Intersections represents one of the most common accident sites in traffic today. The biggest cause of accidents is obstructed view and subpar communication between vehicles. Since autonomous vehicles rely on sensors that require a direct view intersections are some of the most complex situations. Where the potential for inter vehicular communication exists between modern vehicles, it is absent in the older generation. An overseeing intersection system can fill this function during the transition period to fully autonomous traffic. This project aimed to implement an intersection system to assist autonomous vehicles through a crossroad. The assist system’s objective was to collect and transmit data from cars close to the junction to the autonomous vehicles nearby. The concept was tested in simulations by having models traverse a crossroad to evaluate how it utilised the external information. No persistent conclusion could be made due to insufficient simulation environment and vehicle model control.

This thesis report presents a study on the virtualization of an Electric Cabin Scooter used to validate the feasibility of converting it into an autonomous vehicle. The project aimed to design, develop, and test a virtual model of the car that can navigate from points A to B while avoiding obstacles. The report describes the methodology used in the project, which includes setting up the workspace, construction of the virtual model, implementation of ROS2 controllers, and integration of SLAM and Navigation2. The thesis report also describes and discusses related work, as well as the theoretical background of the project. Results show a successfully developed working virtual vehicle model, which provides a solid starting point for future work. / Detta examensarbete presenterar en studie om virtualiseringen av en elektrisk kabinscooter. Den virtuella modellen används för att validera genomförbarheten av att omvandla den till ett autonomt fordon. Projektet syftade till att designa, utveckla och testa en virtuell modell av bilen som kan navigera från punkt A till B medan den undviker hinder. Rapporten beskriver metodiken som används i projektet, vilket inkluderar att sätta upp arbetsytan, konstruktion av den virtuella modellen, implementering av ROS2-kontroller och integration av SLAM och Navigation2. Rapporten diskuterar även relaterat arbete, samt teoretisk bakgrund till arbetet. Resultaten visar en framgångsrikt utvecklad fungerande virtuell fordonsmodell, som ger en solid utgångspunkt för framtida arbete.

コウモリは1送信2受信器のミニマルな超音波センシングデザインからは想像できない，高度な3次元飛行を実現させている．本論文では，①繰り返し同じ障害物環境下を飛行するコウモリの未知と既知の空間に対する音響センシング行動の違いを比較した．さらに，②未知環境飛行時に見られる特徴的な空間スキャニングの行動パターンをモデル化し，自律走行車を用いてコウモリの行動の有用性を実環境センシングのふるまいから定量的に評価した． / Bats possess a highly developed biosonar system that can be regarded as the minimum sensor requirement for three-dimensional spatial sensing. The present study 1) experimentally investigated changes in the pulse direction, pulse emission timing and flight path of CF-FM bats during an obstacle avoidance flight as the bats became familiar with the space around them and 2) expressed behavioral principles observed in the bats during flight, especially in an unfamiliar space, using an algorithm and then embedded the principles into an autonomous vehicle equipped with simple ultrasound sensors. The findings of this world-leading biomimetic research offer new possibilities for artificial-intelligence navigation systems. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

International crash statistics indicate a decrease of bicycle crashes, but at a slower pace compared to total crash numbers. The share of crashes with involved cyclists is above the modal share (see [1] for an overview). Depending on sources, types of analyses, and geographic regions, crash statistics suggest high rates of singlebike crashes and crashes between cyclists and other vulnerable road users (VRUs) [2], while cars are opponents in more than half of all fatal crashes in the European Union [3]. The design of th.e road environment is of particular relevance for crash risks. A study from London found three times higher injury odds for cyclists at intersections [4]. Connected and automated vehicles (CAV) are frequently said to increase the safety level in road traffic since they are less prone to human errors [5]. This might hold true in transport systems with little complexity, such as highways [6]. However, when it comes to complex situations in multimodal systems with multiple interactions between different road users, such as intersections in urban environments, existing solutions are not sufficient yet in terms of protecting VRUs. ... In order to contribute to the safety of VRUs in the interplay with CAVs in current systems, we propose a geospatial model, which delineates potential interaction risk zones from high definition (HD) maps and enriching these zones with additional information. These enriched risk zones are then provided as standardized OGC web service, which can be integrated in V2X systems. With this, we contribute to the visibility, and thus the safety of VRUs in connected transport systems. From a methodological point of view, the proposed model is a first step in integrating spatial context and semantic information explicitly into V2X communication. [From: Introduction]

The field of autonomous vehicle research is growing rapidly. The Congressional mandate for the military to use unmanned vehicles has, in large part, sparked this growth. In conjunction with this mandate, DARPA sponsored the Urban Challenge, a competition to create fully autonomous vehicles that can operate in urban settings. An extremely important feature of autonomous vehicles, especially in urban locations, is their ability to perceive their environment. The research presented in this thesis is directed toward providing an autonomous vehicle with real-time data that efficiently and compactly represents its forward environment as it navigates an urban area. The information extracted from the environment for this application consists of stop line locations, lane information, and obstacle locations, using a single camera and LIDAR scanner. A road/non-road binary mask is first segmented. From the road information in the mask, the current traveling lane of the vehicle is detected using a minimum distance transform and tracked between frames. The stop lines and obstacles are detected from the non-road information in the mask. Stop lines are detected using a variation of vertical profiling, and obstacles are detected using shape descriptors. A laser rangefinder is used in conjunction with the camera in a primitive form of sensor fusion to create a list of obstacles in the forward environment. Obstacle boundaries, lane points, and stop line centers are then translated from image coordinates to UTM coordinates using a homography transform created during the camera calibration procedure. A novel system for rapid camera calibration was also implemented. Algorithms investigated during the development phase of the project are included in the text for the purposes of explaining design decisions and providing direction to researchers who will continue the work in this field. The results were promising, performing the tasks fairly accurately at a rate of about 20 frames per second, using an Intel Core2 Duo processor with 2 GB RAM.

With all the advancements in autonomous and connected cars, there is a developing body of research around the security and robustness of driving automation systems. Attacks and mitigations for said attacks have been explored, but almost always solely in software simulations. For this thesis, I led a team to build the foundation for an open source platoon of scale semi-autonomous vehicles. This work will enable future research into implementing theoretical attacks and mitigations. Our 1/10 scale car leverages an Nvidia Jetson, embedded microcontroller, and sensors. The Jetson manages the computer vision, networking, control logic, and overall system control; the embedded microcontroller directly controls the car. A lidar module is responsible for recording distance to the preceding car, and an inertial measurement unit records the velocity of the car itself. I wrote the software for the networking, interprocess, and serial communications, as well as the control logic and system control.

<p dir="ltr">The growth of electric vehicles (EVs) offers several benefits for air quality improvement and emissions reduction. Nonetheless, EVs also pose several challenges in the area of highway transportation. These barriers are related to the limitations of EV technology, particularly the charge duration and speed of battery recharging, which translate to vehicle range anxiety for EV users. A promising solution to these concerns is V2V DWC technology (Vehicle to Vehicle Dynamic Wireless Charging), particularly mobile energy disseminators (MEDs). The MED is mounted on a large vehicle or truck that charges all participating EVs within a specified locus from the MED. However, current research on MEDs offers solutions that are widely considered impractical for deployment, particularly in urban environments where range anxiety is common. Acknowledging such gap in the literature, this thesis proposes a comprehensive methodological framework for optimal MED deployment decisions. In the first component of the framework, a practical system, termed “ChargingEnv” is developed using reinforcement learning (RL). ChargingEnv simulates the highway environment, which consists of streams of EVs and an MED. The simulation accounts for a possible misalignment of the charging panel and incorporates a realistic EV battery model. The second component of the framework uses multiple deep RL benchmark models that are trained in “ChargingEnv” to maximize EV service quality within limited charging resource constraints. In this study, numerical experiments were conducted to demonstrate the MED deployment decision framework’s efficacy. The findings indicate that the framework’s trained model can substantially improve EV travel range and alleviate battery depletion concerns. This could serve as a vital tool that allows public-sector road agencies or private-sector commercial entities to efficiently orchestrate MED deployments to maximize service cost-effectiveness.</p>

<p dir="ltr">Most road fatalities are caused by human error. To help mitigate this issue and enhance overall transportation safety, companies are turning to advanced driver assistance systems and autonomous vehicle development. Perception, a key module of these systems, mostly uses light detection and ranging (LiDAR) sensors and enables efficient obstacle detection and environment mapping. Extensive research on the use of LiDAR for autonomous driving has been documented in the literature. Yet still, several researchers and practitioners have advocated continued investigation of LiDAR placement alternatives. To address this research need, this thesis research begins with a comprehensive review of different sensor technologies – camera, radio detection and ranging, global positioning system, and inertial measurement units – and exploring their inherent strengths and limitations. Next, the thesis research developed a methodological multiple criteria framework and implemented it in the context of LiDAR placement optimization. Given the numerous criteria and placement alternatives associated with LiDAR placement, multi-criteria decision analysis (MCDA) was identified as an effective tool for LiDAR placement optimization. MCDA has been applied to some extent in decision-making regarding autonomous vehicle development. However, its application in LiDAR placement optimization remains unexplored. In evaluating the LiDAR placement alternatives, the research first established the placement alternatives and then developed a comprehensive yet diverse set of criteria – point density, blind spot regions, sensor cost, power consumption, sensor redundancy, ease of installation, and aesthetics. The data collection methods included the CARLA simulator, sensor datasheets, and questionnaire surveys. The relative importance among the evaluation criteria was established using weighting techniques such as respondent-assigned weighting, equal weighting, and randomly generated weighting. Then, to standardize the different measurement units, scaling was carried out using value functions developed for each criterion using data from the respondents. Finally, the weighted and scaled criteria measures were amalgamated to obtain the overall evaluation score for each alternative LiDAR placement design. This enabled the ranking of the placement designs and the identification of the best-performing and worst-performing designs. Hence, the optimization method used is the enumeration technique. The findings of this study serve as a reference for future similar efforts that seek to optimize LiDAR placements based on select criteria. Further, it is expected that the thesis’s framework will contribute to an enhanced understanding of the overall impact of LiDAR placement on autonomous vehicles, thus enabling the cost-effective design of their placement and, ultimately, improving AV operational outcomes, including traffic safety.</p>

<p dir="ltr">State estimation is a type of an <i>inverse problem</i> in which some amount of observed data needs to be processed using computer algorithms (which are designed using analytical techniques) to infer or reconstruct the underlying model that produced the data. Due to the ubiquity of data and interconnected control systems in the present day, many engineering domains have become replete with inverse problems that can be formulated as state estimation problems. The interconnectedness of these control systems imparts the associated state estimation problems with distinctive structural properties that must be taken into consideration. For instance, the observed data could be high-dimensional and have a dependency structure that is best described by a graph. Furthermore, the control systems of today interface with each other and with the internet, bringing in new possibilities for large-scale collaborative sensor fusion, while also (potentially) introducing new sources of disturbances, faults, and cyberattacks. </p><p dir="ltr">The main thesis of this document is to investigate the unique challenges related to the issues of robustness, resilience (to faults and cyberattacks), and scalability of state estimation algorithms. These correspond to research questions such as, <i>"Does the state estimation algorithm retain its performance when the measurements are perturbed by unknown disturbances or adversarial inputs?"</i> and <i>"Does the algorithm have any bottlenecks that restrict the size/dimension of the problems that it could be applied to?".</i> Most of these research questions are motivated by a singular domain of application: autonomous navigation of unmanned aerial vehicles (UAVs). Nevertheless, the mathematical methods and research philosophy employed herein are quite general, making the results of this document applicable to a variety of engineering tasks, including anomaly detection in time-series data, autonomous remote sensing, traffic monitoring, coordinated motion of dynamical systems, and fault-diagnosis of wireless sensor networks (WSNs), among others.</p>

Autonomous vehicles are currently under study and one of the critical parts is the localization of the vehicle in the environment. Different localization methods have been studied over the years, such as the GPS sensor, commonly fused with other sensors such as the IMU. However, situations where the vehicle crosses a tunnel, a bridge, or there is simply traffic congestion, can cause the vehicle to get lost. Therefore, other methods such as point cloud registration have been used, where two point clouds are aligned, thus finding the pose of the vehicle on a precomputed map. Point cloud alignment, although a useful and functional method, is not free from errors that can lead to vehicle mislocalization. The intention of this work is to develop and compare different metrics capable of measuring in real time the performance of the point cloud alignment algorithm used, in this case Normal Distribution Transform (NDT). Therefore, it is important first of all to know if the position obtained meets the minimum requirements defined, just by knowing the input and output parameters of the algorithm. In addition to classifying the positioning as good or bad, the objective is to have a quality parameter that allows estimating the error committed in a complex environment where the uncertainty is very high. In addition, the influence of vehicle speed on the error made by the point cloud alignment algorithm will also be studied to determine whether there is any significant correlation between them. For this purpose, four different metrics have been studied, two of them being new contributions to this algorithm, called Error Propagation and CorAl, while the ones called Hessian and Score are obtained from the alignment algorithm itself. Data used was previously recorded and corrected, therefore obtaining ground truth data. Once the metrics were implemented, all of them were subjected to the same experiments, thus obtaining for each instant a quality measure that allowed a fair comparison to be made. These experiments were carried out on two different routes, being simulated 5 times each. In addition, from these simulations the speed was recorded, allowing the influence study to be carried out. The results show that the best performing metrics in terms of classification and estimation were the Error Propagation and the Hessian, while being impossible to determine a threshold value for the case of CorAl. Furthermore, they show that despite being functional, the error estimation is still far from perfect. It has also been shown that the error estimation of the lateral axis of the vehicle is more complex than in the case of the longitudinal axis. Finally, a strong and positive relationship between the vehicle speed and the error made by the alignment algorithm has been found. / Autonoma fordon studeras för närvarande och en av de kritiska delarna är lokaliseringen av fordonet i omgivningen. Olika lokaliseringsmetoder har studerats genom åren, t.ex. GPS-sensorn som ofta kombineras med andra sensorer, t.ex. IMU. Situationer där fordonet korsar en tunnel, en bro eller där det helt enkelt är trafikstockningar kan leda till att fordonet tappar uppfattningen om sin position. Därför har andra metoder utvecklats, t.ex. registrering av punktmoln, där två punktmoln justeras för att hitta fordonets position på en förinställd karta. Även om punktmolnsjustering är en användbar och funktionell metod, är den inte fri från fel som kan leda till felaktig lokalisering av fordonet. Syftet med detta arbete är att utveckla och jämföra olika mätmetoder som i realtid kan mätaprestandan hos den algoritm för punktmolnsjustering som används, i detta fall Normal DistributionTransform (NDT). Därför är det viktigt att först och främst veta om den erhållna tjänsten uppfyllerde fastställda minimikraven, bara genom att känna till algoritmens in- och utgångsparametrar.Förutom att klassificera positioneringen som bra eller dålig är målet att ha en kvalitetsparametersom gör det möjligt att uppskatta det fel som begåtts i en komplex miljö där osäkerheten är myckethög. Dessutom kommer fordonshastighetens inverkan på felet som görs av algoritmen för justeringav punktmoln också att studeras för att avgöra om det finns någon signifikant korrelation mellandem. För detta ändamål har fyra olika mått studerats, varav två är nya bidrag till denna algoritm, kallade Error Propagation och CorAl, medan de som kallas Hessian och Score erhålls från själva anpassningsalgoritmen. Data har tidigare registrerats och korrigerats, vilket ger sanningsdata. När mätvärdena hade implementerats utsattes de alla för samma experiment, så att man för varje ögonblick fick ett kvalitetsmått som gjorde det möjligt att göra en rättvis jämförelse. Dessa experiment utfördes på två olika rutter, som simulerades 5 gånger vardera. Dessutom registrerades hastigheten från dessa simuleringar, vilket gjorde det möjligt att genomföra en påverkansstudie. Resultaten visar att de bäst presterande mätvärdena när det gäller klassificering och uppskattning var Error Propagation och Hessian. Dessutom visar de att feluppskattningen fortfarande är långt ifrån perfekt. Det har också visats att feluppskattningen av fordonets sidoaxel är mer komplex än i fallet med den längsgående axeln. Slutligen har ett starkt och positivt samband mellan fordonshastigheten och felet som görs av inriktningsalgoritmen hittats.