Analysis of Model Predictive Intersection Control for Autonomous Vehicles

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios

Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections

Unsignalized intersections are typically considered as one of the most representative and challenging scenarios for self-driving vehicles. To tackle autonomous driving problems in such scenarios, this paper proposes a curriculum proximal policy optimization (CPPO) framework with stage-decaying clipping. By adjusting the clipping parameter during different stages of training through proximal policy optimization (PPO), the vehicle can first rapidly search for an approximate optimal policy or its neighborhood with a large parameter, and then converges to the optimal policy with a small one. Particularly, the stage-based curriculum learning technology is incorporated into the proposed framework to improve the generalization performance and further accelerate the training process. Moreover, the reward function is specially designed in view of different curriculum settings. A series of comparative experiments are conducted in intersection-crossing scenarios with bi-lane carriageways to verify the effectiveness of the proposed CPPO method. The results show that the proposed approach demonstrates better adaptiveness to different dynamic and complex environments, as well as faster training speed over baseline methods.Comment: 7 pages, 4 figure

Distributed MPC for autonomous ships on inland waterways with collaborative collision avoidance

This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, and is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers is used as a scheme for exchanging and negotiating intentions among ships. Simulation results show that the proposed algorithm can comply with traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase efficiency in complex scenarios

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

Design, testing and validation of model predictive control for an unmanned ground vehicle

The rapid increase in designing, manufacturing, and using autonomous robots has attracted numerous researchers and industries in recent decades. The logical motivation behind this interest is the wide range of applications. For instance, perimeter surveillance, search and rescue missions, agriculture, and construction. In this thesis, motion planning and control based on model predictive control (MPC) for unmanned ground vehicles (UGVs) is tackled. In addition, different variants of MPC are designed, analysed, and implemented for such non-holonomic systems. It is imperative to focus on the ability of MPC to handle constraints as one of the motivations. Furthermore, the proliferation of computer processing enables these systems to work in a real-time scenario. The controller's responsibility is to guarantee an accurate trajectory tracking process to deal with other specifications usually not considered or solved by the planner. However, the separation between planner and controller is not necessarily defined uniquely, even though it can be a hybrid process, as seen in part of this thesis. Firstly, a robust MPC is designed and implemented for a small-scale autonomous bulldozer in the presence of uncertainties, which uses an optimal control action and a feed-forward controller to suppress these uncertainties. More precisely, a linearised variant of MPC is deployed to solve the trajectory tracking problem of the vehicle. Afterwards, a nonlinear MPC is designed and implemented to solve the path-following problem of the UGV for masonry in a construction context, where longitudinal velocity and yaw rate are employed as control inputs to the platform. For both the control techniques, several experiments are performed to validate the robustness and accuracy of the proposed scheme. Those experiments are performed under realistic localisation accuracy, provided by a typical localiser. Most conspicuously, a novel proximal planning and control strategy is implemented in the presence of skid-slip and dynamic and static collision avoidance for the posture control and tracking control problems. The ability to operate in moving objects is critical for UGVs to function well. The approach offers specific planning capabilities, able to deal at high frequency with context characteristics, which the higher-level planner may not well solve. Those context characteristics are related to dynamic objects and other terrain details detected by the platform's onboard perception capabilities. In the control context, proximal and interior-point optimisation methods are used for MPC. Relevant attention is given to the processing time required by the MPC process to obtain the control actions at each actual control time. This concern is due to the need to optimise each control action, which must be calculated and applied in real-time. Because the length of a prediction horizon is critical in practical applications, it is worth looking into in further detail. In another study, the accuracies of robust and nonlinear model predictive controllers are compared. Finally, a hybrid controller is proposed and implemented. This approach exploits the availability of a simplified cost-to-go function (which is provided by a higher-level planner); thus, the hybrid approach fuses, in real-time, the nominal CTG function (nominal terrain map) with the rest of the critical constraints, which the planner usually ignores. The conducted research fills necessary gaps in the application areas of MPC and UGVs. Both theoretical and practical contributions have been made in this thesis. Moreover, extensive simulations and experiments are performed to test and verify the working of MPC with a reasonable processing capability of the onboard process