Optimisation-based coordination of connected, automated vehicles at intersections

In this paper, we analyse the performance of a model predictive controller for coordination of connected, automated vehicles at intersections. The problem has combinatorial complexity, and we propose to solve it approximately by using a two stage procedure where (1) the vehicle crossing order in which the vehicles cross the intersection is found by solving a mixed integer quadratic program and (2) the control commands are subsequently found by solving a nonlinear program. We show that the controller is persistently safe and compare its performance against traffic lights and two simpler optimisation-based coordination schemes. The results show that our approach outperforms the considered alternatives in terms of both energy consumption and travel-time delay, especially for medium to high traffic loads

An Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections

In this paper, we consider the optimal coordination of automated vehicles at intersections under fixed crossingorders. We state the problem as a Direct Optimal Control problem, and propose a line-search Primal-Dual Interior Point algorithm with which it can be solved. We show that the problem structure is such that most computations required to construct the search- direction and step-size can be performed in parallel on-board the vehicles. This is realized through the Schur-complement of blocks in the KKT-matrix in two steps and a merit-function with separa- ble components. We analyze the communication requirements of the algorithm, and propose a conservative approximation scheme which can reduce the data exchange. We demonstrate that in hard but realistic scenarios, reductions of almost 99% are achieved, at the expense of less than 1% sub-optimality

An MIQP-based heuristic for Optimal Coordination of Vehicles at Intersections

The problem of coordinating automated vehicles at intersections can be formulated as an optimal control prob- lem which is inherently difficult to solve, due to its combinato- rial nature. In this paper, we propose a two-stage approximation algorithm based on a previously presented decomposition. The procedure (a) first solves a Mixed Integer Quadratic Program (MIQP) to compute an approximate solution to the combinatorial part of the problem, i.e. the order in which the vehicles cross the intersection; then (b), solves a Nonlinear Program (NLP) for the optimal state and control trajectories. We demonstrate the performance of the algorithm through extensive simulation, and show that it greatly outperforms the natural First-Come-First-Served heuristic

Multi Vehicle Trajectory Planning On Road Networks

When multiple autonomous vehicles work in a shared space, such as in a surface mine or warehouse, they often travel along specified paths through a static road network. Although these vehicles’ actions and performance are coupled, their motion is often planned myopically or omits cooperation beyond avoiding collisions reactively. More desirable solutions could be achieved by coordinating and planning actions ahead of time. To make multi-vehicle systems more productive and efficient, the thesis introduces planning methods that can optimise for travel time, energy consumption, and trajectory smoothness. Vehicle motion is coordinated by using motion models that combine all trajectories, and avoid collisions. Mathematical programming is then used to find optimised solutions. The proposed methods are shown to significantly reduce solution costs compared to an approach based on common driving practices. As the number of vehicles and interactions between them increases, the number of solutions grows exponentially, making finding a solution computationally challenging. A major aim here was to find high quality solutions within practical computation times. To achieve this, techniques were developed that exploit the structure of the problems. This includes a heuristic algorithm that scales better with problem size, and is combined with the mathematical programming techniques to reduce their complexity. These were found to significantly reduce computation times, trading off marginal solution quality

DESIGN AND VERIFICATION OF AUTONOMOUS SYSTEMS IN THE PRESENCE OF UNCERTAINTIES

Autonomous Systems offer hope towards moving away from mechanized, unsafe, manual, often inefficient practices. The last decade has seen several small, but important, steps towards making this dream into reality. These advancements have helped us to achieve limited autonomy in several places, such as, driving, factory floors, surgeries, wearables, and home assistants, etc. Nevertheless, autonomous systems are required to operate in a wide range of environments with uncertainties (viz., sensor errors, timing errors, dynamic nature of the environment, etc.). Such environmental uncertainties, even when present in small amounts, can have drastic impact on the safety of the system—thus hampering the goal of achieving higher degree of autonomy, especially in safety critical domains. To this end, the dissertation shall discuss formaltechniques that are able to verify and design autonomous systems for safety, even under the presence of such uncertainties, allowing for their trustworthy deployment in the real world. Specifically, the dissertation shall discuss monitoring techniques for autonomous systems from available (noisy) logs, and safety-verification techniques of autonomous system controllers under timing uncertainties. Secondly, using heterogeneous learning-based cloud computing models that can balance uncertainty in output and computation cost, the dissertation will present techniques for designing safe and performance-optimal autonomous systems.Doctor of Philosoph