speeds. A linear mixed model was used to predict mean speeds based on curve geometry and driver factors. The third analysis applied functional data analysis method to analyze the time series speed data on four example curves. Functional data analysis was found to be a useful method to analyze the time series observations and understand driver’s behavior from naturalistic driving study. Overall, this dissertation is one of the first studies to investigate drivers’ curve negotiation behavior using naturalistic driving study data, and greatly enhanced our understanding about the role of driver behavior in curve negotiation process. This dissertation had many important implications for curve geometry design, policy making, and advanced vehicle safety system. This dissertation also discussed the opportunities and challenges of analyzing the Strategic Highway Research Program 2 Naturalistic Driving Study data, and the implications for future research

Naturalistic rapid deceleration data: Drivers aged 75 years and older.

The data presented in this article are related to the research manuscript "Predictors of older drivers' involvement in rapid deceleration events", which investigates potential predictors of older drivers' involvement in rapid deceleration events including measures of vision, cognitive function and driving confidence (A. Chevalier et al., 2016) [1]. In naturalistic driving studies such as this, when sample size is not large enough to allow crashes to be used to investigate driver safety, rapid deceleration events may be used as a surrogate safety measure. Naturalistic driving data were collected for up to 52 weeks from 182 volunteer drivers aged 75-94 years (median 80 years, 52% male) living in the suburban outskirts of Sydney. Driving data were collected using an in-vehicle monitoring device. Accelerometer data were recorded 32 times per second and Global Positioning System (GPS) data each second. To measure rapid deceleration behavior, rapid deceleration events (RDEs) were defined as having at least one data point at or above the deceleration threshold of 750 milli-g (7.35 m/s2). All events were constrained to a maximum 5 s duration. The dataset provided with this article contains 473 events, with a row per RDE. This article also contains information about data processing, treatment and quality control. The methods and data presented here may assist with planning and analysis of future studies into rapid deceleration behaviour using in-vehicle monitoring

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Investigation of driver behavior during crash and near-crash events using naturalistic driving data

Various studies demonstrated that the human factors, driver performance, and the interactions among humans and other elements in the transportation systems significantly contributed to the traffic safety and highway design. Therefore, it is critical to understand driver behaviors to reduce the likelihood of crashes and enhance the design of the highway system. The major objective of this study is to investigate driver behavior, particularly during crash and near-crash events, as well as during the preceding time intervals. Of specific interest is how drivers’ reaction times, deceleration rates, and speed selection vary under different roadway environments. The freeway non-crash and crash or near-crash events were obtained from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) dataset and the associated Roadway Information Database (RID). Due to the unique features of the data, the random effect linear regression model with a participant-specific intercept term was utilized to perform the analyses. The participants’ reaction times of crash/near-crash events were determined to have a mean value of 1.51 sec. and a standard deviation of 1.25 sec. The results of the analysis showed that reaction time varied based upon the type of crash/near-crash event, the gender of the driver, and whether the driver was distracted over the course of the driving event. The driver’s deceleration rates of crash/near-crash events were also calculated in the study. The mean and standard deviation of deceleration rates were about 9.53 ft/s2 (0.30 g) and 4.99 ft/s2 (0.15 g) respectively. The initial speed of braking, the grade of the roadway, and the type of incident presented significant influences on the deceleration rates of crash/near-crash events. Lastly, the mean and standard deviation of travel speed for non-crash and crash/near-crash events were investigated to explicitly understand the speed selection of drivers. On average, normal drivers showed higher driving speeds and less variability. Speed limits and traffic density had relatively consistent impacts on mean speed and speed variance under both baseline and crash/near-crash conditions. However, opposing effects of curves and work zones occurred on the standard deviation in travel speed between two groups. These effects suggested drivers were more likely to put themselves at risk for crashes by failing to reduce their speeds in response to these conditions. Other roadway and driver characteristics such as age, time of day, shoulder width, and weather conditions also somewhat showed influence on average speed and speed variance

Understanding Micro-Level Lane Change and Lane Keeping Driving Decisions: Harnessing Big Data Streams from Instrumented Vehicles

It is important to get a deeper understanding of instantaneous driving behaviors, especially aggressive and extreme driving behaviors such as hard acceleration, as they endanger traffic efficiency and safety by creating unstable flows and dangerous situations. The aim of the dissertation is to understand micro-level instantaneous driving decisions related to lateral movements such as lane change or lane keeping events on various roadway types. The impacts of these movements are fundamental to microscopic traffic flow and safety. Sufficient geo-referenced data collected from connected vehicles enables analysis of these driving decisions. The “Big Data” cover vehicle trajectories, reported at 10 Hz frequency, and driving situations, which make it possible to establish a framework.The dissertation conducts several key analyses by applying advanced statistical modeling and data mining techniques. First, the dissertation proposes an innovative methodology for identifying normal and extreme lane change events by analyzing the lane-based vehicle positions, e.g., sharp changes in distance of vehicle centerline relative to the lane boundaries, and vehicle motions captured by the distributions of instantaneous lateral acceleration and speed. Second, since surrounding driving behavior influences instantaneous lane keeping behaviors, the dissertation investigates correlations between different driving situations and lateral shifting volatility, which quantifies the variability in instantaneous lateral displacements. Third, the dissertation analyzes the “Gossip effect” which captures the peer influence of surrounding vehicles on the instantaneous driving decisions of subject vehicles at micro-level. Lastly, the dissertation explores correlations between lane change crash propensity or injury severity and driving volatility, which quantifies the fluctuation variability in instantaneous driving decisions.The research findings contribute to the ongoing theoretical and policy debates regarding the effects of instantaneous driving movements. The main contributions of this dissertation are: 1) Quantification of instantaneous driving decisions with regard to two aspects: vehicle motions (e.g., lateral and longitudinal acceleration, and vehicle speed) and lateral displacement; 2) Extraction of critical information embedded in large-scale trajectory data; and 3) An understanding of the correlations between lane change outcomes and instantaneous lateral driving decisions

Application of Global Positioning System and questionnaires data for the study of driver behavior on two-lane rural roads

This paper is a preprint of a paper accepted by IET Intelligent Transport Systems and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at IET Digital LibraryMethodologies based on naturalistic observation provide the most accurate data for studying drivers' behaviour. This study presents a new methodology to obtain naturalistic data related to drivers' behaviour in a road segment. It is based on the combination of using global positioning system data and drivers' questionnaires. The continuous speed profiles along a road segment and the characteristics of drivers, of their trips and the type of their vehicles can be obtained for a great amount of drivers. It has already been successfully used for several studies, such as the development of models to estimate operating speed profile in two-lane rural road segments; or the characterisation of driving styles. These operating speed models have been the key for the development of a new geometric design consistency model, allowing an easier road safety evaluation. Besides, knowledge on the human factors that influence speed choice may be useful for road safety media campaigns and education programs designers, and also for the improvement of intelligent driver assistance systems.The authors thank 'Centre for Studies and Experimentation of Public Works (CEDEX)' of the 'Spanish Ministry of Public Works' that partially subsidizes the research. We also wish to thank to the 'General Directorate of Public Works, Urban Projects and Housing' of the 'Infrastructure, Territory and Environment Department' of the 'Valencian Government', to the 'Valencian Provincial Council' and to the 'General Directorate of Traffic' of the 'Ministry of the Interior' for their cooperation in field data gathering.Pérez Zuriaga, AM.; Camacho Torregrosa, FJ.; Campoy Ungria, JM.; García García, A. (2013). Application of Global Positioning System and questionnaires data for the study of driver behavior on two-lane rural roads. IET Intelligent Transport Systems. 7(2):182-189. doi:10.1049/iet-its.2012.0151S18218972Fourie, M., Walton, D., & Thomas, J. A. (2011). Naturalistic observation of drivers’ hands, speed and headway. Transportation Research Part F: Traffic Psychology and Behaviour, 14(5), 413-421. doi:10.1016/j.trf.2011.04.009Gibreel, G. M., Easa, S. M., & El-Dimeery, I. A. (2001). Prediction of Operating Speed on Three-Dimensional Highway Alignments. Journal of Transportation Engineering, 127(1), 21-30. doi:10.1061/(asce)0733-947x(2001)127:1(21)Fitzpatrick, K., & Collins, J. M. (2000). Speed-Profile Model for Two-Lane Rural Highways. Transportation Research Record: Journal of the Transportation Research Board, 1737(1), 42-49. doi:10.3141/1737-06Bella, F. (2008). Driving simulator for speed research on two-lane rural roads. Accident Analysis & Prevention, 40(3), 1078-1087. doi:10.1016/j.aap.2007.10.015Van Nes, N., Houtenbos, M., & Van Schagen, I. (2008). Improving speed behaviour: the potential of in-car speed assistance and speed limit credibility. IET Intelligent Transport Systems, 2(4), 323. doi:10.1049/iet-its:20080036Warner, H. W., & Åberg, L. (2006). Drivers’ decision to speed: A study inspired by the theory of planned behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 9(6), 427-433. doi:10.1016/j.trf.2006.03.004Goldenbeld, C., & van Schagen, I. (2007). The credibility of speed limits on 80km/h rural roads: The effects of road and person(ality) characteristics. Accident Analysis & Prevention, 39(6), 1121-1130. doi:10.1016/j.aap.2007.02.012Zuriaga, A. M. P., García, A. G., Torregrosa, F. J. C., & D’Attoma, P. (2010). Modeling Operating Speed and Deceleration on Two-Lane Rural Roads with Global Positioning System Data. Transportation Research Record: Journal of the Transportation Research Board, 2171(1), 11-20. doi:10.3141/2171-02Ottesen, J. L., & Krammes, R. A. (2000). Speed-Profile Model for a Design-Consistency Evaluation Procedure in the United States. Transportation Research Record: Journal of the Transportation Research Board, 1701(1), 76-85. doi:10.3141/1701-10Park, P. Y., Miranda-Moreno, L. F., & Saccomanno, F. F. (2010). Estimation of speed differentials on rural highways using hierarchical linear regression models. Canadian Journal of Civil Engineering, 37(4), 624-637. doi:10.1139/l10-002Wasielewski, P. (1984). Speed as a measure of driver risk: Observed speeds versus driver and vehicle characteristics. Accident Analysis & Prevention, 16(2), 89-103. doi:10.1016/0001-4575(84)90034-4Williams, A. F., Kyrychenko, S. Y., & Retting, R. A. (2006). Characteristics of speeders. Journal of Safety Research, 37(3), 227-232. doi:10.1016/j.jsr.2006.04.001Lajunen, T., Karola, J., & Summala, H. (1997). Speed and Acceleration as Measures of Driving Style in Young Male Drivers. Perceptual and Motor Skills, 85(1), 3-16. doi:10.2466/pms.1997.85.1.3Af Wåhlberg, A. E. (2006). Speed choice versus celeration behavior as traffic accident predictor. Journal of Safety Research, 37(1), 43-51. doi:10.1016/j.jsr.2005.10.01

Application of big data in transportation safety analysis using statistical and deep learning methods

The emergence of new sensors and data sources provides large scale high-resolution big data from instantaneous vehicular movements, driver decision and states, surrounding environment, roadway characteristics, weather condition, etc. Such a big data can be served to expand our understanding regarding the current state of the transportation and help us to proactively evaluate and monitor the system performance. The key idea behind this dissertation is to identify the moments and locations where drivers are exhibiting different behavior comparing to the normal behavior. The concept of driving volatility is utilized which quantifies deviation from normal driving in terms of variations in speed, acceleration/deceleration, and vehicular jerk. This idea is utilized to explore the association of volatility in different hierarchies of transportation system, i.e.: 1) Instance level; 2) Event level; 3) Driver level; 4) Intersection level; and 5) Network level. In summary, the main contribution of this dissertation is exploring the association of variations in driving behavior in terms of driving volatility at different levels by harnessing big data generated from emerging data sources under real-world condition, which is applicable to the intelligent transportation systems and smart cities. By analyzing real-world crashes/near-crashes and predicting occurrence of extreme event, proactive warnings and feedback can be generated to warn drivers and adjacent vehicles regarding potential hazard. Furthermore, the results of this study help agencies to proactively monitor and evaluate safety performance of the network and identify locations where crashes are waiting to happen. The main objective of this dissertation is to integrate big data generated from emerging sources into safety analysis by considering different levels in the system. To this end, several data sources including Connected Vehicles data (with more than 2.2 billion seconds of observations), naturalistic driving data (with more than 2 million seconds of observations from vehicular kinematics and driver behavior), conventional data on roadway factors and crash data are integrated