Intersection Advance Driver Assist Systems (I-ADAS) may prevent 25 to 93% of intersection crashes. The effectiveness of I-ADAS will be limited by driver's pre-crash behavior and other environmental factors. This study will characterize real-world intersection traversals to evaluate the effectiveness of I-ADAS while accounting for driver behavior in crash and near-crash scenarios. This study characterized real-world intersection traversals using naturalistic driving datasets: the Second Strategic Highway Research Program (SHRP-2) and the Virginia Traffic Cameras for Advanced Safety Technologies (VT-CAST) 2020. A step-by-step approach was taken to create an algorithm that can identify three different intersection traversal trajectories: straight crossing path (SCP); left turn across path opposite direction (LTAP/OD); and left turn across path lateral direction (LTAP/LD). About 140,000 intersection traversals were characterized and used to train a unique driver behavior model. The median average speed for all encounter types was about 7.2 m/s. The driver behavior model was a Markov Model with a multinomial regression that achieved an average 90.5% accuracy across the three crash modes. The model used over 124,000 total intersection encounters including 301 crash and near-crash scenarios. I-ADAS effectiveness was evaluated with realistic driver behavior in simulations of intersection traversal scenarios based on proposed US New Car Assessment Program I-ADAS test protocols. All near-crashes were avoided. The driver with I-ADAS overall helped avoid more crashes. For SCP and LTAP the collisions avoided increased as the field of view of the sensor increased in I-ADAS only simulations. There were 18% crash scenarios that were not avoided with I-ADAS with driver. Among near-crash scenarios, where NHTSA expects no I-ADAS activation, there were fewer I-ADAS activations (58.5%) due to driver input compared to the I-ADAS only simulations (0%). / Master of Science / Intersection Advance Driver Assist Systems (I-ADAS) may prevent 25-93% of intersection crashes. I-ADAS can assist drivers in preventing or mitigating these crashes using a collision warning system or automatically applying the brakes for the driver. One way I-ADAS may assist in crash prevention is with automatic emergency braking (AEB), which will automatically apply braking without driver input if the vehicle detects that a crash is imminent. The United States New Car Assessment Program (US-NCAP) has also proposed adding I-ADAS with AEB tests into its standard test matrix. The US-NCAP has proposed three different scenarios. All the tests have two crash-imminent configurations where the vehicles are set up to collide if no deceleration occurs and a near-miss configuration where the vehicles are set up to barely miss each other. This study will use intersection traversals from naturalistic driving data in the US to build a driver behavior model. The intersection travels will be characterized by their speed, acceleration, deceleration, and estimated time to collision. The driver behavior model was able to predict the longitudinal and lateral movements for the driver. The proposed US-NCAP test protocols were then simulated with varied sensors parameters where one vehicle was equipped with I-ADAS and a driver. The vehicle with I-ADAS with a driver was more successful than a vehicle only equipped with I-ADAS at preventing a crash.

This thesis deals with the study of Schubert varieties, which are subsets of flag varieties indexed by elements of Weyl groups. We start by defining Lascoux elements in the Hecke algebra, and showing that they coincide with the Kazhdan-Lusztig basis elements in certain cases. We then construct a resolution (Zw, π) of the Schubert variety Xw for which Rπ*(C[l(w)]) is a sheaf on Xw whose expression in the Hecke algebra is closely related to the Lascoux element. We also define two new polynomials which coincide with the intersection cohomology Poincar\'e polynomial in certain cases. In the final chapter, we discuss some interesting combinatorial results concerning Bell and Catalan numbers which arose throughout the course of this work.

This study focuses on bicycle travel flow to prevent traffic accidents at non-signalized intersections. A bicycle's behavior can be characterized by various parameters, such as travel speed position, trajectory, acceleralion, and deceleration. The prevention of vehicle collisions with bicycles traveling at 10-15 km/h was regulated in the Advanced Emergency Braking System (AEBS) for passenger cars in regulation No. 152 of the World Forum for Harmonization of Vehicle Regulations in the United Nations. Therefore, it is essential to analyze the characteristics of bicycles in a reall trafflc environment to prevent traffic accidents involving cyclists. Meijer et. al. (2017) investigated bicycle behavior and charactericics using measurement devices installed on biccycles [1 ]. Ma et al. (2016) conducted a model of acceleration behavior on eleven cyclists using GPS data [2]. And it was pointed out that there was a need for modeling research for more cyclists.Hirose et al. (2021) examined bicycles' both travel speed and trajectory as bicycle travel flows based on data obtained from fixed-point observations at a non-signalized intersection in Tokyo, Japan [3]. This used fixed-point observalions to obtain raw data of bicycle travel flows in real traffic environment and reported various traffel speed, trajectory, and acceleration/deceleration patterns for bicycles entering intersections. The purpose of this study was to construct a model of bicycle travel flows based on fixed-point observations. It could simulate actual bicycle behaviors based on data that was obtained from measuring bicycle travel flows for 2828 cases from fixed-point observations. Furthermore, the data was divided into five patterns of bicycles entering intersections, and the accuracy of the model was evaluated for each pattern.

Data-driven intelligent transportation systems (ITS) are increasingly playing a critical role in improving the efficiency of the existing transportation network and addressing traffic challenges in large cities, such as safety and road congestion. This dissertation employs data dimensionality reduction, reinforcement learning, and discrete outcome models to improve traffic flow and transportation safety. First, we propose a novel data-driven technique based on Koopman operator theory and dynamic mode decomposition (DMD) to address the complex nonlinear dynamics of signalized intersections. This approach not only provides a better understanding of intersection behavior but also offers faster computation times, making it a valuable tool for system identification and controller design. It represents a significant step towards more efficient and effective traffic management solutions. Second, we propose an innovative phase-switching approach for traffic light control using deep reinforcement learning, enhancing the efficiency of signalized intersections. The novel reward function, based on speed, waiting time, deceleration, and time to collision (TTC) for each vehicle, maximizes traffic flow and safety through real-time optimization. Finally, we introduce a mixed spline indicator pooled model, an approach for multivariate crash severity prediction, addressing the limitations of previous models by capturing temporal instability. It carefully incorporates additional independent variables to measure parameter slope changes over time, enhancing data fit and predictive accuracy. The developed models are estimated and validated using data from the Central Florida region.

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One of the biggest challenges in the development ofautonomous vehicles is to anticipate the behavior of other roadusers. Autonomous vehicles rely on data obtained by on-boardsensors and make decisions accordingly, but this becomes difficultif the sensors are occluded or have limited range. In this reportwe propose an algorithm for connected vehicles in an intersectionto fuse and share sensor data and gain a better estimationof the surrounding environment. The method used for sensorfusion was a Kalman filter and a tracking algorithm, where timedelay from external sensors was considered. Parameters for theKalman filter were decided through measurement of the sensors’variances as well as tuning. It was concluded that the variancesare dependent on the objects’ movements, which means thatconstant parameters for the Kalman filter would not be enoughto make it efficient. However, the tracking and the sensor sharingmade a significant difference in the vehicle’s detection rate whichcould ultimately increase safety in intersections. / En av de största utmaningarna för utvecklingen av autonoma fordon är att förutse andra trafikanters beteenden. Autonoma fordon förlitar sig på data från sensorer ombord och fattar beslut i enlighet med informationen från dessa. Detta blir särskilt svårt om sensorerna skyms eller om sensorerna har begränsad räckvidd. I denna rapport föreslår vi en algoritm för delning och optimering av sensordata för autonoma fordon i en vägkorsning för att ge fordonet en så bra uppfattning av omgivningen som möjligt. Metoden som användes för sensorfusion var ett Kalman-filter tilsammans med en spårningsalgoritm där tidsfördröjning av data från externa sensorer togs i beaktning. Parametrarna för Kalman-filtret valdes genom mätning av sensorns varians samt genom trimning. Slutsatsen drogs att varianserna är beroende av objektens rörelsemönster, vilket innebär att konstanta parametrar för Kalman-filtret inte skulle vara tillräckligt för att göra det funktionellt. Spårningen och delningen av sensordata gjorde emellertid en betydande skillnad i andelen upptäckta objekt vilket skulle kunna nyttjas för att öka säkerheten i korsningar. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

B-spline surfaces have been widely used in aircraft design to represent different types of components in a uniform format. Unlike the visual trimming of B-spline surfaces, which hides unwanted portions in rendering, the geometric trimming approach provides a mathematically clean representation. This dissertation focuses on the geometric trimming of fuselage and wing components represented by B-spline surfaces. To trim two intersecting surfaces requires finding their intersections effectively. Most of the existing algorithms focus on providing intersections suitable for rendering. In this dissertation, an intersection algorithm suitable for geometric trimming of B-spline surfaces is presented. The number of intersection points depends on the number of isoparametric curves selected, and thus is controllable and independent of the error bound of intersection points. Trimming curves are classified and a new scheme for trimming by a closed trimming curve is provided to improve the accuracy. The surface trimmed by a closed trimming curve is subdivided into four patches and the trimming curve is converted into two open trimming curves. Two surface patches are created by knot insertion, which match the original surface exactly. The other two surface patches are trimmed by the converted open trimming curves. Factors affecting the trimming process are discussed and metrics are provided to measure trimming errors. Exact trimming is precluded due to the high degree of intersections. The process may lead to significant deviation from the corresponding portion on the original surface. Optimizations are employed to minimize approximation errors and obtain higher accuracy. The hybrid Parallel Tempering and Simulated Annealing optimization method, which is an effective algorithm to overcome the slow convergence waiting dilemma and initial value sensitivity, is applied for the minimization of B-spline surface representation errors. The results confirm that trimming errors are successfully reduced. / Ph. D.

With the introduction of different forms of adaptive and actuated signal control, there is a need for effective evaluation tools that can capture the intricacies of real-life applications. While the current state-of-the-art analytical procedures provide simple approaches for estimating delay, queue length and stops at signalized intersections, they are limited in scope. Alternatively, several microscopic simulation softwares are currently available for the evaluation of signalized intersections. The objective of this dissertation is fourfold. First, it evaluates the consistency, accuracy, limitations and scope of the alternative analytical models. Second, it evaluates the validity of micro simulation results that evolve as an outcome of the car-following relationships. The validity of these models is demonstrated for idealized hypothetical examples where analytical solutions can be derived. Third, the dissertation expands the scope of current analytical models for the evaluation of oversaturated signalized intersections. Finally, the dissertation demonstrates the implications of using analytical models for the evaluation of real-life network and traffic configurations. This dissertation compared the delay estimates from numerous models for an undersaturated and oversaturated signalized intersection considering uniform and random arrivals in an attempt to systematically evaluate and demonstrate the assumptions and limitations of different delay estimation approaches. Specifically, the dissertation compared a theoretical vertical queuing analysis model, the queue-based models used in the 1994 and 2000 versions of the Highway Capacity Manual, the queue-based model in the 1995 Canadian Capacity Guide for Signalized Intersections, a theoretical horizontal queuing model derived from shock wave analysis, and the delay estimates produced by the INTEGRATION microscopic traffic simulation software. The results of the comparisons for uniform arrivals indicated that all delay models produced identical results under such traffic conditions, except for the estimates produced by the INTEGRATION software, which tended to estimate slightly higher delays than the other approaches. For the random arrivals, the results of the comparisons indicated that the delay estimates obtained by a micro-simulation model like INTEGRATION were consistent with the delay estimates computed by the analytical approaches. In addition, this dissertation compared the number of stops and the maximum extent of queue estimates using analytical procedures and the INTEGRATION simulation model for both undersaturated and oversaturated signalized intersections to assess their consistency and to analyze their applicability. For the number of stops estimates, it is found that there is a general agreement between the INTEGRATION microscopic simulation model and the analytical models for undersaturated signalized intersections. Both uniform and random arrivals demonstrated consistency between the INTEGRATION model and the analytical procedures; however, at a v/c ratio of 1.0 the analytical models underestimate the number of stops. The research developed an upper limit and a proposed model for estimating the number of vehicle stops for oversaturated conditions. It was demonstrated that the current state-of-the-practice analytical models can provide stop estimates that far exceed the upper bound. On the other hand, the INTEGRATION model was found to be consistent with the upper bound and demonstrated that the number of stops converge to 2.3 as the v/c ratio tends to 2.0. For the maximum extent of queue estimates, the estimated maximum extent of queue predicted from horizontal shock wave analysis was higher than the predictions from vertical deterministic queuing analysis. The horizontal shock wave model predicted lower maximum extent of queue than the CCG 1995 model. For oversaturated conditions, the vertical deterministic queuing model underestimated the maximum queue length. It was found that the CCG 1995 predictions were lower than those from the horizontal shock wave model. These differences were attributed to the fact that the CCG 1995 model estimates the remaining residual queue at the end of evaluation time. A consistency was found between the INTEGRATION model and the horizontal shock wave model predictions with respect to the maximum extent of queue for both undersaturated and oversaturated signalized intersections. Finally, the dissertation analyzed the impact of mixed traffic condition on the vehicle delay, person delay, and number of vehicle stops at a signalized intersection. The analysis considered approximating the mixed flow for equivalent homogeneous flows using two potential conversion factors. The first of these conversion factors was based on relative vehicle lengths while the second was based on relative vehicle riderships. The main conclusion of the analysis was that the optimum vehicle equivalency was dependent on the background level of congestion, the transit vehicle demand, and the Measure of Effectiveness (MOE) being considered. Consequently, explicit simulation of mixed flow is required in order to capture the unique vehicle interactions that result from mixed flow. Furthermore, while homogeneous flow approximations might be effective for some demand levels, these approximations are not consistently effective. / Ph. D.

Vehicular safety and efficiency has been an ongoing research topic since the creation of the automobile. Despite this, deaths due to vehicular accidents are still extremely common, with driver issues and errors causing a vast majority of them. In order to combat the safety risks, Connected and Autonomous Vehicles (CAV) and other smart solutions have been heavily researched. CAVs provide the means to increase the safety of travel as well as its efficiency. However, before connected vehicles can be deployed and utilized, safe and secure communication and standards need to be created and evaluated to ensure that the introduction of a new safety threat does not overshadow the one that is already being faced. As such, it is integral for Intelligent Transportation Systems (ITS) to prevent, detect and respond to cyberattacks. This research focuses on the detection and response of ITS components to cyberattacks. An Intrusion Detection System (IDS) located on Roadside Units (RSU) was developed to detect misbehavior nodes. This model maintains a 98%-100% accuracy while reducing system overhead by removing the need for edge or cloud computing. A resilient Intrusion Response System (IRS) for a autonomous intersection was developed to protect again sybil attacks. The IRS utilizes adaptive switching between several intersection types to reduce delay by up to 78% compared to intersections without these defenses. / Master of Science / Vehicular safety and efficiency has been an ongoing research topic since the creation of the automobile. Despite this, deaths due to vehicular accidents are still extremely common, with driver issues and errors causing a vast majority of them. In order to combat the safety risks, Connected and Autonomous Vehicles (CAV) and other smart solutions have been heavily researched. CAVs provide the means to increase the safety of travel as well as its efficiency. However, before connected vehicles can be deployed and utilized, safe and secure communication and standards need to be created and evaluated to ensure that the introduction of a new safety threat does not overshadow the one that is already being faced. As such it is integral for Intelligent Transportation Systems (ITS) to prevent, detect and respond to cyberattacks. This research focuses on the detection and response of ITS components to cyberattacks. An Intrusion Detection System (IDS) was created to detect vehicles misbehaving or conducting cyberattacks. The IDS is installed on off-road computers, called Roadside Units (RSU) which prevents the need for a separate server to be created to hold the IDS. The IDS is able to identify misbehavior and attacks at a 98% to 100% accuracy. An autonomous intersection is an intersection where all directions for driving through the intersection are transmitted through wireless communication. A Intrusion Response System (IRS) was developed for an autonomous intersection, to defend against vehicles making multiple reservation requests to pass through the intersection. The IRS reduces vehicle delay through the intersection by 78% compared to an intersection without defenses.