South Texas Wildlife Activity Across a Fragmented Landscape and Road Mitigation Corridor

Wildlife crossing structures (WCS) and roadside fencing are commonly installed to mitigate habitat fragmentation, wildlife road mortalities, and other negative effects that roads can have on the surrounding landscape. Eight such WCS were constructed below Farm-to-Market (FM)106 in Cameron County, Texas, across a 16 km corridor transecting the Laguna Atascosa National Wildlife Refuge. These WCS, paired with adjacent roadside fencing, were intended to prevent road mortalities of the endangered ocelot (Leopardus pardalis) and to mitigate the barrier effect of FM106 on this and other meso-mammal species. This study will analyze camera trap data from roadside and habitat reference sites to model target species activity throughout the study corridor and identify changes in broader community composition associated with the road and its mitigation structures. This analysis will allow for more accurate estimates of mitigation structure performance while controlling for the influence of land cover characteristics on target species detections

Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectation-maximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles.;Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map\u27s accuracy of the surroundings in a cooperative automated vehicle system

Elk Abundance Estimation and Road Ecology in Whatcom and Skagit Counties, Washington

Chapter 1 – Elk abundance estimation using genetic mark-recapture in the South Fork Nooksack Valley, Whatcom County Washington. Non-invasive genetic mark-recapture is an increasingly useful method for estimating the abundance of elusive wildlife. This method was used to estimate the size of an elk population (Cervus canadensis) in the South Fork Nooksack River valley in northwestern Washington where dense forest cover can hamper aerial surveys. We genotyped 250 elk fecal DNA samples that were collected in a single sampling session. Only 103 samples amplified sufficiently after one PCR for genotype matching, which resulted in 49 unique genotypes. Program Capwire estimated a population size of 91 elk (95% CI = 83 - 130), possibly an underestimate of actual abundance. Unfortunately, funding limitations precluded necessary lab work to determine consensus genotypes so genotyping errors could not be corrected. For this reason, these results must be considered with caution. While genetic mark-recapture has many advantages over traditional mark-recapture methods, the potential for genotyping error can inflate laboratory expenses and should be carefully considered. Chapter 2 – Elk road ecology on state Highway 20 in Skagit Valley, Skagit County, Washington. Wildlife-vehicle collisions pose a significant hazard to humans and wildlife. In Skagit Valley, Washington,158 elk (Cervus canadensis) roadkills were documented between 2002 and 2014 on 34.8 kilometers of state highway 20 between the towns of Sedro-Woolley and Concrete. In the current study, I documented road crossing activity between July and December 2013 between the towns of Sedro-Woolley and Concrete using string traps and remote cameras on game trails (n = 722 trail detections). Roadkill data were compiled from agency reports over comparable time periods for spatial analysis (July to December 2013 (n = 22)) and modeling (January 2012 to January 2014 (n =103)). Roadkill locations were weakly correlated with road crossing locations across the study area (Kendall’s tau = 0.23, P \u3c 0.001). Statistically significant hotspots were found for roadkills (n = 4) and road crossing activity (n = 5) (P \u3c 0.05). One roadkill hotspot coincided with one road crossing hotspot. Presence / absence of road crossing activity and roadkills in 216 0.16-km road segments were each modeled against 10 habitat variables and 4 road variables using logistic regression. The best road crossing model indicated that road crossing activity was negatively associated with distance to forest, distance to streams, distance to crops, percent developed area, and guardrail length. Road crossing predictors with the highest relative importance values in the best model were Distance to forest (RI = 1.00), Distance to crops (RI = 1.00), and Distance to streams (RI = 1.00); however, Distance to streams had 95% confidence intervals containing zero. The best roadkill model indicated that roadkills were negatively associated with distance to pasture/hay, percent developed area, and roadside slope, and positively associated with percent forest cover. Roadkill predictors with the highest relative importance values were Distance to pasture/hay (RI = 01.00) and Percent forest cover (RI = 1.00). Understanding the spatial distribution of road crossing activity and roadkills, combined with the habitat and road factors associated with them, can inform management of wildlife and vehicles in rural areas

Risk Analysis Using Artificial Intelligence Algorithms to Prevent Collisions on Roadway Segments

This thesis focused on improving the risk analysis algorithms used in collision avoidance systems (CASs) designed to reduce the risk of three types of collision on roadway segments: animal-to-vehicle collisions, pedestrian-to-vehicle collisions, and pedestrian-to-pedestrian collisions. Currently available CASs use only one input indicator. This approach is limited as the CASs: apply a simple risk analysis algorithm based on a fixed threshold to identify risky situations; cannot simultaneously capture a variety of important collision contributing factors; and cannot combine multiple contributing factors into a single composite risk indicator. The goal of this thesis was to use artificial intelligence algorithms to create a composite risk indicator based on a combination of various input indicators. The thesis goal was achieved through four objectives: 1) Develop a fuzzy rule-based algorithm for a next generation roadside animal detection system; 2) Develop a fuzzy rule-based algorithm for a smart protection system to reduce the number of collisions with police officers on duty on the roadway; 3) Develop a semi-supervised machine learning algorithm for a smart protection system to reduce the number of collisions with police officers on duty on the roadway; and 4) Develop a risk analysis approach to evaluate physical distancing on urban sidewalks. Improvement of the existing risk analysis algorithm in objective 1 resulted in capturing driver behavior, animal behavior, and the spatial and temporal interaction between animal and vehicle. It also resulted in differentiating risk for following and leading vehicle and generating no-risk when vehicle passed from animal. Objectives 2 and 3 were part of the same CAS study. Improvement of the existing risk analysis algorithm in both objectives 2 and 3 resulted in capturing pedestrian behavior, driver behavior, the spatial and temporal interaction between pedestrian and vehicle with 94% accuracy when estimating all risk labels, and 88% success when identifying near miss collisions. Objective 4 successfully reflected the role of density and exposure time in the level of physical distancing. It could help decision-makers to select the most appropriate interventions (e.g., sidewalk expansion) for pedestrians to maintain physical distancing

Risk Analysis Using Artificial Intelligence Algorithms to Prevent Collisions on Roadway Segments

This thesis focused on improving the risk analysis algorithms used in collision avoidance systems (CASs) designed to reduce the risk of three types of collision on roadway segments: animal-to-vehicle collisions, pedestrian-to-vehicle collisions, and pedestrian-to-pedestrian collisions. Currently available CASs use only one input indicator. This approach is limited as the CASs: apply a simple risk analysis algorithm based on a fixed threshold to identify risky situations; cannot simultaneously capture a variety of important collision contributing factors; and cannot combine multiple contributing factors into a single composite risk indicator. The goal of this thesis was to use artificial intelligence algorithms to create a composite risk indicator based on a combination of various input indicators. The thesis goal was achieved through four objectives: 1) Develop a fuzzy rule-based algorithm for a next generation roadside animal detection system; 2) Develop a fuzzy rule-based algorithm for a smart protection system to reduce the number of collisions with police officers on duty on the roadway; 3) Develop a semi-supervised machine learning algorithm for a smart protection system to reduce the number of collisions with police officers on duty on the roadway; and 4) Develop a risk analysis approach to evaluate physical distancing on urban sidewalks. Improvement of the existing risk analysis algorithm in objective 1 resulted in capturing driver behavior, animal behavior, and the spatial and temporal interaction between animal and vehicle. It also resulted in differentiating risk for following and leading vehicle and generating no-risk when vehicle passed from animal. Objectives 2 and 3 were part of the same CAS study. Improvement of the existing risk analysis algorithm in both objectives 2 and 3 resulted in capturing pedestrian behavior, driver behavior, the spatial and temporal interaction between pedestrian and vehicle with 94% accuracy when estimating all risk labels, and 88% success when identifying near miss collisions. Objective 4 successfully reflected the role of density and exposure time in the level of physical distancing. It could help decision-makers to select the most appropriate interventions (e.g., sidewalk expansion) for pedestrians to maintain physical distancing

Effectiveness of Wildlife Mitigation Treatments Along the Nelsonville Bypass

SJN 135024The Nelsonville Bypass is a 9 mile stretch of U.S. Route 33 that runs through the Wayne National Forest, an area high in species diversity and home to several threatened and endangered species. The motorist safety, economic and conservation values of building effective mitigation features that reduce vehicle-wildlife collisions along the bypass have been nationally recognized. Mitigation features include: high and low fencing to reduce wildlife trespass into the right-of-way (ROW), uni-directional jump outs for wildlife exit from the ROW, underpasses and ecopassages to maintain habitat connectivity across the highway, high-mast lighting to lure bats above traffic flow, and replacement of wetlands and bat roosting habitat. Our two-year study employed road surveys, continuous monitoring of jump outs and wildlife passages, population estimations, detailed mapping of fence structures and breaches, and radio telemetry of an endangered target species. Road surveys of the bypass and control highways revealed that the mitigation structures reduced deer-vehicle collisions, but collisions still occurred on the bypass. Although, generally well-constructed, we identified several ways in which the mitigation features could be made more effective. Placement of fencing near the outer boundary of the ROW made it vulnerable to damage from erosion and tree falls, and isolated high-quality habitats within the ROW. Placement of the fence within 30-50 ft. of the roadway on less rugged terrain away from the forest would likely reduce costs of construction and maintenance while allowing wildlife access to habitat within the ROW. We also recommended regular maintenance inspections and mowing on both sides of the fencing. Jump outs were effective uni-directional exits, but wildlife, particularly deer, were not compelled to exit the expansive area within the ROW fencing. Placement of the fence with jump outs closer to the road would reduce habitat within the fence and combined with traffic noise may increase jump out use. Large wildlife underpasses and crossings were well used by a variety of mammal species. Smaller mammals used the small wildlife ecopassages. Reptiles and amphibians avoided the use of underpasses and road mortality rates of amphibians were high on Ohio State Route 78 (tributary road) near wetlands. Placement and passage design were contributing factors to high amphibian mortality. Radio-tracking of rattlesnakes discovered that snakes easily trespassed the small wildlife fencing and used the habitat within the ROW, likely because it was warmer than the surrounding forested habitat. No road mortality or attempted road crossings by rattlesnakes were detected. Finally, while bats foraged near the lights, most species were detected with equal frequency at different heights under the lighting. Our report details these findings and provides additional recommendations to improve design and construction of wildlife mitigation features both along the Nelsonville Bypass, and for future design of mitigation features for roadways in high-density wildlife areas

FY2010 Annual Report Research, Intelligent Transportation Systems, and Technology Transfer Activities, October 18, 2011

Transportation research makes a difference for Iowans and the nation. Implementation of cost effective research projects contributes to a transportation network that is safer, more efficient, and longer lasting. Working in cooperation with our partners from universities, industry, other states, and FHWA, as well as participation in the Transportation Research Board (TRB), provides benefits for every facet of the DOT. This allows us to serve our communities and the traveling public more effectively. Pooled fund projects allow leveraging of funds for higher returns on investments. In 2010, Iowa led fifteen active pooled fund studies, participated in twenty-two others, and was wrapping-up, reconciling, and closing out an additional 6 Iowa Led pooled fund studies. In addition, non-pooled fund SPR projects included approximately 20 continued, 9 new, and over a dozen reoccurring initiatives such as the technical transfer/training program. Additional research is managed and conducted by the Office of Traffic and Safety and other departments in the Iowa DOT

Landscape functional connectivity and animal movement: application of remote sensing for increasing efficiency of road mitigation measures

Roads are a major threat to wildlife due to induced mortality and restrictions to animal movement. A central issue in conservation biology is the accurate site identification for the implementation of multispecies mitigation measures, on roads. Those measures entail high costs and methodological challenges and their efficiency highly depend on the right location. The aim of this PhD is to inform, through remote sensing and connectivity modelling, how to increase the efficiency of planning mitigation measures to reduce roadkill and promote connectivity; and demonstrate the usefulness of remote sensing in defining suitable areas for the conservation of an endangered species that often occurs in the vicinity of roads. To do so, we first assessed whether occurrence-based strategies were able to infer functional connectivity, compared to those more complex and financially demanding based on telemetry, with respect to daily and dispersal movements. Secondly, we assessed whether remote sensing data were sufficiently informative to identify key habitats for a threatened species around road verges. Thirdly, we assessed the predictive and prioritisation ability of road mitigation units intercepting multispecies corridors to prevent vulnerability to roadkill. Findings revealed that simple models are suitable as complex ones for both daily and dispersal movements, allowing for costly-effective connectivity assessments. Results demonstrated the ability of free remote sensing data to identify microhabitat conditions in verges and surrounding landscape, for a threatened rodent, allowing for the delimitation of refugee areas and definition of monitoring strategies for the species. Undemanding data (occurrence and remote sensing) were able to describe species-specific ecological requirements for birds, bats and non-flying mammals as well as roadkill patterns, possibly due to similar overlapping corridors and habitats, despite some mismatches that occurred for highly mobile species. This framework ensured high efficiency in prioritisation of multispecies roadkill mitigation planning, resilient to long-term landscape dynamics; Conectividade funcional da paisagem e movimento animal: aplicação da detecção remota para aumentar a eficiência de medidas de mitigação em estradas. Resumo: As estradas constituem uma enorme ameaça para a vida selvagem devido à mortalidade. Uma questão central é a identificação dos locais para implementar medidas de mitigação multiespécies, em estradas. Essas medidas envolvem custos elevados e desafios metodológicos e sua eficiência depende muito da localização correcta. O objetivo deste doutoramento é informar, através de detecção remota e conectividade, como aumentar a eficiência do planeamento de medidas de mitigação para reduzir atropelamentos e promover a conectividade; e demonstrar a utilidade da detecção remota na definição de áreas adequadas para a conservação de espécies ameaçadas que podem ocorrer nas proximidades de estradas. Portanto, primeiro avaliamos se os dados resultantes de amostragens simples eram capazes de inferir conectividade funcional, em comparação com estratégias complexas, respeito aos movimentos diários e de dispersão. Segundo, avaliamos se os dados de detecção remota eram suficientemente informativos para identificar habitats-chave para uma espécie ameaçada em torno das margens das estradas. Terceiro, avaliamos a capacidade preditiva e de prioritização das unidades de mitigação de estradas que cruzam corredores multi-espécies para reduzir o risco de atropelamentos. Os resultados revelaram que os modelos simples são adequados quanto os complexos para os movimentos diários e de dispersão. Os resultados demonstraram a capacidade dos dados de detecção remota gratuitos em identificar condições de microhabitats nos habitats de berma e na paisagem circundante, para um roedor ameaçado, permitindo a delimitação de áreas de refúgio. Dados pouco exigentes (ocorrência e detecção remota) foram capazes de descrever os requisitos ecológicos específicos de aves, morcegos e mamíferos não voadores, bem como padrões de atropelamentos, possivelmente devido a corredores e habitats semelhantes, apesar de haver algumas incompatibilidades para espécies de maior mobilidade. Essa estrutura foi capaz de garantir uma elevada eficiência na prioritização de planeamento de mitigação de atropelamentos para multi-espécies, resiliente à dinâmica da paisagem de longo prazo

Runtime Restriction of the Operational Design Domain: A Safety Concept for Automated Vehicles

Automated vehicles need to operate safely in a wide range of environments and hazards. The complex systems that make up an automated vehicle must also ensure safety in the event of system failures. This thesis proposes an approach and architectural design for achieving maximum functionality in the case of system failures. The Operational Design Domain (ODD) defines the domain over which the automated vehicle can operate safely. We propose modifying a runtime representation of the ODD based on current system capabilities. This enables the system to react with context-appropriate responses depending on the remaining degraded functionality. In addition to proposing an architectural design, we have implemented the approach to prove its viability. An analysis of the approach also highlights the strengths and weaknesses of the approach and how best to apply it. The proof of concept has shown promising directions for future work and moved our automated vehicle research platform closer to achieving level 4 automation. A ROS-based architecture extraction tool is also presented. This tool helped guide the architectural development and integration of the automated vehicle research platform in use at the University of Waterloo, and improve the visibility of safety and testing procedures for the team