Ankita Pawar, Shikha Tiwari, Christian Gajek

MAN Trucks, University of Freiburg

Monday, November 04, 2024, 9:00 - 11:59

Room 02-012, Georges-Köhler Allee 102, Freiburg 79110, Germany

This event is devoted to the Master defenses of Ankita Pawar and Shikha Tiwari, who both did their theses at MAN Trucks, Munich. Before the defenses, their supervisor, Christian Gajek, will give a presentation about research on autonomous truck driving at MAN.

Master defense committee:

• First examiner: Prof. Dr. Moritz Diehl
• Second examiner: Prof. Dr. Stefan J. Rupitsch

09:30 to 10:00 am: Talk by Christian Gajek (Ph.D. student at MAN) 

Robust Trajectory Tracking Control for Articulated Vehicles

In the ATLAS-L4 project, we aim to modernize highway freight transportation by developing a Level 4 autonomous driving solution for heavy-duty trucks. A key challenge is to design a controller capable of accurately following planned trajectories within a specified tolerance while maintaining robustness against uncertainties arising from vehicle dynamics, variable loading conditions, and other environmental influences. This talk will present a preliminary concept using Stochastic MPC for lateral control.

10:00 to 11:00 am: Master defense of Ankita Pawar

MPC-based Lateral Control for Autonomous Freight Trucks

This thesis presents a Linear Time-Varying Model Predictive Control (LTV-MPC) approach to enhance lateral trajectory tracking for autonomous heavy-duty vehicles, addressing challenges posed by their complex dynamics at high speeds. The framework integrates a dynamic vehicle model that accounts for steering delays and safety constraints, and is implemented in MATLAB/Simulink. The LTV-MPC controller is rigorously tested in various simulation scenarios, demonstrating its robust performance. Real-time validation on a MAN prototype truck further illustrates the approach’s effectiveness, highlighting its potential to improve safety and operational efficiency in autonomous highway logistics.
 

11:00 to 12:00 am: Master defense of Shikha Tiwari

Ego-motion Estimation of the Truck’s Cabin

The thesis explores state estimation algorithms to address the unique challenges of cabin motion in heavy-duty autonomous vehicles. Focusing on pitch and roll dynamics, it utilizes low-cost inertial navigation sensors to estimate the cabin's motion relative to the chassis. By employing system modeling and filtering techniques for state estimation in addition to optimization-based tuning of system parameters, the research compensates for cabin motion effects on perception systems. This real-time compensation enhances the accuracy of localization and control tasks, ultimately improving the safety and efficiency of autonomous vehicles in long-haul logistics.

Noon: Joint lunch