International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / There is an increasing demand for practical and powerful navigation system to lead people from one place to another quickly and rightly. The introduction of a new embedded operating system, Windows CE, allows us to design a compact, low-cost, efficient autonomous vehicle location and navigation system. This paper discusses the advantages of Windows CE, demonstrates the possibility of building an AVLNS based on it. Then a realization scheme of hardware platform and navigation software is presented.

Starting this thesis with the intention to inspire and to be inspired, I have tried to zoom out and look on designing a car from a new perspective. By telling a holistic design story rather than solving a specific problem I want people to imagine a future where one can live a modern and connected life in rural communities, all made possible after a green energy revolution. Design research, brand analysis, sketching, form verification using clay and digital modeling and advanced visualization were the main activities performed during the project. They helped me to explore, understand and successfully propose a complete story of vehicle and context. The result is BMW iMPULSE, a shared and fully autonomous vehicle that is wirelessly powered by clean energy and is always ready to support the spontaneous lifestyle

abstract: A novel underwater, open source, and configurable vehicle that mimics and leverages advances in quad-copter controls and dynamics, called the uDrone, was designed, built and tested. This vehicle was developed to aid coral reef researchers in collecting underwater spectroscopic data for the purpose of monitoring coral reef health. It is designed with an on-board integrated sensor system to support both automated navigation in close proximity to reefs and environmental observation. Additionally, the vehicle can serve as a testbed for future research in the realm of programming for autonomous underwater navigation and data collection, given the open-source simulation and software environment in which it was developed. This thesis presents the motivation for and design components of the new vehicle, a model governing vehicle dynamics, and the results of two proof-of-concept simulation for automated control. / Dissertation/Thesis / Masters Thesis Computer Science 2020

Indiana University-Purdue University Indianapolis (IUPUI) / Advancement in modern technology has brought with it an advent of increased interest in self-driving. The rapid growth in interest has caused a surge in the development of autonomous vehicles which in turn brought with itself a few challenges. To overcome these new challenges, automotive companies are forced to invest heavily in the research and development of autonomous vehicles. To overcome this challenge, simulations are a great tool in any arsenal that’s inclined towards making progress towards a self-driving autonomous future. There is a massive growth in the amount of computing power in today’s world and with the help of the same computing power, simulations will help test and simulate scenarios to have real time results. However, the challenge does not end here, there is a much bigger hurdle caused by the growing complexities of modelling a complete simulation environment. This thesis focuses on providing a solution for modelling a RADAR sensor for a simulation environment. This research presents a RADAR modeling technique suitable for autonomous vehicle simulation environment using open-source utilities. This study proposes to customize an onboard LiDAR model to the specification of a desired RADAR field of view, resolution, and range and then utilizes a density-based clustering algorithm to generate the RADAR output on an open-source graphical engine such as Unreal Engine (UE). High fidelity RADAR models have recently been developed for proprietary simulation platforms such as MATLAB under its automated driving toolbox. However, open-source RADAR models for open-source simulation platform such as UE are not available. This research focuses on developing a RADAR model on UE using blueprint visual scripting for off-road vehicles. The model discussed in the thesis uses 3D pointcloud data generated from the simulation environment and then clipping the data according to the FOV of the RADAR specification, it clusters the points generated from an object using DBSCAN. The model gives the distance and azimuth to the object from the RADAR sensor in 2D. This model offers the developers a base to build upon and help them develop and test autonomous control algorithms requiring RADAR sensor data. Preliminary simulation results show promise for the proposed RADAR model.

No description available.

In the context of path planning, the non-signalized intersections are always a challenging scenario due to the mixture of traffic flow. Most path planning algorithms use the information at the current time instance to generate an optimal path. Because of the dynamics of the non-signalized intersections, iteratively generating a path in a high frequency is necessary, resulting in an enormous waste of computational resources. Rapidly-exploring Random Tree (RRT) as an effective local path planning methodology can determine a feasible path in the static environment. Few improvements are proposed to adopt the RRT to the non-signalized intersections. Gaussian Processes Regression (GPR) is used to predict the other vehicles' future location. The location information in the current and future time instance is used to generate a probability position map. The map not only avoids useless sampling procedures but also increases the speed of generating a path. The optimal steering strategy is deployed to guarantee the trajectory is collision-free in both current and future time frames. Overall, the proposed probabilistic RRT algorithm can select a collision-free path in the non-signalized intersections by combining the GPR, probability position map, and optimal-steering. / Master of Science / Path planning problem is a challenge in the non-signalized intersections. Many path planning algorithms can generate an optimal path in the space domain but not in the time domain. Thus, the algorithms need to run iteratively at a high frequency to ensure the path's optimality in the time domain. By combining prediction and the standard RRT path planning algorithm, the resulting path ensures to be optimal in the space and time domain.

This study addresses the limitations of existing autonomous vehicle datasets, particularly the need for greater specificity of weather conditions and road types. We utilized X-CAR to highlight the challenges of extreme weather and non-urban road conditions on autonomous driving systems. Our dataset comprises recordings under seven distinct weather and lighting conditions across four road types. Notably, our research focuses on differentiating between various lighting and weather conditions and road types, which often need improvement in many existing datasets. We used the X-CAR platform to collect 360-degree image information and LiDAR point clouds at 10Hz. Due to the constraints of time and resources, we used algorithmic prediction to generate ground truth data via the Co-DETR 2D prediction algorithm. We validated the accuracy of the Co-DETR algorithm through partial manual annotation. However, it is undeniable that in some extreme conditions, the algorithm-generated ground truth can lead to results deviating from expectations and real-world situations. Therefore, we conducted a scaled manual annotation and controlled experiments, ensuring the highest level of accuracy. After the manual annotation, we validated our initial conclusions and trained a model based on YOLOv8x, focusing on weak environmental conditions. The final model underwent multiple iterations and achieved satisfactory accuracy. The enhanced model demonstrated a significant increase in detection accuracy compared to the original YOLOv8x model. At the same time, our analysis identifies weather conditions that markedly reduce detection accuracy, providing focal points for future dataset enhancements. / Master of Science / This study explores the limitations of current autonomous vehicle datasets, particularly their lack of detail regarding weather conditions and road types. We used X-CAR to examine how extreme weather and light conditions affect autonomous driving systems. Our dataset includes recordings from seven different weather and lighting conditions across four types of roads. Due to time and resource constraints, we used an algorithm to predict ground truth data with the help of Co-DETR. While not all data was fully annotated, we manually labeled part of the data to create an actual ground truth. This allowed us to verify our previous findings and train a model based on YOLOv8x, focusing on challenging conditions. The improved model showed much higher accuracy in detecting objects than the original YOLOv8x model. This study highlights the significant impact of weather conditions on detection accuracy and suggests areas for future improvements in datasets.

Autonomous Vehicles (AV) can navigate itself from point `A' to point `B' without the aid of humans. Research on autonomous vehicles were primarily focused on the localization, navigation and path planning schemes. This led to numerous methods in each of the elds of focus. This research focuses on creating a scheme for the autonomous vehicle to navigate using minimal sensors and get maximum data/infor- mation from the map. At rst a digital map contains various structures and each has an associated database. This database contains the details of the environment. At present these data are manipulated for use by humans and for this map to be used with autonomous vehicle require more sensors. This work designs maps for use with autonomous vehicle and navigates using di erential GPS (dGPS) of high accuracy for localization. Then the vehicle gets path and directions from digital map and nav- igates using multiple waypoints that are provided by the path. Finally, the scheme is tested and demonstrated through simulation and test results.

The development of Automated Driving Systems (ADS) has been ongoing for decades in varying levels of sophistication. Levels of automation are defined by Society of American Engineers (SAE) as 0 through 5, with 0 being full human control and 5 being full automation control. Another way to describe levels of automation is through concepts of Functional Safety (FuSa) and Operational Safety (OpSa). These terms of FuSa and OpSa are important, because ADS testing relies on both. Current recommendations for ADS testing include both OpSa and FuSa requirements. However, an examination of ADS safety requirements (e.g., industry reports, post-crash analysis reports, etc.) reveals that ADS safety arguments, in practice, depend almost completely on well-trained human operators, referred to in the industry as in vehicle fallback test drivers (IFTD). To date, the industry has never fielded a truly SAE L4 ADS on public roads due to this persistent hurdle of needing a human operator for Operational Safety. There is a tendency in ADS testing to reference International Standards Organization (ISOs) for validated vehicles for vehicles that are still in development (i.e., unvalidated). To be clear, ISOs for ADS end products are not necessarily applicable to ADS in development. With this in mind, there is a clear gap in the industry for unvalidated ADS literature. Because of this gap, ADS testing for unvalidated vehicles often relies on safety requirements for validated vehicles. This issue remains a significant challenge for ADS testing. Recognizing this gap in on-road, in-development vehicle safety, there is a need for the ADS industry to develop a clear strategy for transitioning from an IFTD (Operational Safety) to an ADS (Functional Safety). Therefore, the purpose of this thesis is to present a framework for transitioning from Operational Safety to Functional Safety. The framework makes this possible through an inductive analysis of available definitions of onroad safety to arrive at a definition that leverages Functional and Operational Safety along a continuum. Ultimately, the framework aims to contribute to onroad safety testing for the ADS industry. / Master of Science / The development of Self-Driving Cars has been ongoing for decades in varying levels of sophistication. Levels of automation are defined by Society of American Engineers (SAE) as 0 through 5, with 0 being full human control and 5 being full automation control. Another way to describe levels of automation is through concepts of Robotic Control and Human Control. If a vehicle relies completely on Human Control, a human operator is responsible for all on-road safety. On the other hand, a fully autonomous would be considered fully in Robotic Control. These terms of Robotic Control and Human Control are important, because Self-Driving Car testing relies on both. Current recommendations for Self-Driving Car testing include both Robotic Control and Human Control requirements. However, an examination of Self-Driving Cars documentation (e.g., industry reports, post-crash analysis reports, etc.) reveals that Self-Driving Car safety arguments, in practice, depend almost completely on well-trained human operators. To date, the industry has never fielded a truly SAE L4 Self-Driving Car on public roads due to this persistent hurdle of needing a human operator for Human Control. There is a tendency in Self-Driving Car testing to reference standars for validated vehicles for vehicles that are still in development (i.e., unvalidated). To be clear, standards for Self-Driving Car end products are not necessarily applicable to Self-Driving Cars in development. With this in mind, there is a clear gap in the industry for unvalidated Self-Driving Car literature. Because of this gap, Self-Driving Car testing for unvalidated vehicles often relies on documentation for validated vehicles. This issue remains a significant challenge for Self-Driving Car testing. Recognizing this gap in on-road, in-development vehicle safety, there is a need for the Self-Driving industry to develop a clear strategy for transitioning from Human Control to Robot Control. Therefore, the purpose of this thesis is to present a framework for transitioning from Human to Robot Control. The framework makes this possible through an inductive analysis of available definitions of onroad safety to arrive at a definition that leverages all definitions of Safety along a continuum. Ultimately, the framework aims to contribute to onroad safety testing for the Self-Driving industry.

No description available.