State Estimation for Connected and Automated Driving Systems

Ehsan Hashemi

Department of Mechanical and Mechatronics Engineering, University of Waterloo

Tuesday, September 22, 2020, 10:45 - 12:00

Room 01-012, Georges-Köhler-Allee 102, Freiburg 79110, Germany

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 and connected vehicles 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. An integrated stabilization and traction (while tracking) ADS control framework, which considers the combined-slip friction effect and uses the estimated states at each vehicle corner, will also 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 ADS and advanced driver-assistance systems.

Remote participation is possible via zoom:

meeting-ID: 950 1374 1122

password: 7UNZMu.xZ