Department Publications

Predicting the possible future trajectories of the surrounding dynamic agents is an essential requirement in autonomous driving. These trajectories mainly depend on the surrounding static environment, as well as the past movements of those dynamic agents. Furthermore, the multimodal nature of agent intentions makes the trajectory prediction problem more challenging. All of the existing models consider the target agent as well as the surrounding agents similarly, without considering the variation of physical properties. In this paper, we present a novel deep-learning based framework for multimodal trajectory prediction in autonomous driving, which considers the physical properties of the target and surrounding vehicles such as the object class and their physical dimensions through a weighted attention module, that improves the accuracy of the predictions. Our model has achieved the highest results in the nuScenes trajectory prediction benchmark, out of the models which use rasterized maps to input environment information. Furthermore, our model is able to run in real-time, achieving a high inference rate of over 300 FPS.

The Kalman filter is often used with plants without zeros. However, when the plant has zeros, the Kalman filter tends to neglect the effects of zeros, leading to errors in output estimation. Without the effects of zeros, the Kalman filter estimation would lag behind the true output, or it would lead to true output if the zeros are on the RHP. The parallel Kalman filter bank, instead of a single filter is proposed to remedy this problem. Each Kalman filter in the bank operates on a plant submodel that is obtained by decomposing the plant model. The output estimations of all of the Kalman filters in the bank are added together to synthesize the plant output estimation. The method was simulated in a PID-controlled system where better results have been obtained.

The quadrotor is an under-actuated nonlinear system that poses challenges in modeling and controller design. This work uses the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor to build a decoupled control system. A linear quadratic Gaussian (LQG) regulator is used as the controller, and two extended Kalman filters are used for state estimation in the two dynamic models. The entire design process from modeling to flight simulation is presented. Model linearization while preserving controllability, state-space modeling under gravity disturbance, feedforward compensation, LQR gain adjustment, actuator dynamics, sensor system for observability, adaptation of extended Kalman filter, and its limitations are presented.

Oceans cover about 70% of Earth's surface and it is difficult to keep them under continuous surveillance with ever increasing criminal activities. Conventional surveillance methods utilize manned vessels to patrol large sea areas, and due to repetitive-monotonous nature, the personnel involved can often make inevitable human errors due to fatigue. As an alternative, solar-charged battery-powered surface vessel that can autonomously navigate in maritime waters is proposed. Autonomous surveillance vessels are required to navigate in oceans for an extended time duration, requiring them to use much power in propulsion. To properly manage propulsion power, the vessel design must minimize the drag acting upon the vessel. In this work, computational fluid dynamics (CFD) techniques are used to identify the most suitable type of vessel shape that can minimize the drag and maximize the lift and stability. According …