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The next generation of intelligent vehicles will evolve from being able to drive autonomously to ones that communicate with other vehicles and execute joint behaviors. Before allowing these vehicles on public roads, we must guarantee that they will not cause accidents. We will apply formal methods to ensure the degree of safety that cannot be assured with simulation or closed-track testing. However, there are challenges that need to be addressed when applying formal verification techniques to cooperative driving systems. This thesis focuses on the techniques that address the following challenges: 1. Automotive applications interact with the physical world in different ways; 2. Cooperative driving systems are time-critical; 3. The problem of state explosion when we apply formal verification to systems with more participants. First, we describe the multiple stack architecture. It combines several stacks, each of which addresses a particular way of interaction with the physical world. The layered structure in each stack makes it possible for engineers to implement cooperative driving applications without being bogged down by the details of low-level devices. Having functions arranged in a layered fashion helps us divide the verification of the whole system into smaller subproblems of independent module verification. Secondly, we present a framework for modeling the protocol systems that uses GPS clocks for synchronization. We introduce the timing stack, which separates a process into two parts: the part modeled as an finite-state machine that controls state transitions and messages exchanges, and the part that determines the exact moment that a timed event should occur. The availability of accurate clocks at different locations allows processes to execute actions simultaneously, reducing interleaving that often arises in systems that use multiple timers to control timed events. With accurate clocks, we create a lock protocol that resolves conflicting merge requests for driver-assisted merging. Thirdly, we introduce stratified probabilistic verification that mitigates state explosion. It greatly improves the probability bound obtained in the original probabilistic verification algorithm. Unlike most techniques that aim at reducing state space, it is a directed state traversal, prioritizing the states that are more likely to be encountered during system execution. When state traversal stops upon depleting the memory, the unexplored states are the ones that are less likely to be reached. We construct a linear program whose solution is the upper bound for the probability of reaching those unexplored states. The stratified algorithm is particularly useful when considering a protocol system that depends on several imperfect components that may fail with small but hard-to-quantify probabilities. In that case, we adopt a compositional approach to verify a collection of components, assuming that the components have inexact probability guarantees. Finally, we present our design of driver-assisted merging. Its design is reasonably simplified by using the multiple stack architecture and GPS clocks. We use a stratified algorithm to show that merging system fails less than once every 5 × 10¹³ merge attempts.

Biomimetic flapping-wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this dissertation is described the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process, an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details of the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

Este trabalho insere-se na área de pesquisa de sistemas de transporte inteligente e mobilidade urbana buscando um cenário onde a infraestrutura rodoviária é capaz de monitorar um tráfego exclusivo de veículos inteligentes que não dependem de motoristas para serem guiados. A principal contribuição do trabalho é o desenvolvimento de uma solução matemática para otimizar o gerenciamento microscópico centralizado do tráfego de veículos inteligentes em trechos (segmentos) de rodovia. Para isto é apresentado um modelo de otimização baseado em Programação Linear Inteira Mista (MILP), que determina um plano ótimo de trajetórias individuais dos veículos em uma evolução de tráfego. O objetivo é reduzir o tempo de viagem individualmente e assegurar fluidez do tráfego. O modelo considera componentes essenciais do sistema dinâmico viário como topografia da pista, regras de trânsito e a curva de aceleração máxima de cada veículo. São contempladas várias situações de tráfego, tais como ultrapassagens, inclinação na pista, obstáculos e redutores de velocidade. Os resultados indicaram uma média de 20,5 segundos para o cálculo de um cenário com 6 veículos e 11 intervalos de tempo. Como o modelo MILP não tem solução em tempo computacional aceitável para aplicação real, também é proposto um algoritmo de simulação baseado em heurísticas o qual busca reduzir esse tempo de cálculo em detrimento da otimalidade da solução. O algoritmo reproduz o comportamento de um motorista que tenta manter sempre um valor de velocidade escolhido previamente, e por isso é forçado a ultrapassar outros veículos quando obstruído ao longo do trajeto. O resultado do algoritmo tem importância adicional, pois serve de referência para resolver o problema da prioridade nas ultrapassagens. Também são propostos novos indicadores para a avaliação microscópica de qualidade de tráfego. Finalmente, são apresentados resultados de testes em simulações a fim de avaliar e validar o modelo e o algoritmo. / This work focus on the research area of intelligent transportation systems and urban mobility. It considers a scenario where the roadside infrastructure is capable of monitoring traffic composed by 100% of intelligent vehicles that do not rely on drivers to be guided. The main contribution of this work is the development of a mathematical solution to optimize the centralized management of intelligent microscopic vehicular traffic in parts (segments) of highway. Therefore an optimization model based on Mixed Integer Linear Programming (MILP) is presented. The model determines individual trajectories plans of vehicles in a traffic evolution. The objective is to reduce the travel time individually and ensure traffic flow. The model considers essential components of the dynamic highway system, such as, topography of the lane, traffic rules and acceleration curve for each vehicle. Many traffic situations are considered, such as, overtaking, slopes, obstacles and speed reducers. The results indicated an average of 20.5 seconds to calculate a scenario with 6 vehicles and 11 time intervals. As the MILP model has no solution in acceptable computational time for real application, it is proposed an algorithm based on heuristic simulation which seeks to reduce the computation time at the expense of optimality of the solution. The algorithm reproduces the behavior of a driver who always tries to maintain a preselected velocity value, and is therefore forced to overtake other vehicles when blocked along the path. The result of the algorithm has additional importance because it serves as a reference for solving the problem of priority when overtaking. New indicators for microscopic evaluation of quality traffic are also proposed. Finally, test results are presented on simulations to evaluate and validate the model and algorithm.

Este trabalho insere-se na área de pesquisa de sistemas de transporte inteligente e mobilidade urbana buscando um cenário onde a infraestrutura rodoviária é capaz de monitorar um tráfego exclusivo de veículos inteligentes que não dependem de motoristas para serem guiados. A principal contribuição do trabalho é o desenvolvimento de uma solução matemática para otimizar o gerenciamento microscópico centralizado do tráfego de veículos inteligentes em trechos (segmentos) de rodovia. Para isto é apresentado um modelo de otimização baseado em Programação Linear Inteira Mista (MILP), que determina um plano ótimo de trajetórias individuais dos veículos em uma evolução de tráfego. O objetivo é reduzir o tempo de viagem individualmente e assegurar fluidez do tráfego. O modelo considera componentes essenciais do sistema dinâmico viário como topografia da pista, regras de trânsito e a curva de aceleração máxima de cada veículo. São contempladas várias situações de tráfego, tais como ultrapassagens, inclinação na pista, obstáculos e redutores de velocidade. Os resultados indicaram uma média de 20,5 segundos para o cálculo de um cenário com 6 veículos e 11 intervalos de tempo. Como o modelo MILP não tem solução em tempo computacional aceitável para aplicação real, também é proposto um algoritmo de simulação baseado em heurísticas o qual busca reduzir esse tempo de cálculo em detrimento da otimalidade da solução. O algoritmo reproduz o comportamento de um motorista que tenta manter sempre um valor de velocidade escolhido previamente, e por isso é forçado a ultrapassar outros veículos quando obstruído ao longo do trajeto. O resultado do algoritmo tem importância adicional, pois serve de referência para resolver o problema da prioridade nas ultrapassagens. Também são propostos novos indicadores para a avaliação microscópica de qualidade de tráfego. Finalmente, são apresentados resultados de testes em simulações a fim de avaliar e validar o modelo e o algoritmo. / This work focus on the research area of intelligent transportation systems and urban mobility. It considers a scenario where the roadside infrastructure is capable of monitoring traffic composed by 100% of intelligent vehicles that do not rely on drivers to be guided. The main contribution of this work is the development of a mathematical solution to optimize the centralized management of intelligent microscopic vehicular traffic in parts (segments) of highway. Therefore an optimization model based on Mixed Integer Linear Programming (MILP) is presented. The model determines individual trajectories plans of vehicles in a traffic evolution. The objective is to reduce the travel time individually and ensure traffic flow. The model considers essential components of the dynamic highway system, such as, topography of the lane, traffic rules and acceleration curve for each vehicle. Many traffic situations are considered, such as, overtaking, slopes, obstacles and speed reducers. The results indicated an average of 20.5 seconds to calculate a scenario with 6 vehicles and 11 time intervals. As the MILP model has no solution in acceptable computational time for real application, it is proposed an algorithm based on heuristic simulation which seeks to reduce the computation time at the expense of optimality of the solution. The algorithm reproduces the behavior of a driver who always tries to maintain a preselected velocity value, and is therefore forced to overtake other vehicles when blocked along the path. The result of the algorithm has additional importance because it serves as a reference for solving the problem of priority when overtaking. New indicators for microscopic evaluation of quality traffic are also proposed. Finally, test results are presented on simulations to evaluate and validate the model and algorithm.