Panagiotis Tsiotras

Adaptive Intelligence for Cyber-Physical Automotive Active Safety - System Design and Evaluation

Current advances in MEMS sensor technology, the emergence of embedded control software, the rapid progress in computer technology, digital image processing, machine learning and control algorithms, along with an ever increasing investment in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies, are about to revolutionize the way we use vehicles and commute in everyday life. Automotive active safety systems, in particular, have been used with enormous success in the past 50 years and have helped keep traffic accidents in check. Still, more than 30,000 deaths and 2,000,000 injuries occur each year in the US alone, and many more worldwide. The impact of traffic accidents on the economy is estimated to be as high as $300B/yr in the US alone. Further improvement in terms of driving safety (and comfort) necessitates that the next generation of active safety systems are more proactive (as opposed to reactive) and can comprehend and interpret driver intent. Future active safety systems will have to account for the diversity of drivers’ skills, the behavior of drivers in traffic, and the overall traffic conditions.

This research aims at improving the current capabilities of automotive active safety control systems (ASCS) by taking into account the interactions between the driver, the vehicle, the ASCS and the environment. Beyond solving a fundamental problem in automotive industry, this research will have ramifications in other cyber-physical domains, where humans manually control vehicles or equipment including: flying, operation of heavy machinery, mining, tele-robotics, and robotic medicine. Making autonomous/automated systems that feel and behave “naturally” to human operators is not always easy. As these systems and machines participate more in everyday interactions with humans, the need to make them operate in a predictable manner is more urgent than ever.

To achieve the goals of the proposed research, we will use the estimation of the driver’s cognitive state to adapt the ASCS accordingly, in order to achieve a seamless operation with the driver. Specifically, we will use new methodologies to infer long-term and short-term behavior of drivers via the use of Bayesian networks and neuromorphic algorithms to estimate the driver’s skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs. We will inject this information into the ASCS operation in order to enhance its performance by taking advantage of recent results from the theory of adaptive and real-time, model-predictive optimal control. The correct level of autonomy and workload distribution between the driver and ASCS will ensure that no conflicts arise between the driver and the control system, and the safety and passenger comfort are not compromised. A comprehensive plan is proposed to test and validate the developed theory by collecting measurements from several human subjects while operating a virtual reality-driving simulator.

The project is in collaboration with Karen Feigh and Laurent Itti (USC). Please visit Prof. Feigh's and Prof. Itti's websites for additional publications related to this project.

Please also visit our VIP project website.

Sponsors

This project is supported by NSF.

Selected Publications

You, C., and Tsiotras, P., "Optimal Two-Point Visual Driver Model and Controller Development for Driver-Assist Systems for Semi-Autonomous Vehicles,'' American Control Conference, Boston, MA, July 6-8, 2016.
You, C., Lu, J., and Tsiotras, P., "Driver Parameter Estimation Using Joint E-/UKF and Dual E-/UKF Under Nonlinear State Inequality Constraints," IEEE Conference on Systems, Man and Cybernetics, Budapest, Hungary, Oct. 9-12, 2016.
Okamoto, K., and Tsiotras, P., "A New Hybrid Sensorimotor Driver Model with Model Predictive Control," IEEE Conference on Systems, Man and Cybernetics, Budapest, Hungary, Oct. 9-12, 2016.
You, C., Lu, J., and Tsiotras, P., "Driver Parameter Estimation Using Joint E-/UKF and Dual E-/UKF Under Nonlinear State Inequality Constraints,'' IEEE Conference on Systems, Man and Cybernetics, Budapest, Hungary, Oct. 9-12, 2016.
Okamoto, K., and Tsiotras, P., "A New Hybrid Sensorimotor Driver Model with Model Predictive Control,''
IEEE Conference on Systems, Man and Cybernetics, Budapest, Hungary, Oct. 9-12, 2016, (best student paper finalist).
You, C., and Tsiotras, P., "Vehicle Modeling and Parameter Estimation Using Adaptive Limited Memory Joint-State UKF,'' American Control Conference, Seattle, WA, May 24-26, 2017, pp. 322-327, doi: 10.23919/ACC.2017.7962973
You, C., and Tsiotras, P., "Real-Time Trail-Braking Maneuver Generation for Off-Road Vehicle Racing," American Control Conference, Milwaukee, WI, June 27--29, 2018, pp. 4751– 4756, doi: 10.23919/ACC.2018.8431620
You, C., and Tsiotras, P., "Off-Road High-Speed Cornering for Autonomous Rally Racing," IEEE Transactions on Control Systems Technology, Vol. 26, No. 2, pp. 485-501, 2021, doi: 10.1109/TCST.2019.2950354
You, C., Lu, J., Filev, D., and Tsiotras, P., "Autonomous Planning and Control for Intelligent Vehicles in Traffic," IEEE Transactions on Intelligent Transportation Systems, Vol. 21, No. 6, pp. 2339-2349, June 2020, doi: 10.1109/TITS.2019.2918071
Okamoto, K., and Tsiotras, P., “Data-Driven Human Driver Lateral Control Models for Developing Haptic-Shared Control Advanced Driver Assist Systems,” Robotics and Autonomous Systems, (accepted January 2019). doi: 10.1016/j.robot.2019.01.020
You, C., Lu, J., Filev, D., and Tsiotras, P., “Advanced Planning for Autonomous Vehicles Using Reinforcement Learning and Deep Inverse Reinforcement Learning,” Robotics and Autonomous Systems, Vol. 114, pp. 1-18, April~2019, doi:10.1016/j.robot.2019.01.003 .