Evaluation of Kentucky\u27s Graduated Driver Licensing System

Kentucky\u27s current GDL program, which applies to drivers up to age 18, was enacted in 1996. The program includes a six-month instruction permit for drivers under age 18 (minimum age 16), a restriction on driving between midnight and 6am and a requirement for adult-supervised driving for permit drivers. In addition, there is a six-point limit on traffic violations with a penalty of license suspension for drivers under age 18, a requirement for a four-hour driving safety education class (or driver education course) and a 0.02 ml/dl limit on blood alcohol concentration (continues up to age 21 ). Objectives: The objectives of this program evaluation were: (a) to examine teen driver motor vehicle crashes, crash-related injuries, and crash-related costs before and after the implementation of the GDL program; (b) to examine the implementation of the program at the local level ; and (c) to recommend actions to enhance the program\u27s effectiveness in addressing the teen crash problem. Methods: Crash and licensing data before ( 1993-1995) and after GDL ( 1997-2000) were analyzed. Data on miles driven were obtained from driving logs of over I ,000 high school students. Estimation of the cost of crashes was derived from analysis of crash data using the Crash Cost computer software program. Information on local implementation of GDL was obtained through interviews and through a questionnaire survey of 700 law enforcement officers and over 40 district judges. Results: Results indicate a 31 percent reduction in crashes for 16 year-old drivers after the GDL program, and a similar reduction in fatal crashes (31 percent) and injury crashes (33 percent), crashes between midnight and 6am (36 percent), and alcohol-related crashes (32 percent). Cost analysis indicates an estimated reduction of $36 million per year in 16 year-old teen driver crash-related expenses. Results indicate that this is due to the 83% reduction in the number of 16 to 16 1/2 year-old drivers involved in crashes. However, the number of crashes has not been reduced for drivers over age 16 1/2, i.e. drivers who may be past the permit level. In addition, the six-point limit on traffic violations and the non-cumulative penalties on 0.02 blood alcohol limit violations have not reduced the number of traffic violations or alcohol-related crashes for teen drivers over age 16 1/2. Recommendations: The six-month permit level has been successful in substantially reducing crash-related injuries and fatalities and should be retained. Additional measures, such as upgrading to an expanded GDL program, are needed to reduce crash-related injuries and fatalities for 16 1/2 to 18 year-old drivers. Specific recommendation are made to increase parental awareness and enforcement of program provisions

Youth Needs and Assessment Survey

The characteristics of traffic accidents involving teenage (16 to 19 years of age) drivers are summarized in this report and compared to all accidents. This was accomplished through an analysis of statewide accident data for a three-year period (1989 through 1991). Counties having the highest overrepresentation of accidents involving teenage drivers were identified. The status of highway safety programs currently in place in school districts in Kentucky is presented. A survey was sent to 182 school districts in Kentucky with a 64 percent response rate obtained. Relevant literature was reviewed, and summary statements are given in the Appendix. This summary of the literature gives an overview of recognized problems associated with young drivers and programs which have been recommended to address these problems. The information given in this report may be used in the process of developing a statewide youth traffic safety program. Recommendations for implementation of such a program are given

Evaluation of Highway Geometrics Related to Large Trucks

One objective of this study was to determine the extent of highway safety and geometric problems associated with larger trucks using Kentucky\u27s highways. The accident analysis involved both a general analysis of all truck accidents statewide as well as the identification of specific high-accident locations. A second objective was to identify criteria which can be used in identifying roadway sections that cannot safely accommodate large trucks. The accident analysis given can be used to investigate locations which have a high number of truck accidents. The general accident statistics related to trucks can be used in the investigation of the high-accident locations to identify factors which may be contributing to the accident problem. The summary of information obtained from the review of literature can be used as a guide when determining the appropriate criteria to use in formalizing truck access criteria. For example, several references gave recommendations concerning lane width and horizontal curvature appropriate for highways that allowed large truck traffic

An Investigation of Patterns of Adolescent Driving Behaviors Resulting in Fatal Crashes and Their Implications on Policy

The purpose of this quantitative study was to investigate whether there is a statistical relationship between accident-related factors including use of drugs or alcohol, speeding, driver distractions, gender, driver drowsiness, practice of dysfunctional driving maneuvers, and use of occupant protection devices, and fatal vehicle crashes among young teen drivers. Secondary archival data from 84 North Carolina crashes occurring between 2009 and 2013 and involving young teen drivers between the ages of 15 and 18 years were obtained from North Carolina Department of Motor Vehicles Form 349 crash reports. These data were analyzed using chi-square tests for goodness-of-fit, chi-square tests for independence, and z-tests for proportions. The study found statistically significant associations between gender (p \u3c.019), speeding (p \u3c .001), practice of dysfunctional driving maneuvers (p \u3c .001), and non-use of occupant protection devices (p \u3c .001) and teen crash fatalities. The implications of this study for positive social change include recommendations to the State of North Carolina to enact legislative action related to driver education for new drivers, with the anticipated result of reducing traffic fatalities when a teenage driver is involved in an accident. In order to counteract deadly dysfunctional driving maneuvers on the part of young teen drivers, it was recommended that State driver education curricula be expanded to include exposure to more real world, on-the-road supervised driving experience conducted under more varied conditions and that high school driver education facilities be upgraded to include skid pads for student driving practice. Further research relating to the supervised implementation and verification of the requirement of the 50 hours of adult-supervised driving experience for Graduated Driver Licensure was also recommended

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

Decision-Making in Autonomous Driving using Reinforcement Learning

The main topic of this thesis is tactical decision-making for autonomous driving. An autonomous vehicle must be able to handle a diverse set of environments and traffic situations, which makes it hard to manually specify a suitable behavior for every possible scenario. Therefore, learning-based strategies are considered in this thesis, which introduces different approaches based on reinforcement learning (RL). A general decision-making agent, derived from the Deep Q-Network (DQN) algorithm, is proposed. With few modifications, this method can be applied to different driving environments, which is demonstrated for various simulated highway and intersection scenarios. A more sample efficient agent can be obtained by incorporating more domain knowledge, which is explored by combining planning and learning in the form of Monte Carlo tree search and RL. In different highway scenarios, the combined method outperforms using either a planning or a learning-based strategy separately, while requiring an order of magnitude fewer training samples than the DQN method. A drawback of many learning-based approaches is that they create black-box solutions, which do not indicate the confidence of the agent\u27s decisions. Therefore, the Ensemble Quantile Networks (EQN) method is introduced, which combines distributional RL with an ensemble approach, to provide an estimate of both the aleatoric and the epistemic uncertainty of each decision. The results show that the EQN method can balance risk and time efficiency in different occluded intersection scenarios, while also identifying situations that the agent has not been trained for. Thereby, the agent can avoid making unfounded, potentially dangerous, decisions outside of the training distribution. Finally, this thesis introduces a neural network architecture that is invariant to permutations of the order in which surrounding vehicles are listed. This architecture improves the sample efficiency of the agent by the factorial of the number of surrounding vehicles

How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio

\u27How\u27s My Driving?\u27 for Everyone (and Everything?)

This is a paper about using reputation tracking technologies to displace criminal law enforcement and improve the tort system. The paper contains an extended application of this idea to the regulation of motorist behavior in the United States and examines the broader case for using technologies that aggregate dispersed information in various settings where reputational concerns do not adequately deter antisocial behavior. The paper begins by exploring the existing data on “How’s My Driving?” programs for commercial fleets. Although more rigorous study is warranted, the initial data is quite promising, suggesting that the use of “How’s My Driving?” placards in commercial trucks is associated with fleet accident reductions ranging from 20% to 53%. The paper then proposes that all vehicles on American roadways be fitted with “How’s My Driving?” placards so as to collect some of the millions of daily stranger-on-stranger driving observations that presently go to waste. By delegating traffic regulation to the motorists themselves, the state might free up substantial law enforcement resources, police more effectively dangerous and annoying forms of driver misconduct that are rarely punished, reduce information asymmetries in the insurance market, improve the tort system, and alleviate road rage and driver frustration by providing drivers with opportunities to engage in measured expressions of displeasure. The paper addresses obvious objections to the displacement of criminal traffic enforcement with a system of “How’s My Driving?”-based civil fines. Namely, it suggests that by using the sorts of feedback algorithms that eBay and other reputation tracking systems have employed, the problems associated with false and malicious feedback can be ameliorated. Indeed, the false feedback problem presently appears more soluble in the driving context than it is on eBay. Driver distraction is another potential pitfall, but available technologies can address this problem, and the implementation of a “How’s My Driving?” for Everyone system likely would reduce the substantial driver distraction that already results from driver frustration and rubbernecking. The paper also addresses the privacy and due process implications of the proposed regime. It concludes by examining various non-driving applications of feedback technologies to help regulate the conduct of soldiers, police officers, hotel guests, and participants in virtual worlds, among others

Haptic Steering Interfaces for Semi-Autonomous Vehicles

Autonomous vehicles are predicted to significantly improve transportation quality by reducing traffic congestion, fuel expenditure and road accidents. However, until autonomous vehicles are reliable in all scenarios, human drivers will be asked to supervise automation behavior and intervene in automated driving when deemed necessary. Retaining the human driver in a strictly supervisory role, however, may make the driver complacent and reduce driver's situation awareness and driving skills which ironically, can further compromise the driver’s ability to intervene in safety-critical scenarios. Such issues can be alleviated by designing a human-automation interface that keeps the driver in-the-loop through constant interaction with automation and continuous feedback of automation's actions. This dissertation evaluates the utility of haptic feedback at the steering interface for enhancing driver awareness and enabling continuous human-automation interaction and performance improvement in semi-autonomous vehicles. In the first part of this dissertation, I investigate a driving scheme called Haptic Shared Control (HSC) in which the human driver and automation system share the steering control by simultaneously acting at the steering interface with finite mechanical impedances. I hypothesize that HSC can mitigate the human factors issues associated with semi-autonomous driving by allowing the human driver to continuously interact with automation and receive feedback about automation action. To test this hypothesis, I present two driving simulator experiments that are focused on the evaluation of HSC with respect to existing driving schemes during induced human and automation faults. In the first experiment, I compare obstacle avoidance performance of HSC with two existing control sharing schemes that support instantaneous transfers of control authority between human and automation. The results indicate that HSC outperforms both schemes in terms of obstacle avoidance, maneuvering efficiency, and driver engagement. In the second experiment, I consider emergency scenarios where I compare two HSC designs that provide high and low control authority to automation and an existing paradigm that decouples the driver input from the tires during collision avoidance. Results show that decoupling the driver invokes out-of-the-loop issues and misleads drivers to believe that they are in control. I also discover a `fault protection tradeoff': as the control authority provided to one agent increases, the protection against that agent's faults provided by the other agent reduces. In the second part of this dissertation, I focus on the problem of estimating haptic feedback from the road, or the road feedback. Road feedback is critical to making the driver aware of the state of the vehicle and road conditions, and its estimates are used in a variety of driver assist systems. However, conventional estimators only estimate road feedback on flat roads. To overcome this issue, I develop three estimators that enable road feedback estimation on uneven roads. I test and compare the performance of the three estimators by performing driving experiments on uneven roads such as road slopes and cleats. In the final part of this dissertation, I shift focus from physical human-automation interaction to human-human interaction. I present the evidence from the literature demonstrating that haptic feedback improves the performance of two humans physically collaborating on a shared task. I develop a control-theoretic model for haptic communication that can describe the mechanism by which haptic interaction facilitates performance improvement. The model creates a promising means to transfer the obtained insights to design robots or automation systems that can collaborate more efficiently with humans.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169975/1/akshaybh_1.pd

THE SYNTHESIS OF NAVISECTION: MODERNIZING DRIVER REHABILITATION PROGRAMS TO ENCOMPASS INTELLIGENT VEHICLE TECHNOLOGIES

The synthesis of NAViSection introduced a concept for using vehicle-based sensor data to improve the practice of driver evaluation. This project to reinforce licensing recommendations acknowledges that pen and paper documentation confines the expertise of evaluators to driving programs, while advances in vehicle sensors could address driving privilege as people age, experience medical impairments, and acquire disabilities. Through a review of medical record data, client files showed internal and external limitations to current practice. Within the program, a majority of evaluations resulted in a recommendation to continue driving despite the medical conditions referenced in the physician’s referral. This finding connected to concerns of client intake waiting lists before evaluation. Additionally, driver rehabilitation programs lack insight to council clients with poor medical prognosis on when to review driving capability. The NAViSection methodology proposed a way to integrate data collection with the standard processes of a driver rehabilitation program. While collecting event data based on evaluator intervention, the broader vision sought to correlate interventions with vehicle data patterns for typical driving errors. Through multiple tests and simulations, a design project yielded a novel data collection system based on the NAViSection methodology. The pilot study results showed that assisted-driving events (steering, braking, and verbal cue assistance) correlate best with the recommendations of a Certified Driver Rehabilitation Specialist (CDRS). The NAViSection correlation presented improved predictive values compared to clinical assessment scores and driver history as screening tools. Future work could extend the reach of the CDRS by establishing correlations to telematics products (ex. OBD2 readers) and other sensing technologies as a screening system in future vehicles. In relation to driving simulators and naturalistic driving studies, the NAViSection system is better suited to help with at-risk drivers (teen and older Americans) within the setting of driving programs. Lastly, the assisted-driving events by a CDRS present a unique source of collision-avoidance, which may provide an opportunity to validate collision avoidance technologies from automotive manufacturers through real drivers, on real roads, and in real scenarios