Towards the design of robotic drivers for full-scale self-driving racing cars

Autonomous vehicles are undergoing a rapid development thanks to advances in perception, planning and control methods and technologies achieved in the last two decades. Moreover, the lowering costs of sensors and computing platforms are attracting industrial entities, empowering the integration and development of innovative solutions for civilian use. Still, the development of autonomous racing cars has been confined mainly to laboratory studies and small to middle scale vehicles. This paper tackles the development of a planning and control framework for an electric full scale autonomous racing car, which is an absolute novelty in the literature, upon which we report our preliminary experiments and perspectives on future work. Our system leverages real time Nonlinear Model Predictive Control to track a pre-planned racing line. We describe the whole control system architecture including the mapping and localization methods employed

Teaching Autonomous Systems at 1/10th-scale

Teaching autonomous systems is challenging because it is a rapidly advancing cross-disciplinary field that requires theory to be continually validated on physical platforms. For an autonomous vehicle (AV) to operate correctly, it needs to satisfy safety and performance properties that depend on the operational context and interaction with environmental agents, which can be difficult to anticipate and capture. This paper describes a senior undergraduate level course on the design, programming and racing of 1/10th-scale autonomous race cars. We explore AV safety and performance concepts at the limits of perception, planning, and control, in a highly interactive and competitive environment. The course includes an ethics-centered design philosophy, which seeks to engage the students in an analysis of ethical and socio-economic implications of autonomous systems. Our hypothesis is that 1/10th-scale autonomous vehicles sufficiently capture the scaled dynamics, sensing modalities, decision making and risks of real autonomous vehicles, but are a safe and accessible platform to teach the foundations of autonomous systems. We describe the design, deployment and feedback from two offerings of this class for college seniors and graduate students, open-source community development across 36 universities, international racing competitions, student skill enhancement and employability, and recommendations for tailoring it to various settings

Implementing and Tuning an Autonomous Racing Car Testbed

Achieving safe autonomous driving is far from a vision at present days, with many examples like Uber, Google and the most famous of all Tesla, as they successfully deployed self driving cars around the world. Researchers and engineers have been putting tremendous efforts and will continue to do so in the following years into developing safe and precise control algorithms and technologies that will be included in future self driving cars. Besides these well known autonomous car deployments, some focus has also been put into autonomous racing competitions, for example the Roborace. The fact is that although significant progress that has been made, testing on real size cars in real environments requires immense financial support, making it impossible for many research groups to enter the game. Consequently, interesting alternatives appeared, such as the F1 Tenth, which challenges students, researchers and engineers to embrace in a low cost autonomous racing competition while developing control algorithms, that rely on sensors and strategies used in real life applications. This thesis focus on the comparison of different control algorithms and their effectiveness, that are present in a racing aspect of the F1 Tenth competition. In this thesis, efforts were put into developing a robotic autonomous car, relying on Robot Operative System, ROS, that not only meet the specifications from the F1 Tenth rules, but also allowed to establish a testbed for different future autonomous driving research.Obter uma condução autónoma segura está longe de uma visão dos dias de hoje, com exemplos como a Uber, Google e o mais famoso deles todos, a Tesla, que já foram globalmente introduzidos com sucesso. Investigadores e engenheiros têm colocado um empenho tremendo e vão continuar a fazê-lo nos próximos anos, a desenvolver algoritmos de controlo precisos e seguros, bem como tecnologias que serão colocados nos carros autónomos do futuro. Para além destes casos de sucesso bem conhecidos, algum foco tem sido colocado em competições de corridas de carros autónomos, como por exemplo o Roborace. O facto ´e que apesar do progresso significante que tem sido feito, fazer testes em carros reais em cenários verdadeiros, requer grande investimento financeiro, tornando impossível para muitos grupos de investigação investir na área. Consequentemente, apareceram alternativas relevantes, tal como o F1 Tenth, que desafia estudantes, investigadores e engenheiros a aderir a uma competição de baixos custos de corridas autónomas, enquanto desenvolvem algoritmos de controlo, que dependem de sensores e estratégias usadas em aplicações reais. Esta tese foca-se na comparação de diferentes algoritmos de controlo e na eficácia dos mesmos, que estão presentes num cenário de corrida da competição do F1 Tenth. Nesta tese, foram colocados muitos esforços para o desenvolvimento de um carro autónomo robótico, baseado em Robot Operative System, ROS, que não só vai de encontro `as especificações do F1 Tenth, mas que também permita estabelecer uma plataforma para futuras investigações de condução autónoma

Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This article analyses the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analysed. Conclusions show that operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.Comment: 18 pages, 5 figure