Description:
In the first part of the tutorial a state estimation approach, which estimates slip and longitudinal speed at each wheel and can be integrated with the existing visual inertial navigation systems, will be introduced.
The novel wheel-level observer, which uses proprioceptive sensor data, fuses vehicle kinematic states, tire internal states, and the wheel dynamics to estimate the speed at each tire, without any information of the road surface friction or global navigation satellite systems.
A wheel-vehicle dynamical model, which augments estimates at each tire with the vehicle dynamics, will be presented to design an integrated slip-aware framework for state estimation.
Fig. The test vehicle in harsh launch scenario & Input-Output layout and the data acquisition system for the experimental setup
In the second part of the session, a novel slip-aware networked vehicle model, will be given to design a controller for cooperative adaptive cruise control and safety of the intended functionality (SOTIF) in connected automated driving systems.
By sharing the magnitude of tire-level relative longitudinal speed (i.e., longitudinal slip ratio) in addition to the vehicle kinematic states, the novel slip-aware model which utilizes an augmented state to enhance the safety of the vehicular network will be introduced.
The proposed model is compelling in the sense that it requires minimal information sharing, while facilitating the formation of platoons which comprise vehicles that might operate at the capacity limit.
For controlling the overall system of networked vehicles, a time-delayed control based strategy will be presented along with the stability of the model.
Recent Publications (Related to the Tutorial Topic from the Speakers in the Past 3 Year):
1. X. He, E. Hashemi, and K. H. Johansson, “Distributed Control Under Compromised Measurements: Resilient Estimation, Attack detection, and Vehicle Platooning,” Automatica, 134, 2022.
2. E. Hashemi and A. Banerjee, “Proprioceptive Observer Design for Speed Estimation in Automated Driving Systems,” IEEE Intelligent Vehicles Symposium (IV), pp. 224-229, 2022.
3. A. Banerjee and E. Hashemi, “Slip-Aware Networked Vehicular Model and Control for Connected Automated Driving,” IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 2818-282, 2022.
4. Y. Zhang, Y. Qin, E. Hashemi, “MILE: Multi-objective Integrated Model Predictive Adaptive Cruise Control for Intelligent Vehicle,” IEEE Trans. on Industrial Informatics, 2022.
5. N. P. Bhatt, A. Khajepour, and E. Hashemi “MPC-PF: Social Interaction Aware Trajectory Prediction of Dynamic Objects for Autonomous Driving Using Potential Fields,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2022.
6. E. Hashemi, X. He, and K. H. Johansson, “Robust Slip-Aware Fusion for Mobile Robots State Estimation,” IEEE Robotics and Automation Letters, 7(3), pp. 7896-7903, 2022.
7. E. Hashemi, A. Khajepour, N. Moshchuk, and S.K. Chen, “Real-Time Road Bank Estimation with Disturbance Observers for Vehicle Control Systems,” IEEE Trans. on Control Systems Technology, 2021.
8. E. Hashemi, X. He, and K. H. Johansson, “A Dynamical Game Approach for Integrated Stabilization and Path Tracking for Autonomous Vehicles,” American Control Conference, 2020.
9. M. H. Mamduhi, E. Hashemi, J. S. Baras, and K. H. Johansson, “Event-triggered Add-on Safety for Connected and Automated Vehicles Using Road-side Network Infrastructure,” IFAC World Cong., 2020.
10. X. He, E. Hashemi, and K. H. Johansson, “A Hybrid Framework of Centralized Control and Distributed Estimation,” IFAC World Cong., 2020.
11. P. E. Pare, E. Hashemi, R. Stern, H. Sandberg, and K. H. Johansson, “Networked Model for Cooperative Adaptive Cruise Control,” IFAC Workshop on Distributed Estimation & Cont. in Networked Systems, 2019.