MPC-BASED AUTONOMOUS DRIVING CONTROL WITH LOCALIZED PATH PLANNING FOR OBSTACLE AVOIDANCE AND NAVIGATING SIGNALIZED INTERSECTIONS

Connected and autonomous vehicles are becoming the major focus of research for the industry and academia in the automotive field. Many companies and research groups have demonstrated the advantages and the requirement of such technology to improve the energy efficiency of vehicles, decrease the number of crash and road accidents, and control emissions. This research delves into improving the autonomy of self-driving vehicles by implementing localized path planning algorithms to introduce motion control for obstacle avoidance during uncertainties. Lateral path planning is implemented using the A* algorithm combined with piecewise Bezier curve generation which provides an optimum trajectory reference to avoid a collision. Model Predictive Control (MPC) is used to implement longitudinal and lateral control of the vehicle. The data from vehicle-to-everything (V2X) communication infrastructure is used to navigate through multiple signalized intersections. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller. The results of the proposed controller and the scope of the future work conclude the research

Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles

There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3D marine habitat mapping, require that a number of heterogeneous vehicles operate simultaneously in small areas, often in close proximity of each other. In these circumstances safety, reliability, and efficient multiple vehicle operation are key ingredients for mission success. Additionally, the deployment and operation of multiple AMVs at sea are extremely costly in terms of the logistics and human resources required for mission supervision, often during extended periods of time. These costs can be greatly minimized by automating the deployment and initial steering of a vehicle fleet to a predetermined configuration, in preparation for the ensuing mission, taking into account operational constraints. This is one of the core issues addressed in the scope of the Widely Scalable Mobile Underwater Sonar Technology project (WiMUST), an EU Horizon 2020 initiative for underwater robotics research. WiMUST uses a team of cooperative autonomous ma- rine robots, some of which towing streamers equipped with hydrophones, acting as intelligent sensing and communicat- ing nodes of a reconfigurable moving acoustic network. In WiMUST, the AMVs maintain a fixed geometric formation through cooperative navigation and motion control. Formation initialization requires that all the AMVs start from scattered positions in the water and maneuver so as to arrive at required target configuration points at the same time in a completely au- tomatic manner. This paper describes the decoupled prioritized vehicle motion planner developed in the scope of WiMUST that, together with an existing system for trajectory tracking, affords a fleet of vehicles the above capabilities, while ensuring inter- vehicle collision and streamer entanglement avoidance. Tests with a fleet of seven marine vehicles show the efficacy of the system planner developed.Peer reviewe

Priority-based intersection management with kinodynamic constraints

We consider the problem of coordinating a collection of robots at an intersection area taking into account dynamical constraints due to actuator limitations. We adopt the coordination space approach, which is standard in multiple robot motion planning. Assuming the priorities between robots are assigned in advance and the existence of a collision-free trajectory respecting those priorities, we propose a provably safe trajectory planner satisfying kinodynamic constraints. The algorithm is shown to run in real time and to return safe (collision-free) trajectories. Simulation results on synthetic data illustrate the benefits of the approach.Comment: to be presented at ECC2014; 6 page

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page