Thesis (Ph. D.)--Psychology, Georgia Institute of Technology, 2006. / Ute Fischer, Committee Member ; Jerry R. Duncan, Committee Member ; Gregory Corso, Committee Member ; Wendy A. Rogers, Committee Member ; Arthur D. Fisk, Committee Chair.

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

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.

Forward Collision Avoidance Systems (FCAS) are a type of active safety system which have great potential for rear-end collision avoidance. These systems use either radar, lidar, or cameras to track objects in front of the vehicle. In the event of an imminent collision, the system will warn the driver, and, in some cases, can autonomously brake to avoid a crash. However, driver acceptance of the systems is paramount to the effectiveness of a FCAS system. Ideally, FCAS should only deliver an alert or intervene at the last possible moment to avoid nuisance alarms, and potentially have drivers disable the system. A better understanding of normal driving behavior can help designers predict when drivers would normally take avoidance action in different situations, and customize the timing of FCAS interventions accordingly. The overall research object of this dissertation was to characterize normal driver behavior in car following events based on naturalistic driving data. The dissertation analyzed normal driver behavior in car-following during both braking and lane change maneuvers. This study was based on the analysis of data collected in the Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study which involved over 100 drivers operating instrumented vehicles in over 43,000 trips and 1.1 million miles of driving. Time to Collision in both braking and lane change were quantified as a function of vehicle speed and driver characteristics. In general, drivers were found to brake and change lanes more cautiously with increasing vehicle speed. Driver age and gender were found to have significant influence on both time to collision and maximum deceleration during braking. Drivers age 31-50 had a mean braking deceleration approximately 0.03 g greater than that of novice drivers (age 18-20), and female drivers had a marginal increase in mean braking deceleration as compared to male drivers. Lane change maneuvers were less frequent than braking maneuvers. Driver-specific models of TTC at braking and lane change were found to be well characterized by the Generalized Extreme Value distribution. Lastly, driver's intent to change lanes can be predicted using a bivariate normal distribution, characterizing the vehicle's distance to lane boundary and the lateral velocity of the vehicle. This dissertation presents the first large scale study of its kind, based on naturalistic driving data to report driver behavior during various car-following events. The overall goal of this dissertation is to provide a better understanding of driver behavior in normal driving conditions, which can benefit automakers who seek to improve FCAS effectiveness, as well as regulatory agencies seeking to improve FCAS vehicle tests. / Ph. D.

Previous research efforts aimed at understanding the relationship between automation reliability and reliance on the automation have mainly focused on a single dimension of reliability, the automations error rate. Efforts to understand the effects of additional dimensions, such as types of errors, have merely provided suggestions about the effects that automation false alarms and misses can have on human behavior). Furthermore, other dimensions of reliability, such as the distribution of errors in time, have been almost completely ignored. A multi-task simulation of an agricultural vehicle was used in this investigation. The simulator was composed of two main tasks, a collision avoidance task and a tracking task. The collision avoidance task was supported by an imperfect automated collision avoidance system and the tracking task was performed manually. The results of this investigation indicated that there are distinct patterns of reliance that develop as a function of error type, which are dependent on the state of the automation (alarms or non-alarms). The different distributions of errors across time had an effect on the estimates of reliability and subjective trust ratings. The recency of errors was negatively related to perceived reliability and trust. The results of the current investigation also suggest that older adults are able to adjust their behavior according to the characteristics of the automation, although it takes them longer to do so. Furthermore, it appears that older adults are willing to use automated systems, as long as they are reliable enough to reduce workload.

The thesis aims to design and implement an Android application with ability to control the autopilot of the Skydog aircraft model using the wireless telemetry. The application shall receive data from an aircraft model gathered from various installed sensors. These data shall be then processed and corresponding instructions for autopilot shall be sent back. When collision with terrain or obstacle is detected, the application shall send instructions to autopilot to avoid such collision. RRT algorithm is used to find collision-free flight trajectory. Database of known obstacles and digital terrain model are provided to application in formats XML and GeoTIFF respectively.

The Traffic alert and Collision Avoidance System (TCAS) is an aircraft collision avoidance system designed to prevent mid-air collisions. During an advisory, danger is imminent, and TCAS is assumed to have better, more up-to-date information than the ground operated air traffic control (ATC) facility. Following a TCAS RA is generally the safe course of action during an advisory. However, pilot compliance with RAs is surprisingly low. Results from a TCAS monitoring study show pilots are not complying with many TCAS advisories. As revealed by pilot-submitted Aviation Safety Reporting System (ASRS) reports, this noncompliance could be attributed, in part, to pilot confusion to TCAS operation as well as misunderstandings of the appropriate response to a TCAS issued advisory. This thesis details the development and evaluation of a TCAS training program intended to improve pilots' understanding of TCAS use for collision avoidance in a range of traffic situations. The training program integrated Demonstration Based and Event Based Training techniques. Its efficacy was analyzed in an integrated ATC-cockpit simulator study in which eighteen commercial airline pilots were asked to complete the TCAS training program and afterwards experienced twelve experimental traffic events. The trained pilots' performance was compared to the performance of 16 baseline pilots who did not receive the modified training. Overall, the training program did have a significant impact on the pilots' behavior and response to TCAS advisories. The measure Time Pilots First Achieved Compliance decreased with the trained pilots, as did the measure Autopilot Disconnect Time After RA Initiation. Trained pilots exhibited less aggressive performance in response to a TCAS RA (including a decrease in the measures Altitude Deviation Over Duration Of RA, Average Vertical Rate Difference, Maximum Vertical Rate Difference, and Maximum Vertical Rate). The measure Percent Compliance did not significantly vary between trained and baseline pilots, although trained pilots had a more consistent response in the traffic event with conflicting ATC guidance. Finally, on the post-experiment questionnaires, pilots commented on their increase in understanding of TCAS as well as an increase in their trust in the advisory system. Results of this research inform TCAS training objectives provided by the FAA as well as the design of TCAS training. Additionally, conclusions extend more broadly to improved training techniques for other similar complex, time-critical situations.