Advances in applications of sensor technologies and cooperative estimation in intelligent transportation systems facilitate reliable and robust estimation of vehicle states and road conditions, which are required for path planning and lateral/longitudinal stabilization of an Automated Driving System (ADS). One of the main challenges of the current state estimators in ADS is low reliability in high-slip maneuvers due to complex and nonlinear behavior of tire forces. An opinion dynamics approach in intra-vehicular networks will be presented in the first part of presentation to address this issue. The main objective is to enhance vehicle safety by developing a tire-level estimation method for estimating individual forces and autonomous vehicles' longitudinal/lateral slips, regardless of drive type or powertrain configurations. Maintaining autonomous vehicles lateral stability during path tracking is challenging due to potentially conflicting control inputs for vehicle body controls and wheel dynamic stabilization programs. An integrated control framework for the stability, traction, and path following controls, which takes the combined-slip friction effect into account, will be discussed in the second part of the presentation. Road experiments confirmed the validity and robustness of the new approach, in different driving scenarios, especially for combined-slip and low-excitation maneuvers, which are demanding for existing control systems in the ADS and advanced driver-assistance systems.
Bio: Ehsan Hashemi received his PhD in Mechanical and Mechatronics Engineering in 2017 from University of Waterloo, ON, Canada, and is currently a Research Assistant Professor at the University of Waterloo. Previously he was a visiting professor at KTH Royal Institute of Technology (Jan.-June 2019) and a postdoctoral fellow at the University of Waterloo (2017-2018). His research is focused on distributed and fault-tolerant estimation, cooperative intelligent transport systems, and automated driving systems.
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