Computationally efficient exact remodeling of optimization programs with applications to autonomous driving

The future of autonomous vehicles is rapidly approaching and the published and available research, both from vehicle manufacturers and universities, is abundant. This new technology promises less pollution, lower accident rates, decreased congestion and the possibility to relax or work while a vehicle takes you where you need to go.In this thesis we use nonlinear model predictive control to control autonomous vehicles overtaking on highways and driving through intersections. One of the main disadvantages of model predictive control is that the optimal control problems can be computationally expensive to solve. This could certainly be the case for the exact temporal formulation of the intersection and highway problem since the modeling of for both applications include binary decisions; and thus, have mixed integer optimization programs as their optimal control problems. To decrease the computational complexity of these optimal control problems this thesis introduces a novel reformulation technique for optimal control problems which removes the integer decisions present due to the collision constraints; which results in a continuous, nonlinear control problem for both applications. The remodeling technique involves changing the independent variable from travel time to traveled distance, introducing travel time and inverse velocity as states and lastly by introducing new input signals. After the remodeling, the continuous, nonlinear optimal control problems are solved using sequential quadratic programming. Further, it is shown that the introduced remodeling technique guarantees that the subproblems of the sequential quadratic programming scheme provides feasible solutions to the original nonlinear program being solved; for both the intersection and overtaking problem. This makes it possible to stop the sequential quadratic programming scheme prematurely and still have access to a solution that is feasible in the nonlinear program; provided, of course, that the subproblems themselves are feasible

Predictive cruise control with autonomous overtaking

This paper studies the problem of optimally controlling an autonomous vehicle, to safely overtake a slow-moving leading vehicle. The problem is formulated to minimize deviation from a reference velocity and position trajectory, while keeping the vehicle on the road and avoiding collision with surrounding vehicles. We show that the optimization problem can be formulated as a convex program, by providing convex modeling steps that include change of reference frame, change of variables, sampling in relative longitudinal distance, convex relaxation and linearization. A case study is provided showing overtaking scenarios in proximity of an oncoming vehicle, and a vehicle driving on an adjacent lane and in the same direction as the leading vehicle

Traffic Management System for the combined optimal routing, scheduling and motion planning of self-driving vehicles inside reserved smart road networks

The topic discussed in this thesis belongs to the field of automation of transport systems, which has grown in importance in the last decade, both in the innovation field (where different automation technologies have been gradually introduced in different sectors of road transport, in the promising view of making it more efficient, safer, and greener) and in the research field (where different research activities and publications have addressed the problem under different points of view). More in detail, this work addresses the problem of autonomous vehicles coordina tion inside reserved road networks by proposing a novel Traffic Management System (TMS) for the combined routing, scheduling and motion planning of the vehicles. To this aim, the network is assumed to have a modular structure, which results from a certain number of roads and intersections assembled together. The way in which roads and intersections are put together defines the network layout. Within such a system architecture, the main tasks addressed by the TMS are: (1) at the higher level, the optimal routing of the vehicles in the network, exploiting the available information coming from the vehicles and the various elements of the network; (2) at a lower level, the modeling and optimization of the vehicle trajectories and speeds for each road and for each intersection in the network; (3) the coordination between the vehicles and the elements of the network, to ensure a combined approach that considers, in a recursive way, the scheduling and motion planning of the vehicles in the various elements when solving the routing problem. In particular, the routing and the scheduling and motion planning problems are formulated as MILP optimization problems, aiming to maximize the performance of the entire network (routing model) and the performance of its single elements - roads and intersections (scheduling and motion planning model) while guaranteeing the requested level of safety and comfort for the passengers. Besides, one of the main features of the proposed approach consists of the integration of the scheduling decisions and the motion planning computation by means of constraints regarding the speed limit, the acceleration, and the so-called safety dynamic constraints on incompatible positions of conflicting vehicles. In particular, thanks to these last constraints, it is possible to consider the real space occupancy of the vehicles avoiding collisions. After the theoretical discussion of the proposed TMS and of its components and models, the thesis presents and discusses the results of different numerical experiments, aimed at testing the TMS in some specific scenarios. In particular, the routing model and the scheduling and motion planning model are tested on different scenarios, which demonstrate the effectiveness and the validity of such approach in performing the addressed tasks, also compared with more traditional methods. Finally, the computational effort needed for the problem solution, which is a key element to take into account, is discussed both for the road element and the intersection element

Trajectory generation for autonomous highway driving using model predictive control

Model Predictive Control (MPC) has had an increasing role in autonomous driving applications over the last decade, enabled by the continuous rising of the computational power in microcontrollers. In this thesis a collision avoidance trajectory generation algorithm based in MPC formulation is developed. The operating environment consists in a one-way highway with two lanes. The overall system is equipped with a low-level controller capable of tracking the trajectory generated by the MPC planner. In the path towards this goal, a MPC based lane changing application in an obstacle-free highway environment has been developed. A point-mass kinematic vehicle model is used as the MPC plant model for its simplicity and enabled by the usage of a low-level controller. This thesis studies several obstacle representation approaches and then, explains in detail the development process of the collision avoidance trajectory generation application, defining and discussing simulation results for each intermediate approach obtained. Both applications have been implemented in a BeagleBone Black online board situated in small-scale trucks (1:12) for testing purpose. The experimental results have been studied and discussed to prove the algorithms functionalities, as well as to check the board capabilities to run online MPC applications in comparison with polynomials based approaches

Qualitative Process Analysis : Theoretical Requirements and Practical Implementation in Naval Domain

Understanding complex behaviours is an essential component of everyday life, integrated into daily routines as well as specialised research. To handle the increasing amount of data available from (logistic) dynamic scenarios, analysis of the behaviour of agents in a given environment is becoming more automated and thus requires reliable new analytical methods. This thesis seeks to improve analysis of observed data in dynamic scenarios by developing a new model for transforming sparse behavioural observations into realistic explanations of agent behaviours, with the goal of testing that model in a real-world maritime navigation scenario

Cognitive Vehicle Platooning in the Era of Automated Electric Transportation

Vehicle platooning is an important innovation in the automotive industry that aims at improving safety, mileage, efficiency, and the time needed to travel. This research focuses on the various aspects of vehicle platooning, one of the important aspects being analysis of different control strategies that lead to a stable and robust platoon. Safety of passengers being a very important consideration, the control design should be such that the controller remains robust under uncertain environments. As a part of the Department of Energy (DOE) project, this research also tries to show a demonstration of vehicle platooning using robots. In an automated highway scenario, a vehicle platoon can be thought of as a string of vehicles, following one another as a platoon. Being equipped by wireless communication capabilities, these vehicles communicate with one another to maintain their formation as a platoon, hence are cognitive. Autonomous capable vehicles in tightly spaced, computer-controlled platoons will lead to savings in energy due to reduced aerodynamic forces, as well as increased passenger comfort since there will be no sudden accelerations or decelerations. Impacts in the occurrence of collisions, if any, will be very low. The greatest benefit obtained is, however, an increase in highway capacity, along with reduction in traffic congestion, pollution, and energy consumption. Another aspect of this project is the automated electric transportation (AET). This aims at providing energy directly to vehicles from electric highways, thus reducing their energy consumption and CO2 emission. By eliminating the use of overhead wires, infrastructure can be upgraded by electrifying highways and providing energy on demand and in real time to moving vehicles via a wireless energy transfer phenomenon known as wireless inductive coupling. The work done in this research will help to gain an insight into vehicle platooning and the control system related to maintaining the vehicles in this formation