Recently, there has been a growing interest in research towards autonomous vehicles, aiming to improve efficiency, safety, comfort and to eliminate human errors. However, a question arises: what should set the speed of the vehicle? The road legal limit, the maximum possible given the traffic conditions, the road critical speed or any other matter? Moreover, it has been mentioned in literature that drivers feel unsafe when using cruise control systems, namely in cornering conditions. Therefore, to study the driver’s behaviour when cornering, a test vehicle was equipped with a dedicated data acquisition system to acquire the vehicle dynamics and the driver heart rate. Trials with volunteers were carried out on a 22 km selected route, consisting of multiple types of roads, from city roads to highways. Based on the field results, we propose a control strategy for the vehicle speed when cornering relating the curve radius with the centrifugal acceleration. The proposed control strategy could be easily adopted by advanced driver assistance systems and autonomous vehicles. The reproduction of a human-like behavior will enhance the perception of safety and comfort of both the driver and occupants.

A automação está a mudar o mundo. Como na aeronáutica, as empresas da indústria automóvel estão atualmente a desenvolver veículos autónomos. No entanto a autonomia do veículo não é completa, necessitando por vezes das ações do condutor. A forma como é feita a transição entre condução manual e autónoma e como mostrar esta informação de transição para o condutor constitui um desafio para a ergonomia. Novos ecrãs estão a ser estudados para facilitar estas transições. Este estudo usou um simulador de condução para investigar, se a informação em realidade aumentada pode influenciar positivamente a experiência do condutor durante a condução manual e autónoma. Compararam-se duas formas de apresentar a comunicação ao condutor. Um “conceito AR” mostrou toda a informação no para-brisas para ser mais fácil o condutor aceder à informação. O “conceito IC” mostrou a informação que aparece atualmente nos carros, usando o painel de instrumentos e o e-HUD. Os resultados indicam que a experiência do utilizador (UX) é influenciada pelos conceitos, sendo que o “conceito AR” teve uma melhor UX em todos os estados de transição. Em termos de confiança, os resultados revelaram também valores mais elevado para o “conceito AR”. O tipo de conceito não influenciou nem o tempo nem o comportamento de retomar o controlo do carro. Em termos de situação consciente, o “conceito AR” deixa os condutores mais conscientes durante a disponibilidade e ativação da função. Este estudo traz implicações para as empresas que desenvolvem a próxima geração de ecrãs no mundo automóvel.

Autonomous Vehicles will fundamentally disrupt the mobility and automotive industry and, hence, challenge current business models and players on the market. Car manufacturers and major tech companies, as well as many start-ups, are already today fighting for supremacy on the future market. Therefore, the questions arise which business models will potentially be successful and which players will dominate the market. This thesis explores high potential use cases for autonomous mobility services in urban passenger transportation in order to subsequently identify auspicious business models. For the examination of the use cases, market sizes, the affinity of different consumer segments towards autonomous vehicles, as well as a comprehensive comparison of the current and the estimated future mobility market concerning the fulfillment of customer needs, are considered. Business models are identified based on 55 business model patterns that evolved in business model innovation during the past 50 years. All patterns are applied to the mobility industry, and a selection of promising patterns is further elaborated for the high-potential use cases. The result is a rating of the fit between promising business models and the high-potential use cases. The identification of stakeholders and the analysis which part of the value chain each of them may occupy results in different conceivable scenarios. However, the analysis also reveals stakeholders who, due to the current market development, have a challenging future ahead and may be driven off the market in the long run.

Over the past decade, the autonomous vehicle (AV) market has continued to gather momentum and the development of such technology was placed on the board agenda of many leading automotive manufacturers, technology companies and start-ups. In this transformative environment, companies are looking for opportunities to exploit the value potential of AVs by enhancing existing and developing new business models (BMs). This research aims to identify high potential use cases for AVs in passenger transportation with a geographical focus on Europe. Use cases are segmented by its operating environment and ownership model and subsequently analysed regarding their potential for commercial success. This research highlights the importance of taking a holistic approach when measuring the business potential of radical technologies. Among thirteen identified use cases, two reached the designation high potential use case and were thereby described in detail. Emphasis is put on the profitability analysis of both use cases by calculating the business rationale including a sensitivity analysis to increase robustness for geographical fluctuations of estimates. Furthermore, this research gives an outlook on the strategic positioning of industry players in the mobility ecosystem of the selected use cases. The importance of strategic partnerships both in technology development and the development of mobility services is highlighted, as complexity and resources needed exceed what companies can achieve by themselves. The path towards mainstream adoption of high potential use cases is outlined and critically assessed by considering primary insights from automotive, mobility service providers and other related industries.

This thesis deals on the development an autonomous system, able to move within a culture accurately and effectively, being functional and economically viable in small cultures, which constitute the most percentage to agricultural production area in Colombia. A system capable to mapping the boundaries of culture it has develop with sub metric GPS in free signal without real time correction, and generate an innovative and proprietary methodology that integrates many different systems to a level pilot of one hectare. The GPS data are stored in a file and used for limits correction with software developed, using methods of interpolation and optimization with cubic splines, to make the distribution of culture and establishment the points of automatic positioning to the vehicle within the culture. With these coordinates, the trajectories are generate using A* algorithm to move between two points fulfilling the tasks of culture, interacting with a remote computer over wireless network Xbee. The result is a vehicle capable of dispensing liquid fertilizer with an error less than 3% by volume, with the possibility of moving in a culture following the trajectory generated by A* algorithm with error less than 10 cm, based on the embedded sensing in the vehicle and supervised by remote computer.

Vehicle-to-Infrastructure (V2I) collaboration constitutes an emerging paradigm for advancing autonomous driving. However, the integrated collaboration of perception, communication, and computation within V2I system remains a critical challenge. To address it, we propose a Software-Defined Network (SDN)-based collaborative approach for Autonomous Vehicle and Road Infrastructure Network (AVRIN). The architecture designates road infrastructures as road nodes and autonomous vehicles as dynamic vehicle nodes, establishing AVRIN through SDN. The control plane dynamically maintains global network topology and distributed flow tables by continuously evaluating node accessibility, while the forwarding plane is responsible for packet transmission via the OpenFlow protocol. In the perception module, road nodes divide the perception range into spatial units, whereas vehicle nodes dynamically align these units with their drivable areas across temporal sequences. Through coordinated communication and computation modules, road nodes strategically allocate dedicated bandwidth and computational resources. Building on this approach, we develop a particle swarm-based multi-objective optimization algorithm to achieve balanced co-optimization across perception, communication, and computation. Experimental validation demonstrates its superior collaborative Bird's Eye View (BEV) detection performance on the V2X-Sim 2.0 dataset, outperforming existing approaches by 10.37% in mean Average Precision. Furthermore, evaluations on the newly collected Jiading dataset, from a real-world urban roadway, confirm the approach's robustness with 1.823-second computation time under dynamic network conditions.

The objective of this paper is to present the developments of an ongoing project that aims at implementing an internal materials movement system using autonomous vehicles for supporting logistics processes. In particular the project focus on the movement of final products, from assembly lines to the expedition warehouse and the supply of packaging materials (customer packages), from the raw materials warehouse to the assembly lines. This process is currently carried out by two stackers, in a milk-run environment and, in the context of this project, an innovative solution is proposed to improve the performance of the supply and pickup processes, transforming and optimizing an entirely manual process into an automated one. The main challenge was to investigate the impact on the existing internal logistic system of the new solution and, simultaneously, to explore if new management strategies are needed to satisfy both throughput levels and overall supply chain needs. A simulation model was developed using ARENA Simulation Software to get insights concerning the new challenges posed by the solution to be adopted. Results have revealed that the use of simulation allowed the assessment of the impact of the new internal logistics solution and played a critical role to identify the best dispatching rules to schedule transportation orders in such a complex system. This research shows the flexibility of the simulation tool to address several complex management issues simultaneously that arises in the context of innovative solutions aligned with industry 4.0 challenges.

Advanced Driver Assistance Systems (ADAS) and autonomous driving require high positioning performance (high accuracy, reliability, and availability). These requirements are not always possible due to disruptions and limitations on Global Navigation Satellite System (GNSS) signals. Cooperative positioning approaches aim to mitigate the drawbacks of GNSS by exploring the collaboration of road participants enhancing positioning performance. The development, testing and evaluation of cooperative positioning approaches is a complex process that is very difficult to conduct in real world since it depends on several vehicles equipped with sensors for perception and localization, as well as referencing systems which are expensive. To overcome this, researchers rely on simulation tools to experiment with their systems. This paper presents a study on simulation tools suitable for testing cooperative positioning scenarios using GNSS as one of the main sensors. We focus on tools capable of simulating realistic environments for vehicles with multiple sensors (autonomous driving), traffic and mobility models, V2X communications, and generating raw GNSS signals. We compare applications, features, and interfaces of these tools and analyze their integration for a cooperative positioning simulation pipeline.

Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This paper explores multiple combinations of both approaches and determines the procedure to select the best model based on them through a simulated environment. The evaluation of the proposed model in a real-world industrial scenario shows that the positioning error (according to different metrics including the 95th and 99th percentiles) is highly improved with respect to the traditional fingerprint.

Visible Light communication is a data transmission technology that uses the LED lighting infrastructure to simultaneously illuminate and communicate. The ubiquitous existence of LED lamps opened a new opportunity for addressing VLC communication in many indoor communication scenarios. The motivation for the application presented in this paper is the modern, efficient management of warehouses supported by autonomous navigation robots that grab goods and deliver the items at the packaging station. This functionality demands bi-directional communication among infrastructures and vehicles. In this paper we propose links for Infrastructure-To-Vehicle (I2V), Vehicle-ToInfrastructure (V2I) and Vehicle-To-Vehicle (V2V) to perform indoors, bi-directional communication for robot navigation in automated warehouses. In this work it is proposed a bidirectional communication system between a static infrastructure and a mobile robot (I2V). The LED lamps of the warehouse illumination system are used to lighten the space, and to transmit information about position and about racks content. The mobile robots communicate with the infrastructure (V2I) to transmit information on the items that are being removed and carried to the packaging station. The communication among the autonomous robots (V2V) provides information on the number of items intended to be collected when the vehicles are in the same lane, possibly with the purpose of collecting the same items. Different codification schemes are proposed to establish the V2I, I2V and V2V links. Tri-chromatic white LEDs with the red and blue chips modulated at different frequencies and a photodetector based on a-SiC:H/a-Si:H with selective spectral sensitivity are used at the emitter and receiver. Position information is provided by each LED lamp to the autonomous vehicle by adequate modulation of the RGB emitters. The decoding strategy is based on accurate calibration of the output signal. Different scenarios were designed and tested. Requirements related to synchronous transmission and flickering were addressed to enhance the system performance.