Accidental collisions involving wheeled industrial ground vehicles can be costly to repair, cause serious (even fatal) human injury, and lead to setbacks with tight operation schedules. Reduction of vehicle collisions carries numerous safety and financial incentives. In this work, an integrated collision avoidance package is developed to reduce the number of vehicle collisions. Utilizing feedback from on-board sensing devices, a model predictive control (MPC) algorithm predicts control options and paths, then disallows drivers to accelerate and/or induces braking of the vehicle if a collision is imminent. A prototype system is developed, implemented, and tested on an industrial vehicle to mitigate collisions with people and high-value equipment. Testing results show that control can be executed in real time by the proposed system, and that the proposed method is effective in preventing an industrial vehicle from hitting detected obstacles and entering restricted areas.

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Active safety systems that warn automobile drivers of various types of impending collisions have been developed. How these systems alert drivers when integrated, however, is a crucial component to their effectiveness that hinges on the consideration of human factors. Drivers' ability to comprehend multiple alerts presented through a haptic driver seat was investigated in this dissertation. Twenty-four participants, balanced for age and gender, drove an instrumented vehicle on a test-track while haptic alerts (vibrations in the driver seat) were generated. Drivers' ability to transmit the information conveyed by the alerts was investigated through two experiments. The first experiment investigated the effects of increasing the number of potential alerts on drivers' response performance. The second experiment investigated whether presenting haptic alerts through unique versus common locations in the driver seat affects drivers' response performance. Younger drivers (between the ages of 18 and 25 years old) were found to efficiently process the increased information contained in the alerts, while older drivers were not as efficient. However, it is foreseeable that older driver performance decrements may be assuaged when a crash context is provided. A third experiment evaluated the haptic driver seat's ability to alert distracted drivers to an actual crash threat. Drivers that received a haptic seat alert returned their gaze to the forward roadway sooner, removed their foot from the throttle sooner, pressed the brake pedal sooner, and stopped farther away from an inflatable barricade than drivers that did not receive a haptic seat alert. No age or gender effects were found in this experiment. Furthermore, half of the drivers that received the haptic seat alert lifted up on the throttle before returning their eyes to the forward roadway. This suggests these drivers developed an automatic response to the haptic seat alerts through their experience with the previous two experiments. A three-alert haptic seat approach, the intermediate alternative tested, is recommended providing specific design requirements are met. / Ph. D.

The overall objective of this thesis is to develop, validate and assess a probabilistic collision damage model to support ongoing work by the Society of Naval Architecture and Marine Engineering (SNAME) Ad Hoc Panel #6 and IMO working groups. It is generally agreed that structural design has a major influence on tanker oil outflow and damaged stability in grounding and collision, but crashworthiness is not considered in present regulations. The proposed methodology provides a practical means of considering structural design in a regulatory framework, and when implemented would improve the safety and environmental performance of ships. This thesis continues the development and applies a Simplified Collision Model (SIMCOL) to calculate damage extent (transverse, vertical and longitudinal) and oil outflow in ship collisions. The primary contribution of this thesis is the development and validation of a theory for the determination of energy absorbed in longitudinal extent of damage, and the implementation of the theory within SIMCOL. SIMCOL is sufficiently fast to be applied to thousands of collision cases as is required for a probabilistic analysis. The following specific tasks were completed using SIMCOL in support of this project: Completed the development of SIMCOL Version 3.0 including: 1) Deformable Bow sub model 2) Implementation and validation of theory for the determination of energy absorbed in longitudinal extent of damage. • Developed the capability to model collision events using LSDYNA. • Validated Virginia Tech LSDYNA ship collision modeling procedure. • Validated SIMCOL using real collision data, and probabilistic collision data for penetrating collisions. / Ph. D.

Relative to other roadway segments, intersections occupy a small portion of the overall infrastructure; however, they represent the location for nearly 41 % of the annual automotive crashes in the United States. Thus, intersections are an inherently dangerous roadway element and a prime location for vehicle conflicts. Traditional safety treatments are effective at addressing certain types of intersection safety deficiencies; however, cumulative traffic data suggests these treatments do not address a large portion of the crashes that occur each year. Intersection Collision Avoidance Systems (ICAS) represent a new breed of countermeasures that focus on the types of crashes that have not been reduced with the application of traditional methods. Incursion systems, a subset of ICAS, are designed to specifically undertake crashes that are a result of the violation of a traffic control device. Intersection Collision Avoidance Systems to address Violations (ICAS-V) monitor traffic as it approaches the intersection through a network of in-vehicle sensors, infrastructure- mounted sensors, and communication equipment. A threat-assessment algorithm performs computations to predict the driver's intended intersection maneuver, based on these sensor inputs. If the system predicts a violation, it delivers a timely warning to the driver with the aim of compelling the driver to stop. This warning helps the driver to avoid a potential crash with adjacent traffic. The following dissertation describes an investigation of intersection approach behavior aimed at developing a threat assessment algorithm for stop-sign intersections. Data were collected at live intersections to gather infrastructure-based naturalistic vehicle approach trajectories. This data were compiled and analyzed with the goal of understanding how drivers approach intersections under various speeds and environmental conditions. Six stop-controlled intersection approaches across five intersections in the New River Valley, Virginia area were selected as the test sites. Data were collected from each site for at least two months, resulting in over sixteen total months of data. A series of statistical analysis techniques were applied to construct a set of threat assessment algorithms for stop-controlled intersections. These analyses identified characteristics of intersection approaches that suggested driver intent at the stop sign. Models were constructed to predict driver stopping intent based on measured vehicle kinematics. These models were thoroughly tested using simulation and evaluated with signal detection theory. The overall output of this work is a set of algorithms that may be integrated into an ICAS-V for on-road testing. / Ph. D.

Rear-end collisions in which the leading vehicle was stationary prior to impact and at least one vehicle was towed from the crash site represent 18% of all yearly crashes in the United States. Forward Collision Avoidance Systems (FCASs) are becoming increasingly available in production vehicles and have a great potential for preventing or mitigating rear-end collisions. The objective of this study was to compare the effectiveness of five crash avoidance algorithms that are similar in design to systems found on production vehicles of model year 2011. To predict the effectiveness of each algorithm, this study simulated a representative sample of rear-end collisions as if the striking vehicle was equipped with each FCAS. In 2011, the ADAC (Allgemeiner Deutscher Automobil-Club e.V) published a test report comparing advanced emergency braking systems. The ADAC tested production vehicles of model year 2011 made by Audi, BMW, Infiniti, Volvo, and VW. The ADAC test results were used in conjunction with video evidence and owner's manual information to develop mathematical models of five different FCASs. The systems had combinations of Forward Collision Warning (FCW), Assisted Braking (AB), and Autonomous Emergency Braking (AEB). The effectiveness of each modeled system was measured by its ability to prevent collisions or reduce the collision severity of reconstructed crashes. In this study, 977 rear-end crashes that occurred from 1993 to 2008 were mathematically reconstructed. These crashes were investigated as part of NHTSA's National Automotive Sampling System, Crashworthiness Data System (NASS/CDS). These crashes represent almost 800,000 crashes during that time period in which the struck vehicle was stationary. Part of the NASS/CDS investigation was to reconstruct the vehicle change in velocity during impact, ∆V. Using energy and Newtonian based methods, the ∆V in each crash was calculated as if the vehicle was equipped with each modeled FCAS. Using the predicted reduction in crash ∆V, the expected reduction in the number of moderately-to-fatally injured (MAIS2+) drivers was predicted. This study estimates that the most effective FCAS model was the Volvo algorithm which could potentially prevent between 79% and 92% of the crashes simulated in this study and between 76% and 94% of associated driver injuries. This study estimates that the BMW algorithm would prevent the fewest number of crashes (between 11% and 14%), but would provide admirable benefits to driver safety by preventing between 21% and 25% of driver injuries. The VW algorithm would be the least effective at preventing driver injuries if the system were to be implemented across the U.S. fleet. This algorithm offers a 19% reduction in crashes, but only prevents 15% of driver injuries. This study introduces and demonstrates a unique method of comparing potential benefits of competing FCAS algorithms. This method could be particularly useful to system designers for comparing the expected effects of design decisions on safety performance. This method could also be useful to government officials who wish to evaluate the effectiveness of FCASs. / Master of Science

No-passing zone crashes constitute a sizable percentage of the total crashes on two-lane rural roads. A detection and warning system has been devised and implemented at a no-passing zone site on route 114 of Southwest Virginia to address this problem. The warning system aims at deterring drivers from illegally conducting a passing maneuver within the no-passing zone area. The violating driver is warned in real time to stop the illegal act. This system is currently operational and its main function is to warn the no-passing zone violator. The aim of this research is to extend the warning system to the opposing vehicle in the same lane of the persistent violator in order to avoid crashes caused by the illegal maneuver that is taking place at a crest vertical curve of the two-lane rural road. In order to test the new system prior to its physical installation, a computer simulation has been developed to represent the real world violation conditions so that a better understanding of the problem and its varying scenarios would be achieved. The new simulation, which is the focus of this thesis, takes advantage of an existing simulation developed earlier to replicate only the illegal maneuver without giving any warnings to the opposing vehicle. The new program simulates the outcome of deploying a warning sign to the opposing driver for crash avoidance purposes assuming that all violators persist to pass the vehicle ahead. More than 712,000 computer runs were conducted to simulate the various possible outcomes including the sensitivity analysis. A critical comparison was made between the previous system that warned only the violating vehicle and the current program that warns both the violator as well as the opposing vehicle. The results indicate that warning the opposing driver would reduce the rate of unavoidable crashes by approximately 11% in the east direction and 13.25% in the west direction. / Master of Science