xii, 57 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / The goals of this study are to assess a) the role of mass and velocity on the size of entrance wounds, b) the presence or absence and types of exit wounds, and c) the role of gender in choosing to commit suicide with a firearm. The results of an ANOVA revealed that the combination of a bullet’s mass and the relative velocity of the weapon is the most significant factor in entrance wound size. A logistic regression found that mass plays the most significant role in the presence of an exit wound. When considered separately, velocity had a more significant effect on exit type than did mass. The study also found that being male increases the odds that a firearm will be chosen to commit suicide. Handguns and the head were the most common choices for weapon and wound location, respectively, in both firearm suicides and homicides. / Committee in charge: Dr. John Lukacs, Chairperson; Dr. Andrew Karduna, Member; Dr. David Levin, Member

As important components of intelligent transportation system, lane detection and tracking (LDT) and lane departure warning (LDW) systems have attracted great interest from the computer vision community over the past few years. Conversely, lane markings recognition (LMR) systems received surprisingly little attention. This thesis proposed a real-time lane assisting framework for intelligent vehicles, which consists of a comprehensive module and simplified module. To the best of our knowledge, this is the first parallel architecture that considers not only lane detection and tracking, but also lane marking recognition and departure warning. A lightweight version of the Hough transform, PPHT is used for both modules to detect lines. After detection stage, for the comprehensive module, a novel refinement scheme consisting of angle threshold and segment linking (ATSL) and trapezoidal refinement method (TRM) takes shape and texture information into account, which significantly improves the LDT performance. Also based on TRM, colour and edge informations are used to recognize lane marking colors (white and yellow) and shapes (solid and dashed). In the simplified module, refined MSER blobs dramatically simplifies the preprocessing and refinement stage, and enables the simplified module performs well on lane detection and tracking. Several experiments are conducted in highway and urban roads in Ottawa. The detection rate of the LDT system in comprehensive module average 95.9% and exceed 89.3% in poor conditions, while the recognition rate depends on the quality of lane paint and achieves an average accuracy of 93.1%. The simplified module has an average detection rate of 92.7% and exceeds 84.9% in poor conditions. Except the conventional experimental methods, a novel point cluster evaluation and pdf analysis method have been proposed to evaluate the performance of LDT systems, in terms of the stability, accuracy and similarity to Gaussian distribution.

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Vehicular communication is the enabling technology for the development of the intelligent transportation systems (ITS), which aims to improve the efficiency and safety of transportation. It can be used for a variety of useful applications such as adaptive traffic control, coordinated braking, emergency messaging, peer-to-peer networking for infotainment services and automatic toll collection etc... Accurate yet simple models for vehicular networks are required in order to understand and optimize their operation. For reliable communication between vehicles, the spectrum access is coordinated via carrier sense multiple access (CSMA) protocol. Existing models either use a simplified network abstraction and access control scheme for analysis or depend on simulation studies. Therefore it is important to develop an analytical model for CSMA coordinated communication between vehicles. In the first part of the thesis, stochastic geometry is exploited to develop a modeling framework for CSMA coordinated inter-vehicle communication (IVC) in a multi-lane highway scenario. The performance of IVC is studied in multi-lane highways taking into account the inter-lane separations and the number of traffic lanes and it is shown that for wide multi-lane highways, the line abstraction model that is widely used in literature loses accuracy and hence the analysis is not reliable. Since the analysis of CSMA in the vehicular setting makes the analysis intractable, an aggressive interference approximation and a conservative interference approximation is proposed for the probability of transmission success. These approximations are tight in the low traffic and high traffic densities respectively. In the subsequent part of the thesis, the developed model is extended to multi-hop IVC because several vehicular applications require going beyond the local communication and efficiently disseminate information across the roads via multi-hops. Two well-known greedy packet forwarding schemes are studied, that impose different tradeoffs between per-hop transmission success probability and forward packet progress, namely, the most forward with fixed radius (MFR) and the nearest with forward progress (NFP). In particular, a tractable and accurate modeling framework is developed to characterize the per-hop transmission success probability and the average forward progress for vehicular networks in a multi-lane highway setup. The developed model reveals the interplay between the spectrum sensing threshold of the CSMA protocol and the packet forwarding scheme. A new performance metric is defined, denoted as the aggregate packet progress (APP), which is a dimensionless quantity that captures the tradeoffs between the spatial frequency reuses efficiency, the per-hop transmission success probability, and the per-hop forward progress of the packets. To this end, in contrary to existing studies, the results show that with the proper manipulation of CSMA threshold, the MFR achieves the highest APP.

Nowadays safety, along with efficiency, is one of the two strongest shaping forces of the automotive world, with advanced active safety applications being the major concentration of effort. Their development depends heavily on the quality of sensor data, a detailed measure of which is often up to the automotive manufacturers to derive, since the original equipment manufacturers (OEMs) may not disclose it on trade secrecy grounds. A model would not only provide a measure of the real-world performance of the sensor, but would also enable a higher degree of simulation accuracy which is vital to active safety function development. This is largely due to the high cost and risk involved in testing, a significant part of which is possible to be done in simulation alone. This thesis is an effort to derive a sensor model on behalf of Volvo Trucks of the performance of one of the most crucial sensors in current active safety - a lane detection camera.The work is focused on investigating approaches for modelling and simulation implementation of the lane estimation process within the black-box camera using reverse-engineering of the sensor's principles of operation. The main areas of analysis to define the factors that affect performance are the optics, image sensor, software and computer vision algorithms, and system interface. Each of them is considered separately and then methods for modelling are proposed, motivated, and derived accordingly. Finally, the finished model is evaluated to provide a measure of work success and a basis for further development. / Säkerhet är idag, tillsammans med effektivitet, en av de två starkaste förändringskrafterna i bilvärlden. Störst fokus ligger på avancerade aktiva säkerhetsfunktioner. Deras utveckling beror till stor del på kvaliteten på sensordata. En detaljerad modell för sensordata är ofta upp till fordonstillverkarna att härleda, eftersom tillverkare av sensorn ofta inte vill lämna ut sådan information. En modell ger inte bara ett mått på den verkliga prestandan hos sensorn, men ger också möjlighet till en högre grad av simuleringsnoggrannhet vilket är avgörande för utveckling av aktiva säkerhetsfunktioner. Tester är kostsamma och medför risker och en noggrann modell gör att tester kan utföras i simulering vilket minskar kostnader och risker. I denna avhandling härleds en sensormodell på uppdrag av Volvo Lastvagnar. Sensorn i fråga är en av de viktigaste sensorerna i det nuvarande aktiva säkerhetssystemet, kameran för att följa en körfil på vägen. Arbetet är fokuserat på undersökning av metoder för modellering och simulering av processen för filföljning baserat på sensorns funktionsprinciper. De viktigaste områdena för analys för att definiera de faktorer som påverkar prestanda är optik, bildsensorn, programvara, datorseendealgoritmer och systemets gränssnitt. Var och en av dessa behandlas separat och sedan föreslås och motiveras metoder för modellering. Slutligen utvärderas den färdiga modellen för att ge ett mått på hur framgångsrikt arbetet varit samt för att lägga en grund för ytterligare utveckling. / <p>The thesis work was carried out at Volvo Group Trucks Technology in Göteborg, Sweden. Supervisor for GTT: Mansour Keshavarz.</p>

In intelligent transportation systems, most of the research work has focused on lane change assistant systems. No existing work considers minimizing the interruption of traffic flow by maximizing the number of lane changes while eliminating the collisions. In this thesis, we develop qualitative and quantitative approaches for minimizing the interruption of traffic flow for three lane scenarios and show that we can extend our approach to any random number of lanes. The algorithm we propose in this thesis is able to achieve the maximum number of lane changes provided that only one vehicle per group (novel concept which is described in this thesis) is allowed to change lanes at a time. Simulation results show that our approach provides much better performance when compared with different lane change algorithms without incurring large overhead, and is hence suitable for online use.

A biography of Franklin Knight Lane.

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No description available.

Road departure crashes are among the deadliest crash modes in the U.S. each year. In response, automakers have been developing lane departure active safety systems to alert drivers to impending departures. These lane departure warning (LDW) and lane departure prevention (LDP) systems have great potential to reduce the frequency and mitigate the severity of serious lane and road departure crashes. The objective of this thesis was to characterize lane and road departures to better understand the effect of systems such as LDW and LDP on single vehicle road departure crashes. The research includes the following: 1) a characterization of lane departures through analysis of normal lane keeping behavior, 2) a characterization of road departure crashes through the development and validation of a real-world crash database of road departures (NCHRP 17-43 Lite), and 3) develop enhancements to the Virginia Tech LDW U.S. fleetwide benefits model. Normal lane keeping behavior was found to vary with road characteristics such as lane width and road curvature. Consideration of the dynamic driving behaviors observed in the naturalistic driving study (NDS) data is important to avoid LDW false alarms and driver annoyance. Departure characteristics computed in normal driving were much less severe than the departure parameters measured in real-world road departure crashes. The real-world crash data collected in NCHRP 17-43 Lite database was essential in developing enhancements to the existing Virginia Tech LDW fleetwide benefits model. Replacement of regression model predictions with measured crash data and improvement of the injury criteria resulted in an 11-16% effectiveness for road departure crashes, and an 11-15% reduction in seriously injured drivers. / Master of Science